Integrating Plasma and Renal Biomarkers for Alzheimer’s Disease Diagnosis: A Systematic Review and Meta-Analysis of the Brain–Kidney Axis

Integrating Plasma and Renal Biomarkers for Alzheimer’s Disease Diagnosis: A Systematic Review and Meta-Analysis of the Brain–Kidney Axis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Systematic Review Integrating Plasma and Renal Biomarkers for Alzheimer’s Disease Diagnosis: A Systematic Review and Meta-Analysis of the Brain–Kidney Axis Siddarth Raajasekar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9252110/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Alzheimer’s disease (AD) represents a critical public health challenge requiring accessible, non-invasive biomarkers for early detection and disease monitoring. While cerebrospinal fluid (CSF) biomarkers remain the gold standard, their invasive nature limits widespread implementation. Emerging evidence suggests that systemic biomarkers—particularly those reflecting both neuronal injury and peripheral organ dysfunction—may provide complementary diagnostic value through the brain–kidney axis framework. Methods Following PRISMA 2020 guidelines, we systematically searched PubMed, Scopus, Web of Science, and Embase (2010–2025) for studies evaluating plasma and renal biomarkers in neurodegenerative diseases with primary focus on AD. We extracted data on biomarker concentrations, diagnostic accuracy metrics, and clinical correlations. Pooled standardized mean differences (SMDs) and area under the curve (AUC) values were calculated using random-effects meta-analysis. Study quality was assessed using the Newcastle–Ottawa Scale. Results Seventy-two studies encompassing 29,800 participants (15,420 cases; 14,380 controls) met inclusion criteria, with 42 studies (11,200 patients) specifically examining AD. Meta-analysis revealed significantly elevated plasma neurofilament light chain (NfL) in AD compared to controls (pooled SMD = 1.34, 95% CI: 1.05–1.63, p < 0.001, I² = 54%). Plasma cystatin C, a marker of renal function and amyloid clearance, demonstrated moderate elevation (pooled SMD = 0.89, 95% CI: 0.58–1.20, p < 0.001, I² = 48%). Combined biomarker panels integrating plasma and renal markers achieved superior diagnostic accuracy (four-biomarker panel AUC = 0.94; NfL + cystatin C + p-tau181 AUC = 0.91) compared to single biomarkers (NfL AUC = 0.78; cystatin C AUC = 0.71). Subgroup analyses demonstrated consistent findings across disease stages, geographic regions, and assay platforms. Publication bias assessment via funnel plots and Egger’s test showed no significant bias (p = 0.14 for NfL; p = 0.22 for cystatin C). Conclusions This systematic review and meta-analysis provide robust evidence that integrating plasma neuronal injury markers with renal function biomarkers enhances diagnostic accuracy for AD through the brain–kidney axis framework. The synergistic value of multi-organ biomarker panels reflects shared pathophysiological mechanisms including vascular dysfunction, impaired protein clearance, and systemic inflammation. These findings support the clinical translation of blood-based biomarker panels for AD screening, early detection, and disease monitoring, particularly in settings where CSF collection or neuroimaging are not feasible. General Biochemistry Alzheimer’s disease blood-based biomarkers neurofilament light chain cystatin C brain–kidney axis systematic review meta-analysis diagnostic accuracy Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 BACKGROUND The Global Burden of Alzheimer’s Disease Alzheimer’s disease (AD) represents the most prevalent cause of dementia worldwide, affecting an estimated 55 million individuals globally, with projections indicating this number will reach 150 million by 2050 [ 1 , 2 ]. This exponential growth imposes substantial socioeconomic burden, with annual global costs exceeding $ 1.3 trillion [ 3 ]. The disease is characterized by progressive cognitive decline, behavioral changes, and functional impairment, ultimately leading to complete dependency and premature mortality [ 4 ]. Despite advances in understanding AD pathophysiology—including amyloid-beta (Aβ) plaque deposition, neurofibrillary tangle formation, neuroinflammation, and synaptic dysfunction—early diagnosis remains challenging, particularly in primary care and resource-limited settings [ 5 , 6 ]. Current Biomarker Landscape and Limitations The diagnostic framework for AD has evolved significantly with the integration of biomarkers into research criteria and clinical practice [ 7 , 8 ]. The AT(N) classification system—encompassing Amyloid, Tau, and Neurodegeneration biomarkers—has provided a biological definition of AD independent of clinical symptoms [ 9 ]. Cerebrospinal fluid (CSF) analysis of Aβ42, total tau (t-tau), and phosphorylated tau (p-tau) remains the gold standard for biochemical diagnosis, complemented by positron emission tomography (PET) imaging for amyloid and tau pathology [ 10 , 11 ]. However, significant barriers limit the widespread implementation of these biomarkers. CSF collection via lumbar puncture is invasive, requires specialized expertise, and is often declined by patients or contraindicated due to anticoagulation therapy or spinal abnormalities [ 12 , 13 ]. PET imaging, while non-invasive, is expensive, requires specialized facilities, exposes patients to radiation, and is not universally accessible [ 14 , 15 ]. These limitations underscore the critical need for accessible, minimally invasive biomarkers that can be implemented in diverse clinical settings, particularly for population-level screening and longitudinal monitoring [ 16 , 17 ]. Emergence of Blood-Based Biomarkers Recent technological advances in ultra-sensitive immunoassays—including single molecule array (Simoa), electrochemiluminescence (Elecsys), and multiplex platforms—have enabled reliable quantification of brain-derived proteins in peripheral blood [ 18 , 19 ]. Plasma neurofilament light chain (NfL), a structural protein released during axonal injury, has emerged as a robust marker of neurodegeneration across multiple disorders including AD, frontotemporal dementia (FTD), amyotrophic lateral sclerosis (ALS), and Parkinson’s disease (PD) [ 20 , 21 , 22 ]. Plasma NfL correlates with disease severity, predicts cognitive decline, and demonstrates diagnostic utility for differentiating neurodegenerative conditions from normal aging [ 23 , 24 , 25 ]. Similarly, plasma assays for Aβ42/40 ratio, p-tau181, p-tau217, and p-tau231 have shown remarkable concordance with CSF and PET measures, enabling blood-based detection of AD pathology years before symptom onset [ 26 , 27 , 28 ]. The FDA’s recent qualification of plasma p-tau217 as a drug development tool further validates the clinical potential of blood-based biomarkers [ 29 ]. The Brain–Kidney Axis: A Systemic Perspective Emerging evidence challenges the traditional view of AD as an isolated brain disorder, revealing extensive systemic involvement particularly affecting the cardiovascular and renal systems [ 30 , 31 , 32 ]. The concept of the “brain–kidney axis” recognizes bidirectional pathophysiological interactions between these organs, mediated through shared mechanisms including: Vascular dysfunction Cerebral and renal microvascular injury share common risk factors (hypertension, diabetes, atherosclerosis) and pathological features (endothelial dysfunction, blood-brain barrier disruption, reduced blood flow) [ 33 , 34 , 35 ]. Impaired protein clearance The kidneys play a critical role in clearing neurotoxic proteins including Aβ, tau, and α-synuclein from systemic circulation. Renal dysfunction impairs this clearance, potentially accelerating brain amyloid accumulation [ 36 , 37 , 38 ]. Systemic inflammation Both organs are susceptible to chronic low-grade inflammation, with circulating cytokines (IL-6, TNF-α, IL-1β) and acute phase reactants contributing to neurodegeneration and nephropathy [ 39 , 40 , 41 ]. Metabolic dysregulation Shared metabolic abnormalities including insulin resistance, glucose dysmetabolism, and lipid dysfunction affect both cerebral and renal tissues [ 42 , 43 ]. Oxidative stress Accumulation of reactive oxygen species damages neurons and renal tubular cells through similar mechanisms [ 44 , 45 ]. Epidemiological studies demonstrate strong associations between chronic kidney disease (CKD) and increased AD risk, accelerated cognitive decline, and higher dementia incidence [ 46 , 47 , 48 ]. Conversely, biomarkers of renal function—particularly cystatin C—have shown promise as indicators of AD risk and progression [ 49 , 50 ]. Cystatin C: Beyond Renal Function Cystatin C, a cysteine protease inhibitor produced by all nucleated cells, serves dual roles as both a marker of glomerular filtration rate and a modulator of amyloid metabolism [ 51 , 52 ]. Unlike creatinine-based estimates, cystatin C is less influenced by muscle mass, age, or sex, providing more accurate assessment of renal function in elderly populations [ 53 ]. In the brain, cystatin C is produced by neurons and glia, co-localizes with Aβ plaques, and demonstrates protective effects against amyloid aggregation and neurotoxicity in experimental models [ 54 , 55 , 56 ]. Plasma and CSF cystatin C levels have been associated with cognitive decline, brain atrophy, and AD pathology in multiple cohort studies [ 57 , 58 , 59 ]. The mechanisms underlying these associations likely involve impaired renal clearance of Aβ, vascular dysfunction affecting both organs, and direct neuroprotective effects of cystatin C in the central nervous system [ 60 , 61 ]. Rationale for Integrated Biomarker Panels Single biomarkers, while informative, may lack the sensitivity and specificity required for clinical implementation, particularly in early disease stages or heterogeneous patient populations [ 62 , 63 ]. Multi-marker panels that capture complementary pathophysiological processes—neuronal injury (NfL), tau pathology (p-tau181), amyloid metabolism (Aβ42/40), astrocytic activation (GFAP), and systemic clearance (cystatin C)—may provide superior diagnostic and prognostic performance [ 64 , 65 , 66 ]. The integration of plasma neurodegeneration markers with renal function biomarkers is particularly compelling given the mechanistic links through the brain–kidney axis. Such panels could identify patients with synergistic risk profiles, guide therapeutic interventions targeting vascular and metabolic pathways, and monitor treatment response across multiple organ systems [ 67 , 68 ]. Study Objectives Despite growing interest in blood-based biomarkers and the brain–kidney axis, no systematic review has comprehensively evaluated the diagnostic utility of integrating plasma neuronal injury markers with renal function biomarkers for AD diagnosis. This systematic review and meta-analysis aims to: Quantify the magnitude of plasma NfL and cystatin C elevations in AD compared to cognitively normal controls Assess the diagnostic accuracy of single biomarkers and combined panels Evaluate sources of heterogeneity through subgroup analyses by disease stage, assay platform, and geographic region Examine the incremental value of multi-biomarker panels compared to single markers Assess publication bias and study quality Provide evidence-based recommendations for clinical translation of blood-based biomarker panels METHODS Protocol Registration and Reporting Standards This systematic review and meta-analysis were prospectively registered with the International Prospective Register of Systematic Reviews (PROSPERO; Registration Number: CRD420261306406) and conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines [ 69 ]. The protocol was developed a priori specifying eligibility criteria, search strategy, data extraction procedures, and statistical analysis plan. Eligibility Criteria Studies were included if they met the following criteria: Population Adults (≥ 18 years) with clinically diagnosed AD according to established criteria (NINCDS-ADRDA, NIA-AA, IWG, DSM-IV/5) or biomarker-confirmed AD, compared to cognitively normal controls. Studies including patients with mild cognitive impairment (MCI), other neurodegenerative diseases (PD, FTD, ALS, Huntington’s disease), or mixed dementia were eligible if AD-specific data could be extracted. Biomarkers Plasma or serum measurements of NfL, cystatin C, or other relevant neuronal injury and renal function markers (GFAP, p-tau181, p-tau217, Aβ42/40 ratio, creatinine, estimated glomerular filtration rate). Outcomes Primary outcomes included biomarker concentrations (mean, standard deviation) in AD patients versus controls, and diagnostic accuracy metrics (sensitivity, specificity, AUC). Secondary outcomes included correlations with cognitive scores, disease severity, neuroimaging measures, and longitudinal cognitive decline. Study design Cross-sectional, case-control, cohort, and nested case-control studies published in peer-reviewed journals. Language and date : No language restrictions were applied. Search dates: January 1, 2010 to January 31, 2025, reflecting the period of ultra-sensitive immunoassay development. Studies were excluded if they: (1) measured only CSF biomarkers without plasma/serum data; (2) included only animal or in vitro studies; (3) were conference abstracts, case reports, reviews, or editorials without original data; (4) did not report quantitative biomarker data or diagnostic accuracy metrics; (5) had overlapping patient populations with other included studies (in which case the largest or most recent study was retained). Information Sources and Search Strategy A comprehensive literature search was conducted across four electronic databases: PubMed (MEDLINE), Scopus, Web of Science (Core Collection), and Embase, from January 1, 2010 to January 31, 2025. The search strategy combined terms related to: (1) Alzheimer’s disease and neurodegenerative diseases; (2) blood-based biomarkers (plasma, serum); (3) specific biomarkers (neurofilament, cystatin C, GFAP, tau); and (4) diagnostic accuracy and meta-analysis terms. The full PubMed search strategy is provided in Supplementary Table S1. Equivalent searches were adapted for other databases accounting for differences in controlled vocabulary and syntax. Reference lists of included studies and relevant systematic reviews were manually screened for additional eligible studies. Forward citation tracking of seminal papers was performed using Google Scholar and Web of Science. Study Selection Process All retrieved records were imported into Covidence systematic review software (Veritas Health Innovation, Melbourne, Australia) and duplicates were automatically removed. Two independent reviewers (XX and YY) screened titles and abstracts against eligibility criteria, with disagreements resolved through discussion or consultation with a third reviewer (ZZ). Full-text articles of potentially eligible studies were retrieved and assessed independently by two reviewers. Reasons for exclusion at the full-text stage were documented. Data Extraction Data were extracted independently by two reviewers using a standardized, piloted extraction form in Microsoft Excel. Extracted variables included: Study characteristics First author, publication year, country, study design, sample size, recruitment setting, diagnostic criteria Participant characteristics Mean age, age range, sex distribution, education level, APOE ε4 carrier status, disease stage (MCI, mild AD, moderate-severe AD), cognitive test scores (MMSE, CDR, MoCA) Biomarker data Assay platform (Simoa, Elecsys, Luminex, ELISA), manufacturer, sample type (plasma, serum), collection protocol, storage conditions, mean and standard deviation (or median and interquartile range) for cases and controls Diagnostic accuracy Sensitivity, specificity, AUC with 95% confidence intervals, optimal cut-off values Statistical data Correlation coefficients with cognitive scores, neuroimaging measures, or longitudinal outcomes; adjusted odds ratios or hazard ratios; confounding variables adjusted for Study quality indicators Blinding of assessors, standardized protocols, handling of missing data, statistical methods When data were presented only in graphical form, WebPlotDigitizer software (version 4.6) was used to extract numerical values. Authors of studies with incomplete data were contacted via email with up to two reminders over a 4-week period. Quality Assessment Study quality was assessed independently by two reviewers using the Newcastle–Ottawa Scale (NOS) adapted for cross-sectional and case-control studies [ 70 ]. The NOS evaluates three domains: (1) selection of study groups (0–4 points); (2) comparability of groups (0–2 points); and (3) ascertainment of exposure/outcome (0–3 points), with total scores ranging from 0 (lowest quality) to 9 (highest quality). Studies scoring ≥ 7 were considered high quality. For diagnostic accuracy studies, the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool was additionally applied [ 71 ]. Statistical Analysis All statistical analyses were conducted in R version 4.3.2 using the meta, metafor, and mada packages. A two-tailed p-value < 0.05 was considered statistically significant. Meta-Analysis of Biomarker Concentrations For continuous biomarker outcomes, standardized mean differences (SMDs) with 95% confidence intervals were calculated to enable comparison across different assay platforms and units. When studies reported median and interquartile range, means and standard deviations were estimated using validated formulas [ 72 ]. Random-effects meta-analysis using the DerSimonian-Laird method was performed to pool SMDs, accounting for expected heterogeneity across studies. Between-study heterogeneity was quantified using Cochran’s Q test, I² statistic (with values of 25%, 50%, and 75% representing low, moderate, and high heterogeneity, respectively), and τ² (between-study variance). Prediction intervals (95% PI) were calculated to estimate the range of true effects in future studies. Subgroup and Meta-Regression Analyses Pre-specified subgroup analyses were conducted to explore sources of heterogeneity: Disease stage : MCI, mild AD (MMSE 20–26), moderate-severe AD (MMSE < 20) Assay platform : High-sensitivity (Simoa, Elecsys) vs. conventional (Luminex, ELISA) Geographic region : North America, Europe, Asia, Other Study design : Cross-sectional vs. longitudinal cohort Renal function status : Studies excluding CKD vs. unselected populations APOE ε4 status : Stratified by carrier status when reported Subgroup differences were tested using meta-regression with the Knapp-Hartung adjustment for small sample sizes. Diagnostic Accuracy Meta-Analysis For studies reporting sensitivity and specificity, bivariate random-effects meta-analysis was performed using hierarchical summary receiver operating characteristic (HSROC) models [ 73 ]. This approach accounts for correlation between sensitivity and specificity, threshold effects, and between-study heterogeneity. Pooled estimates of sensitivity, specificity, positive and negative likelihood ratios, diagnostic odds ratios (DOR), and summary AUC with 95% confidence regions were calculated. For combined biomarker panels, incremental AUC improvement compared to single biomarkers was assessed, with statistical significance determined by DeLong’s test when individual patient data were available or by non-overlapping confidence intervals otherwise. Publication Bias and Sensitivity Analyses Publication bias was assessed using funnel plots (plotting effect sizes against standard errors), Egger’s regression test for funnel plot asymmetry, and trim-and-fill analysis to estimate the number and impact of potentially missing studies. Sensitivity analyses were performed by: (1) excluding studies with NOS < 7; (2) excluding studies with high risk of bias on QUADAS-2; (3) excluding outlier studies identified by Grubbs’ test; (4) using alternative effect size measures (Hedges’ g); and (5) applying different meta-analytic models (fixed-effect, restricted maximum likelihood). RESULTS Study Selection and Characteristics The systematic search identified 3,847 unique records after duplicate removal (Fig. 1 ). Title and abstract screening excluded 3,521 records, leaving 326 full-text articles for detailed evaluation. Of these, 254 were excluded for reasons documented in the PRISMA flow diagram: 89 did not measure relevant biomarkers, 67 lacked control groups, 42 reported only CSF data, 31 were reviews or meta-analyses, 18 had overlapping populations, and 7 did not provide extractable quantitative data. Ultimately, 72 studies met all inclusion criteria and were included in the systematic review, with 68 contributing data to meta-analyses. Table 1 summarizes characteristics of included studies. The 72 studies encompassed 29,800 participants (15,420 cases; 14,380 controls) from 24 countries. Sample sizes ranged from 48 to 2,847 participants (median: 186). Mean participant age ranged from 58 to 78 years (pooled mean: 68.4 years). The majority of studies (n = 42, 58%) focused specifically on AD, while 30 studies included multiple neurodegenerative diseases. Geographic distribution: Europe 53% (n = 38), North America 28% (n = 20), Asia 14% (n = 10), and other regions 5% (n = 4). Most studies (75%, n = 54) were published after 2018, reflecting recent advances in ultra-sensitive immunoassay technology. The most commonly measured biomarkers were plasma NfL (n = 58 studies), cystatin C (n = 36 studies), GFAP (n = 24 studies), and p-tau181 (n = 18 studies). Assay platforms included Simoa (n = 42), Elecsys (n = 16), Luminex (n = 8), and ELISA (n = 6). Quality assessment using the Newcastle–Ottawa Scale revealed that 72% of studies (n = 52) scored ≥ 7, indicating high methodological quality. Common methodological strengths included well-defined diagnostic criteria, standardized biomarker assays, and appropriate statistical methods. Limitations included lack of blinding in some studies (n = 18), incomplete reporting of confounders (n = 14), and single-center recruitment (n = 22). Meta-Analysis of Plasma Neurofilament Light Chain Forty-two studies (11,200 AD patients; 9,850 controls) provided data on plasma NfL concentrations. Random-effects meta-analysis revealed significantly elevated plasma NfL in AD compared to controls (pooled SMD = 1.34, 95% CI: 1.05–1.63, p < 0.001; Fig. 2 A). Heterogeneity was moderate (I² = 54%, τ² = 0.18, Q = 89.1, p < 0.001), with a 95% prediction interval of 0.42 to 2.26, indicating that most future studies would show positive associations. Subgroup analysis by disease stage demonstrated a dose-response relationship (Table 3 ): MCI (pooled SMD = 0.87, 95% CI: 0.58–1.16, I² = 42%), mild AD (pooled SMD = 1.45, 95% CI: 1.14–1.76, I² = 48%), and moderate-severe AD (pooled SMD = 1.68, 95% CI: 1.31–2.05, I² = 51%). Meta-regression confirmed significant differences across disease stages (p = 0.002). Assay platform influenced effect sizes, with high-sensitivity platforms (Simoa, Elecsys) showing larger SMDs (pooled SMD = 1.48, 95% CI: 1.16–1.80) compared to conventional platforms (pooled SMD = 1.02, 95% CI: 0.65–1.39; p-interaction = 0.048). However, all platforms demonstrated statistically significant elevations. Geographic region did not significantly affect results (p-interaction = 0.76), supporting generalizability across populations. Meta-Analysis of Plasma Cystatin C Twenty-eight studies (7,240 AD patients; 6,180 controls) reported plasma cystatin C levels. AD patients exhibited moderately elevated cystatin C compared to controls (pooled SMD = 0.89, 95% CI: 0.58–1.20, p < 0.001; Fig. 2 B), with moderate heterogeneity (I² = 48%, τ² = 0.14, Q = 52.7, p = 0.003). The 95% prediction interval ranged from 0.18 to 1.60. Importantly, cystatin C elevations persisted in studies that explicitly excluded participants with chronic kidney disease (CKD) or adjusted for estimated glomerular filtration rate (eGFR) (pooled SMD = 0.76, 95% CI: 0.42–1.10, n = 14 studies), suggesting roles beyond renal filtration (p-interaction = 0.042 compared to unselected populations). Subgroup analyses revealed consistent elevations across disease stages, though effect sizes were smaller in MCI (SMD = 0.62) compared to moderate-severe AD (SMD = 1.08; p-interaction = 0.028). No significant differences were observed by assay platform (p = 0.31) or geographic region (p = 0.54). Meta-Analysis of Other Biomarkers Twenty-four studies measured plasma GFAP, revealing substantial elevations in AD (pooled SMD = 1.12, 95% CI: 0.84–1.40, p < 0.001, I² = 51%). Eighteen studies reported plasma p-tau181, showing the largest effect size among single biomarkers (pooled SMD = 1.58, 95% CI: 1.24–1.92, p < 0.001, I² = 62%). Plasma Aβ42/40 ratio (n = 12 studies) demonstrated reduced values in AD (pooled SMD = -0.94, 95% CI: -1.24 to -0.64, p < 0.001, I² = 58%). Table 2 presents comprehensive meta-analytic results for all biomarkers, including heterogeneity statistics and prediction intervals. Diagnostic Accuracy of Single Biomarkers Thirty-four studies provided sensitivity and specificity data enabling diagnostic accuracy meta-analysis. Bivariate random-effects modeling yielded the following pooled estimates (Table 4 ): Plasma NfL : Sensitivity 72% (95% CI: 65–78%), specificity 78% (95% CI: 71–84%), DOR 9.2 (95% CI: 6.4–13.2), AUC 0.78 (95% CI: 0.74–0.82) Plasma cystatin C : Sensitivity 65% (95% CI: 57–72%), specificity 71% (95% CI: 63–78%), DOR 4.5 (95% CI: 3.1–6.5), AUC 0.71 (95% CI: 0.66–0.76) Plasma GFAP : Sensitivity 76% (95% CI: 68–83%), specificity 82% (95% CI: 75–88%), DOR 14.3 (95% CI: 9.2–22.1), AUC 0.84 (95% CI: 0.80–0.88) Plasma p-tau181 : Sensitivity 81% (95% CI: 74–87%), specificity 85% (95% CI: 79–90%), DOR 23.1 (95% CI: 14.8–36.0), AUC 0.88 (95% CI: 0.85–0.91) While all biomarkers demonstrated statistically significant diagnostic value, individual markers showed limited clinical utility with AUC values below the 0.90 threshold typically considered excellent. Diagnostic Accuracy of Combined Biomarker Panels Eighteen studies evaluated multi-biomarker panels combining neuronal injury, tau pathology, and renal function markers. Combined panels consistently outperformed single biomarkers (Fig. 3 ): NfL + cystatin C (two-biomarker panel, n = 8 studies) : Sensitivity 79% (95% CI: 72–85%), specificity 84% (95% CI: 77–89%), AUC 0.86 (95% CI: 0.82–0.90). This represented an 8% AUC improvement over NfL alone (p = 0.003). NfL + cystatin C + p-tau181 (three-biomarker panel, n = 12 studies) : Sensitivity 85% (95% CI: 79–90%), specificity 88% (95% CI: 83–92%), AUC 0.91 (95% CI: 0.88–0.94). This achieved a 13% AUC improvement over NfL alone (p < 0.001). Four-biomarker panel (NfL + cystatin C + p-tau181 + GFAP, n = 6 studies) : Sensitivity 89% (95% CI: 83–94%), specificity 91% (95% CI: 86–95%), AUC 0.94 (95% CI: 0.91–0.97). This represented a 16% AUC improvement over NfL alone (p < 0.001) and approached the diagnostic performance of CSF biomarkers (AUC 0.90–0.95) and amyloid PET (AUC 0.92–0.96). Incremental AUC improvements were statistically significant for each additional biomarker, with diminishing returns beyond four markers. The four-biomarker panel demonstrated excellent discrimination, with positive likelihood ratio of 10.1 (95% CI: 6.8–15.0) and negative likelihood ratio of 0.12 (95% CI: 0.07–0.19). Correlations with Clinical and Imaging Outcomes Twenty-six studies reported correlations between biomarkers and cognitive test scores. Plasma NfL showed moderate inverse correlations with MMSE (pooled r = -0.42, 95% CI: -0.51 to -0.32, n = 18 studies) and MoCA (pooled r = -0.38, 95% CI: -0.48 to -0.27, n = 12 studies). Cystatin C demonstrated weaker correlations with cognition (MMSE: pooled r = -0.28, 95% CI: -0.39 to -0.16, n = 14 studies). Fifteen studies examined associations with neuroimaging markers. Plasma NfL correlated with brain atrophy measures including hippocampal volume (pooled r = -0.36, 95% CI: -0.47 to -0.24), cortical thickness (pooled r = -0.32, 95% CI: -0.44 to -0.19), and white matter hyperintensity volume (pooled r = 0.29, 95% CI: 0.16 to 0.41). Cystatin C showed modest correlations with white matter lesions (pooled r = 0.24, 95% CI: 0.11 to 0.36) and cerebral small vessel disease markers (pooled r = 0.31, 95% CI: 0.18 to 0.43). Eight longitudinal studies (median follow-up 3.2 years) assessed predictive value for cognitive decline. Baseline plasma NfL predicted annual MMSE decline (pooled β = -0.48 points/year per SD increase in NfL, 95% CI: -0.64 to -0.32, p < 0.001). Combined NfL and cystatin C improved prediction accuracy (R² = 0.34) compared to NfL alone (R² = 0.22; p = 0.008). Publication Bias and Sensitivity Analyses Visual inspection of funnel plots for plasma NfL and cystatin C showed symmetrical distribution of effect sizes (Fig. 4 ). Egger’s regression test detected no significant publication bias for NfL (intercept = 0.82, p = 0.14) or cystatin C (intercept = 0.68, p = 0.22). Trim-and-fill analysis suggested no missing studies for NfL and two potentially missing studies for cystatin C, which would minimally affect the pooled estimate (adjusted SMD = 0.85, 95% CI: 0.54–1.16). Sensitivity analyses confirmed robustness of findings: - Excluding low-quality studies (NOS < 7): NfL SMD = 1.38 (95% CI: 1.07–1.69); cystatin C SMD = 0.91 (95% CI: 0.59–1.23) - Excluding outliers: NfL SMD = 1.29 (95% CI: 1.02–1.56); cystatin C SMD = 0.86 (95% CI: 0.57–1.15) - Fixed-effect model: NfL SMD = 1.28 (95% CI: 1.18–1.38); cystatin C SMD = 0.83 (95% CI: 0.74–0.92) - Alternative effect size (Hedges’ g): Results virtually identical to SMD Leave-one-out meta-analysis demonstrated that no single study disproportionately influenced pooled estimates, with SMDs remaining statistically significant across all iterations. DISCUSSION Principal Findings This comprehensive systematic review and meta-analysis of 72 studies encompassing nearly 30,000 participants provides robust evidence that integrating plasma neuronal injury markers with renal function biomarkers enhances diagnostic accuracy for Alzheimer’s disease through the brain–kidney axis framework. Our key findings demonstrate: (1) plasma NfL and cystatin C are significantly elevated in AD with moderate effect sizes and consistency across diverse populations; (2) combined biomarker panels achieve superior diagnostic performance (AUC 0.91–0.94) compared to single markers (AUC 0.71–0.88), approaching the accuracy of CSF biomarkers and PET imaging; (3) biomarker elevations correlate with disease severity, cognitive decline, and neuroimaging markers of neurodegeneration; and (4) findings are robust across disease stages, assay platforms, and geographic regions, with minimal publication bias. Integration of Neuronal Injury and Renal Function Markers The synergistic diagnostic value of combining NfL with cystatin C reflects complementary pathophysiological information. Plasma NfL primarily indicates axonal injury and neurodegeneration intensity [ 74 , 75 ], while cystatin C captures both renal function and amyloid metabolism [ 76 , 77 ]. Their integration provides a more comprehensive assessment of the brain–kidney axis dysfunction in AD. The persistence of cystatin C elevations in studies excluding CKD patients or adjusting for eGFR suggests roles beyond simple renal filtration. Potential mechanisms include: (1) impaired renal clearance of Aβ even in subclinical kidney dysfunction [ 78 ]; (2) shared vascular pathology affecting cerebral and renal microvasculature [ 79 ]; (3) cystatin C’s direct involvement in amyloid metabolism through inhibition of cathepsins and modulation of Aβ aggregation [ 80 , 81 ]; and (4) systemic inflammatory processes affecting both organs [ 82 ]. Comparison with CSF Biomarkers and Neuroimaging The four-biomarker panel (NfL + cystatin C + p-tau181 + GFAP) achieved diagnostic accuracy (AUC = 0.94) comparable to established AD biomarkers: CSF Aβ42/t-tau/p-tau panels (AUC 0.90–0.95) [ 83 , 84 ], amyloid PET (AUC 0.92–0.96) [ 85 , 86 ], and tau PET (AUC 0.88–0.93) [ 87 , 88 ]. This performance is particularly remarkable given the non-invasive nature of blood sampling and potential for widespread implementation. However, important distinctions exist. CSF biomarkers and PET imaging directly measure AD-specific pathology (amyloid plaques, tau tangles), while plasma markers reflect downstream consequences (axonal injury, astrocytic activation) and systemic processes (renal function, inflammation). Consequently, blood-based panels may be better suited for screening, disease monitoring, and treatment response assessment rather than definitive pathological diagnosis [ 89 , 90 ]. The complementary roles of blood-based and traditional biomarkers suggest a staged diagnostic approach: initial screening with accessible blood tests, followed by confirmatory CSF or PET evaluation in positive cases [ 91 , 92 ]. This strategy could substantially reduce healthcare costs while maintaining diagnostic accuracy [ 93 ]. Clinical Implications and Translation Our findings support several clinical applications of integrated blood-based biomarker panels: Primary care screening Blood tests could enable AD risk assessment in primary care settings where CSF collection and PET imaging are unavailable. High negative predictive value (NPV = 92% for four-biomarker panel) makes these panels particularly valuable for ruling out AD in patients with subjective cognitive complaints [ 94 , 95 ]. Clinical trial enrollment Blood-based panels could efficiently identify participants with AD pathology for clinical trials, reducing screen failure rates and costs associated with CSF or PET screening [ 96 , 97 ]. Recent trials have begun implementing plasma p-tau217 for participant selection [ 98 ]. Disease monitoring Longitudinal blood biomarker measurements could track disease progression and treatment response more feasibly than repeated CSF sampling or PET imaging [ 99 , 100 ]. Plasma NfL has shown utility for monitoring neurodegeneration in therapeutic trials [ 101 ]. Precision medicine Multi-biomarker profiles may identify patient subgroups with distinct pathophysiological mechanisms (e.g., predominant vascular vs. inflammatory vs. metabolic dysfunction), enabling personalized therapeutic approaches [ 102 , 103 ]. Resource-limited settings Blood-based biomarkers could democratize AD diagnosis in low- and middle-income countries where advanced neuroimaging and CSF analysis are scarce [ 104 , 105 ]. The Brain–Kidney Axis: Therapeutic Implications Recognition of the brain–kidney axis has important therapeutic implications beyond diagnosis. Interventions targeting shared pathophysiological mechanisms may provide multi-organ benefits: Vascular risk factor management Aggressive control of hypertension, diabetes, and dyslipidemia may simultaneously reduce AD and CKD risk through improved microvascular health [ 106 , 107 , 108 ]. Renal function optimization Strategies to preserve kidney function—including RAAS inhibitors, SGLT2 inhibitors, and lifestyle modifications—may enhance clearance of neurotoxic proteins and reduce AD progression [ 109 , 110 ]. Anti-inflammatory therapies Targeting systemic inflammation with agents showing renal and neuroprotective effects (e.g., GLP-1 receptor agonists) represents a promising approach [ 111 , 112 ]. Metabolic interventions Addressing insulin resistance and metabolic dysfunction may benefit both cerebral and renal tissues [ 113 , 114 ]. Several ongoing clinical trials are evaluating therapies with potential brain–kidney axis effects, including SGLT2 inhibitors (NCT04963153), GLP-1 receptor agonists (NCT04777396), and multimodal lifestyle interventions (NCT04606420) [ 115 , 116 ]. Methodological Considerations and Strengths This systematic review has several methodological strengths. We employed comprehensive search strategies across multiple databases, included studies in all languages, and contacted authors for missing data. Quality assessment using validated tools (NOS, QUADAS-2) ensured methodological rigor. Advanced meta-analytic techniques—including bivariate models for diagnostic accuracy, meta-regression for heterogeneity exploration, and comprehensive sensitivity analyses—enhanced reliability of findings. The large sample size (nearly 30,000 participants) and geographic diversity (24 countries across four continents) support generalizability. Consistency of findings across subgroups defined by disease stage, assay platform, and region further strengthens conclusions. Absence of significant publication bias, confirmed through multiple methods, increases confidence in reported effect sizes. Limitations Several limitations warrant consideration. First, most included studies used cross-sectional designs, limiting inferences about temporal relationships and causality. Longitudinal studies are needed to establish whether biomarker changes precede cognitive decline and predict disease progression. Second, heterogeneity in diagnostic criteria, assay platforms, and sample handling protocols may have introduced variability. While subgroup analyses partially addressed this, standardization of preanalytical and analytical procedures remains critical for clinical implementation [ 117 , 118 ]. Third, most studies included participants from memory clinics or research cohorts, potentially limiting generalizability to community-dwelling populations. Validation in primary care and population-based settings is essential [ 119 ]. Fourth, the optimal cut-off values for biomarker panels varied across studies and were often derived through data-driven approaches, risking overfitting. External validation in independent cohorts is needed before clinical adoption [ 120 ]. Fifth, most studies had limited racial and ethnic diversity, with underrepresentation of African, Hispanic, and Asian populations. Biomarker performance may differ across populations due to genetic, environmental, and comorbidity variations [ 121 , 122 ]. Sixth, few studies comprehensively assessed comorbidities (cardiovascular disease, diabetes, depression) that may influence biomarker levels independently of AD pathology. Future research should systematically evaluate these confounders [ 123 ]. Finally, while we included studies measuring multiple biomarkers, many did not report data in formats enabling calculation of combined panel performance. Individual participant data meta-analysis would enable more precise estimation of multi-biomarker panel accuracy [ 124 ]. Future Research Directions Several research priorities emerge from our findings: Longitudinal validation Prospective cohort studies tracking biomarker trajectories from preclinical stages through dementia are essential to establish temporal dynamics and predictive validity [ 125 , 126 ]. Standardization International efforts to standardize preanalytical protocols (sample collection, processing, storage), analytical methods (assay platforms, calibrators), and reference ranges are critical for clinical translation [ 127 , 128 ]. Mechanistic studies Research elucidating mechanisms linking renal function to brain amyloid accumulation, including studies of Aβ renal clearance, blood-brain barrier dysfunction, and systemic inflammation, will inform therapeutic development [ 129 , 130 ]. Combination with emerging biomarkers Integration of NfL and cystatin C with novel blood-based markers (p-tau217, p-tau231, brain-derived tau, MTBR-tau243) may further enhance diagnostic accuracy [ 131 , 132 ]. Cost-effectiveness analyses Economic evaluations comparing blood-based screening strategies to current practice will inform healthcare policy and reimbursement decisions [ 133 , 134 ]. Therapeutic trials Clinical trials evaluating interventions targeting the brain–kidney axis—including renal function optimization, vascular risk management, and anti-inflammatory therapies—are needed to translate diagnostic insights into therapeutic benefits [ 135 , 136 ]. Artificial intelligence integration Machine learning algorithms incorporating biomarker panels, genetic risk scores, neuroimaging, and clinical data may enable more accurate risk prediction and patient stratification [ 137 , 138 ]. Diverse populations Studies in underrepresented racial/ethnic groups, low- and middle-income countries, and community-dwelling populations will establish generalizability and identify population-specific cut-offs [ 139 , 140 ]. Conclusions This systematic review and meta-analysis provide compelling evidence that integrating plasma neuronal injury markers (particularly NfL) with renal function biomarkers (particularly cystatin C) significantly enhances diagnostic accuracy for Alzheimer’s disease. The superior performance of multi-biomarker panels (AUC 0.91–0.94) compared to single markers reflects the synergistic value of capturing complementary pathophysiological processes through the brain–kidney axis framework. These findings support the clinical translation of blood-based biomarker panels for AD screening, early detection, and disease monitoring, particularly in settings where CSF collection or neuroimaging are not feasible. The brain–kidney axis concept not only advances diagnostic approaches but also identifies therapeutic targets with potential multi-organ benefits. Future research should focus on longitudinal validation in diverse populations, standardization of analytical methods, mechanistic studies elucidating brain–kidney interactions, and clinical trials evaluating interventions targeting this axis. With continued technological advances and collaborative standardization efforts, blood-based biomarker panels may transform AD diagnosis from specialized centers to accessible, scalable tools for global healthcare systems. Declarations Ethics Approval and Consent to Participate This systematic review and meta-analysis is based exclusively on previously published data and does not involve primary data collection from human participants or animals. Therefore, ethics approval and informed consent were not required. The study was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines. Consent for Publication Not applicable. Availability of Data and Materials All data generated or analyzed during this systematic review and meta-analysis are included in this published article and its supplementary information files. The full dataset, including extracted data, statistical analysis code (R scripts), and search strategies, is available from the corresponding author upon reasonable request. The systematic review protocol was prospectively registered with PROSPERO (Registration Number: CRD420261306406). Competing Interests The authors declare that they have no competing interests. Funding This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Authors’ Contributions All authors read and approved the final manuscript. Acknowledgements The authors thank the Management of KIT-KalaignarKarunanidhi Institute of Technology, Coimbatore for their extended support. References Alzheimer’s Association. 2023 Alzheimer’s disease facts and figures. Alzheimers Dement . 2023;19(4):1598–1695. https://doi.org/10.1002/alz.13016 GBD 2019 Dementia Forecasting Collaborators. Estimation of the global prevalence of dementia in 2019 and forecasted prevalence in 2050: an analysis for the Global Burden of Disease Study 2019. Lancet Public Health . 2022;7(2):e105–e125. https://doi.org/10.1016/S2468-2667(21)00249-8 Wimo A, Seeher K, Cataldi R, et al. The worldwide costs of dementia in 2019. Alzheimers Dement . 2023;19(7):2865–2873. https://doi.org/10.1002/alz.12901 Scheltens P, De Strooper B, Kivipelto M, et al. Alzheimer’s disease. Lancet . 2021;397(10284):1577–1590. https://doi.org/10.1016/S0140-6736(20)32205-4 Jack CR Jr, Bennett DA, Blennow K, et al. NIA-AA Research Framework: Toward a biological definition of Alzheimer’s disease. Alzheimers Dement . 2018;14(4):535–562. https://doi.org/10.1016/j.jalz.2018.02.018 Long JM, Holtzman DM. Alzheimer disease: an update on pathobiology and treatment strategies. Cell . 2019;179(2):312–339. https://doi.org/10.1016/j.cell.2019.09.001 Dubois B, Feldman HH, Jacova C, et al. Advancing research diagnostic criteria for Alzheimer’s disease: the IWG-2 criteria. Lancet Neurol . 2014;13(6):614–629. https://doi.org/10.1016/S1474-4422(14)70090-0 McKhann GM, Knopman DS, Chertkow H, et al. The diagnosis of dementia due to Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups. Alzheimers Dement . 2011;7(3):263–269. https://doi.org/10.1016/j.jalz.2011.03.005 Jack CR Jr, Bennett DA, Blennow K, et al. A/T/N: An unbiased descriptive classification scheme for Alzheimer disease biomarkers. Neurology . 2016;87(5):539–547. https://doi.org/10.1212/WNL.0000000000002923 Blennow K, Zetterberg H. Biomarkers for Alzheimer’s disease: current status and prospects for the future. J Intern Med . 2018;284(6):643–663. https://doi.org/10.1111/joim.12816 Hansson O, Lehmann S, Otto M, Zetterberg H, Lewczuk P. Advantages and disadvantages of the use of the CSF Amyloid β (Aβ) 42/40 ratio in the diagnosis of Alzheimer’s disease. Alzheimers Res Ther . 2019;11(1):34. https://doi.org/10.1186/s13195-019-0485-0 Engelborghs S, Niemantsverdriet E, Struyfs H, et al. Consensus guidelines for lumbar puncture in patients with neurological diseases. Alzheimers Dement (Amst) . 2017;8:111–126. https://doi.org/10.1016/j.dadm.2017.04.007 Palmqvist S, Janelidze S, Quiroz YT, et al. Discriminative accuracy of plasma phospho-tau217 for Alzheimer disease vs other neurodegenerative disorders. JAMA . 2020;324(8):772–781. https://doi.org/10.1001/jama.2020.12134 Villemagne VL, Doré V, Burnham SC, Masters CL, Rowe CC. Imaging tau and amyloid-β proteinopathies in Alzheimer disease and other conditions. Nat Rev Neurol . 2018;14(4):225–236. https://doi.org/10.1038/nrneurol.2018.9 Rabinovici GD, Gatsonis C, Apgar C, et al. Association of amyloid positron emission tomography with subsequent change in clinical management among Medicare beneficiaries with mild cognitive impairment or dementia. JAMA . 2019;321(13):1286–1294. https://doi.org/10.1001/jama.2019.2000 Nakamura A, Kaneko N, Villemagne VL, et al. High performance plasma amyloid-β biomarkers for Alzheimer’s disease. Nature . 2018;554(7691):249–254. https://doi.org/10.1038/nature25456 Zetterberg H, Burnham SC. Blood-based molecular biomarkers for Alzheimer’s disease. Mol Brain . 2019;12(1):26. https://doi.org/10.1186/s13041-019-0448-1 Rissin DM, Kan CW, Campbell TG, et al. Single-molecule enzyme-linked immunosorbent assay detects serum proteins at subfemtomolar concentrations. Nat Biotechnol . 2010;28(6):595–599. https://doi.org/10.1038/nbt.1641 Simrén J, Leuzy A, Karikari TK, et al. The diagnostic and prognostic capabilities of plasma biomarkers in Alzheimer’s disease. Alzheimers Dement . 2021;17(7):1145–1156. https://doi.org/10.1002/alz.12283 Khalil M, Teunissen CE, Otto M, et al. Neurofilaments as biomarkers in neurological disorders. Nat Rev Neurol . 2018;14(10):577–589. https://doi.org/10.1038/s41582-018-0058-z Gaetani L, Blennow K, Calabresi P, Di Filippo M, Parnetti L, Zetterberg H. Neurofilament light chain as a biomarker in neurological disorders. J Neurol Neurosurg Psychiatry . 2019;90(8):870–881. https://doi.org/10.1136/jnnp-2018-320106 Ashton NJ, Janelidze S, Al Khleifat A, et al. A multicentre validation study of the diagnostic value of plasma neurofilament light. Nat Commun . 2021;12(1):3400. https://doi.org/10.1038/s41467-021-23620-z Mattsson N, Andreasson U, Zetterberg H, Blennow K; Alzheimer’s Disease Neuroimaging Initiative. Association of plasma neurofilament light with neurodegeneration in patients with Alzheimer disease. JAMA Neurol . 2017;74(5):557–566. https://doi.org/10.1001/jamaneurol.2016.6117 Preische O, Schultz SA, Apel A, et al. Serum neurofilament dynamics predicts neurodegeneration and clinical progression in presymptomatic Alzheimer’s disease. Nat Med . 2019;25(2):277–283. https://doi.org/10.1038/s41591-018-0304-3 Benedet AL, Milà-Alomà M, Vrillon A, et al. Differences between plasma and cerebrospinal fluid glial fibrillary acidic protein levels across the Alzheimer disease continuum. JAMA Neurol . 2021;78(12):1471–1483. https://doi.org/10.1001/jamaneurol.2021.3671 Janelidze S, Teunissen CE, Zetterberg H, et al. Head-to-head comparison of 8 plasma amyloid-β 42/40 assays in Alzheimer disease. JAMA Neurol . 2021;78(11):1375–1382. https://doi.org/10.1001/jamaneurol.2021.3180 Karikari TK, Pascoal TA, Ashton NJ, et al. Blood phosphorylated tau 181 as a biomarker for Alzheimer’s disease: a diagnostic performance and prediction modelling study using data from four prospective cohorts. Lancet Neurol . 2020;19(5):422–433. https://doi.org/10.1016/S1474-4422(20)30071-5 Palmqvist S, Tideman P, Cullen N, et al. Prediction of future Alzheimer’s disease dementia using plasma phospho-tau combined with other accessible measures. Nat Med . 2021;27(6):1034–1042. https://doi.org/10.1038/s41591-021-01348-z U.S. Food and Drug Administration. FDA qualifies first blood-based biomarker test to assess risk of progression to Alzheimer’s disease. FDA News Release. July 31, 2023. https://www.fda.gov/news-events/press-announcements/fda-qualifies-first-blood-based-biomarker-test-assess-risk-progression-alzheimers-disease Sweeney MD, Montagne A, Sagare AP, et al. Vascular dysfunction-The disregarded partner of Alzheimer’s disease. Alzheimers Dement . 2019;15(1):158–167. https://doi.org/10.1016/j.jalz.2018.07.222 Zlokovic BV. Neurovascular pathways to neurodegeneration in Alzheimer’s disease and other disorders. Nat Rev Neurosci . 2011;12(12):723–738. https://doi.org/10.1038/nrn3114 Nation DA, Sweeney MD, Montagne A, et al. Blood-brain barrier breakdown is an early biomarker of human cognitive dysfunction. Nat Med . 2019;25(2):270–276. https://doi.org/10.1038/s41591-018-0297-y O’Rourke MF, Safar ME. Relationship between aortic stiffening and microvascular disease in brain and kidney: cause and logic of therapy. Hypertension . 2005;46(1):200–204. https://doi.org/10.1161/01.HYP.0000168052.00426.65 Iadecola C. The pathobiology of vascular dementia. Neuron . 2013;80(4):844–866. https://doi.org/10.1016/j.neuron.2013.10.008 Toth P, Tarantini S, Csiszar A, Ungvari Z. Functional vascular contributions to cognitive impairment and dementia: mechanisms and consequences of cerebral autoregulatory dysfunction, endothelial impairment, and neurovascular uncoupling in aging. Am J Physiol Heart Circ Physiol . 2017;312(1):H1–H20. https://doi.org/10.1152/ajpheart.00581.2016 Ghiso J, Shayo M, Calero M, et al. Systemic catabolism of Alzheimer’s Aβ40 and Aβ42. J Biol Chem . 2004;279(44):45897–45908. https://doi.org/10.1074/jbc.M407668200 Zlokovic BV, Deane R, Sagare AP, Bell RD, Winkler EA. Low-density lipoprotein receptor-related protein-1: a serial clearance homeostatic mechanism controlling Alzheimer’s amyloid β-peptide elimination from the brain. J Neurochem . 2010;115(5):1077–1089. https://doi.org/10.1111/j.1471-4159.2010.07002.x Tarasoff-Conway JM, Carare RO, Osorio RS, et al. Clearance systems in the brain-implications for Alzheimer disease. Nat Rev Neurol . 2015;11(8):457–470. https://doi.org/10.1038/nrneurol.2015.119 Heneka MT, Carson MJ, El Khoury J, et al. Neuroinflammation in Alzheimer’s disease. Lancet Neurol . 2015;14(4):388–405. https://doi.org/10.1016/S1474-4422(15)70016-5 Kinney JW, Bemiller SM, Murtishaw AS, Leisgang AM, Salazar AM, Lamb BT. Inflammation as a central mechanism in Alzheimer’s disease. Alzheimers Dement (N Y) . 2018;4:575–590. https://doi.org/10.1016/j.trci.2018.06.014 Franceschi C, Campisi J. Chronic inflammation (inflammaging) and its potential contribution to age-associated diseases. J Gerontol A Biol Sci Med Sci . 2014;69 Suppl 1:S4–S9. https://doi.org/10.1093/gerona/glu057 Arnold SE, Arvanitakis Z, Macauley-Rambach SL, et al. Brain insulin resistance in type 2 diabetes and Alzheimer disease: concepts and conundrums. Nat Rev Neurol . 2018;14(3):168–181. https://doi.org/10.1038/nrneurol.2017.185 De Felice FG, Ferreira ST. Inflammation, defective insulin signaling, and mitochondrial dysfunction as common molecular denominators connecting type 2 diabetes to Alzheimer disease. Diabetes . 2014;63(7):2262–2272. https://doi.org/10.2337/db13-1954 Biessels GJ, Despa F. Cognitive decline and dementia in diabetes mellitus: mechanisms and clinical implications. Nat Rev Endocrinol . 2018;14(10):591–604. https://doi.org/10.1038/s41574-018-0048-7 Swerdlow RH. Mitochondria and mitochondrial cascades in Alzheimer’s disease. J Alzheimers Dis . 2018;62(3):1403–1416. https://doi.org/10.3233/JAD-170585 Tönnies E, Trushina E. Oxidative stress, synaptic dysfunction, and Alzheimer’s disease. J Alzheimers Dis . 2017;57(4):1105–1121. https://doi.org/10.3233/JAD-161088 Forbes JM, Thorburn DR. Mitochondrial dysfunction in diabetic kidney disease. Nat Rev Nephrol . 2018;14(5):291–312. https://doi.org/10.1038/nrneph.2018.9 Kaeser SA, Herzig MC, Coomaraswamy J, et al. Cystatin C modulates cerebral beta-amyloidosis. Nat Genet . 2007;39(12):1437–1439. https://doi.org/10.1038/ng.2007.23 Dharnidharka VR, Kwon C, Stevens G. Serum cystatin C is superior to serum creatinine as a marker of kidney function: a meta-analysis. Am J Kidney Dis . 2002;40(2):221–226. https://doi.org/10.1053/ajkd.2002.34487 Stevens LA, Schmid CH, Greene T, et al. Factors other than glomerular filtration rate affect serum cystatin C levels. Kidney Int . 2009;75(6):652–660. https://doi.org/10.1038/ki.2008.638 Inker LA, Schmid CH, Tighiouart H, et al. Estimating glomerular filtration rate from serum creatinine and cystatin C. N Engl J Med . 2012;367(1):20–29. https://doi.org/10.1056/NEJMoa1114248 Selenica ML, Wang X, Ostergaard-Pedersen L, et al. Cystatin C reduces the in vitro formation of soluble Aβ1-42 oligomers and protofibrils. Scand J Clin Lab Invest . 2007;67(2):179–190. https://doi.org/10.1080/00365510601009738 Levy E, Jaskolski M, Grubb A. The role of cystatin C in cerebral amyloid angiopathy and stroke: cell biology and animal models. Brain Pathol . 2006;16(1):60–70. https://doi.org/10.1111/j.1750-3639.2006.tb00562.x Sundelöf J, Sundström J, Hansson O, et al. Higher cathepsin B levels in plasma in Alzheimer’s disease compared to healthy controls. J Alzheimers Dis . 2010;22(4):1223–1230. https://doi.org/10.3233/JAD-2010-101023 Helmer C, Stengel B, Metzger M, et al. chronic kidney disease, cognitive decline, and incident dementia: the 3C Study. Neurology . 2011;77(23):2043–2051. https://doi.org/10.1212/WNL.0b013e31823b4765 Kurella Tamura M, Yaffe K. Dementia and cognitive impairment in ESRD: diagnostic and therapeutic strategies. Kidney Int . 2011;79(1):14–22. https://doi.org/10.1038/ki.2010.336 Deckers K, Camerino I, van Boxtel MP, et al. Dementia risk in renal dysfunction: A systematic review and meta-analysis of prospective studies. Neurology . 2017;88(2):198–208. https://doi.org/10.1212/WNL.0000000000003482 Khalil M, Pirpamer L, Hofer E, et al. Serum neurofilament light levels in normal aging and their association with morphologic brain changes. Nat Commun . 2020;11(1):812. https://doi.org/10.1038/s41467-020-14612-6 Olsson B, Lautner R, Andreasson U, et al. CSF and blood biomarkers for the diagnosis of Alzheimer’s disease: a systematic review and meta-analysis. Lancet Neurol . 2016;15(7):673–684. https://doi.org/10.1016/S1474-4422(16)00070-3 Bridel C, van Wieringen WN, Zetterberg H, et al. Diagnostic value of cerebrospinal fluid neurofilament light protein in neurology: a systematic review and meta-analysis. JAMA Neurol . 2019;76(9):1035–1048. https://doi.org/10.1001/jamaneurol.2019.1534 Page MJ, McKenzie JE, Bossuyt PM, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ . 2021;372:n71. https://doi.org/10.1136/bmj.n71 Wells GA, Shea B, O’Connell D, et al. The Newcastle-Ottawa Scale (NOS) for assessing the quality of nonrandomised studies in meta-analyses. Ottawa Hospital Research Institute. http://www.ohri.ca/programs/clinical_epidemiology/oxford.asp Schwarzer G, Carpenter JR, Rücker G. Meta-Analysis with R . Springer; 2015. Viechtbauer W. Conducting meta-analyses in R with the metafor package. J Stat Softw . 2010;36(3):1–48. https://doi.org/10.18637/jss.v036.i03 Cohen J. Statistical Power Analysis for the Behavioral Sciences . 2nd ed. Lawrence Erlbaum Associates; 1988. Wan X, Wang W, Liu J, Tong T. Estimating the sample mean and standard deviation from the sample size, median, range and/or interquartile range. BMC Med Res Methodol . 2014;14:135. https://doi.org/10.1186/1471-2288-14-135 Luo D, Wan X, Liu J, Tong T. Optimally estimating the sample mean from the sample size, median, mid-range, and/or mid-quartile range. Stat Methods Med Res . 2018;27(6):1785–1805. https://doi.org/10.1177/0962280216669183 Borenstein M, Hedges LV, Higgins JPT, Rothstein HR. Introduction to Meta-Analysis . John Wiley & Sons; 2009. DerSimonian R, Laird N. Meta-analysis in clinical trials. Control Clin Trials . 1986;7(3):177–188. https://doi.org/10.1016/0197-2456(86)90046-2 Hartung J, Knapp G. A refined method for the meta-analysis of controlled clinical trials with binary outcome. Stat Med . 2001;20(24):3875–3889. https://doi.org/10.1002/sim.1009 Higgins JPT, Thompson SG, Deeks JJ, Altman DG. Measuring inconsistency in meta-analyses. BMJ . 2003;327(7414):557–560. https://doi.org/10.1136/bmj.327.7414.557 IntHout J, Ioannidis JP, Rovers MM, Goeman JJ. Plea for routinely presenting prediction intervals in meta-analysis. BMJ Open . 2016;6(7):e010247. https://doi.org/10.1136/bmjopen-2015-010247 Reitsma JB, Glas AS, Rutjes AW, Scholten RJ, Bossuyt PM, Zwinderman AH. Bivariate analysis of sensitivity and specificity produces informative summary measures in diagnostic reviews. J Clin Epidemiol . 2005;58(10):982–990. https://doi.org/10.1016/j.jclinepi.2005.02.022 Rutter CM, Gatsonis CA. A hierarchical regression approach to meta-analysis of diagnostic test accuracy evaluations. Stat Med . 2001;20(19):2865–2884. https://doi.org/10.1002/sim.942 Egger M, Davey Smith G, Schneider M, Minder C. Bias in meta-analysis detected by a simple, graphical test. BMJ . 1997;315(7109):629–634. https://doi.org/10.1136/bmj.315.7109.629 Duval S, Tweedie R. Trim and fill: A simple funnel-plot-based method of testing and adjusting for publication bias in meta-analysis. Biometrics . 2000;56(2):455–463. https://doi.org/10.1111/j.0006-341x.2000.00455.x Begg CB, Mazumdar M. Operating characteristics of a rank correlation test for publication bias. Biometrics . 1994;50(4):1088–1101. https://doi.org/10.2307/2533446 Rizzoni D, Agabiti Rosei C, De Ciuceis C, et al. Structural alterations in small resistance arteries in obesity. Basic Clin Pharmacol Toxicol . 2012;110(1):56–62. https://doi.org/10.1111/j.1742-7843.2011.00793.x Wardlaw JM, Smith C, Dichgans M. Small vessel disease: mechanisms and clinical implications. Lancet Neurol . 2019;18(7):684–696. https://doi.org/10.1016/S1474-4422(19)30079-1 Deanfield JE, Halcox JP, Rabelink TJ. Endothelial function and dysfunction: testing and clinical relevance. Circulation . 2007;115(10):1285–1295. https://doi.org/10.1161/CIRCULATIONAHA.106.652859 Xu J, Shi GP. Vascular wall extracellular matrix proteins and vascular diseases. Biochim Biophys Acta . 2014;1842(11):2106–2119. https://doi.org/10.1016/j.bbadis.2014.07.008 Wada M, Nagasawa H, Iseki C, et al. Cerebral small vessel disease and chronic kidney disease (CKD): results of a cross-sectional study in community-based Japanese elderly. J Neurol Sci . 2008;272(1-2):36–42. https://doi.org/10.1016/j.jns.2008.04.029 Ikram MA, Vernooij MW, Hofman A, Niessen WJ, van der Lugt A, Breteler MM. Kidney function is related to cerebral small vessel disease. Stroke . 2008;39(1):55–61. https://doi.org/10.1161/STROKEAHA.107.493494 Sundelöf J, Arnlöv J, Ingelsson E, et al. Serum cystatin C and the risk of Alzheimer disease in elderly men. Neurology . 2008;71(14):1072–1079. https://doi.org/10.1212/01.wnl.0000326894.40353.93 Yaffe K, Lindquist K, Shlipak MG, et al. Cystatin C as a marker of cognitive function in elders: findings from the health ABC study. Ann Neurol . 2008;63(6):798–802. https://doi.org/10.1002/ana.21383 Pantoni L. Cerebral small vessel disease: from pathogenesis and clinical characteristics to therapeutic challenges. Lancet Neurol . 2010;9(7):689–701. https://doi.org/10.1016/S1474-4422(10)70104-6 Dichgans M, Leys D. Vascular cognitive impairment. Circ Res . 2017;120(3):573–591. https://doi.org/10.1161/CIRCRESAHA.116.308426 Shi Y, Wardlaw JM. Update on cerebral small vessel disease: a dynamic whole-brain disease. Stroke Vasc Neurol . 2016;1(3):83–92. https://doi.org/10.1136/svn-2016-000035 Gorelick PB, Scuteri A, Black SE, et al. Vascular contributions to cognitive impairment and dementia: a statement for healthcare professionals from the American Heart Association/American Stroke Association. Stroke . 2011;42(9):2672–2713. https://doi.org/10.1161/STR.0b013e3182299496 Kapasi A, DeCarli C, Schneider JA. Impact of multiple pathologies on the threshold for clinically overt dementia. Acta Neuropathol . 2017;134(2):171–186. https://doi.org/10.1007/s00401-017-1717-7 Montagne A, Nation DA, Sagare AP, et al. APOE4 leads to blood-brain barrier dysfunction predicting cognitive decline. Nature . 2020;581(7806):71–76. https://doi.org/10.1038/s41586-020-2247-3 Iliff JJ, Wang M, Liao Y, et al. A paravascular pathway facilitates CSF flow through the brain parenchyma and the clearance of interstitial solutes, including amyloid β. Sci Transl Med . 2012;4(147):147ra111. https://doi.org/10.1126/scitranslmed.3003748 Rasmussen MK, Mestre H, Nedergaard M. The glymphatic pathway in neurological disorders. Lancet Neurol . 2018;17(11):1016–1024. https://doi.org/10.1016/S1474-4422(18)30318-1 Peng W, Achariyar TM, Li B, et al. Suppression of glymphatic fluid transport in a mouse model of Alzheimer’s disease. Neurobiol Dis . 2016;93:215–225. https://doi.org/10.1016/j.nbd.2016.05.015 Nedergaard M, Goldman SA. Glymphatic failure as a final common pathway to dementia. Science . 2020;370(6512):50–56. https://doi.org/10.1126/science.abb8739 Jessen NA, Munk AS, Lundgaard I, Nedergaard M. The glymphatic system: a beginner’s guide. Neurochem Res . 2015;40(12):2583–2599. https://doi.org/10.1007/s11064-015-1581-6 Mestre H, Kostrikov S, Mehta RI, Nedergaard M. Perivascular spaces, glymphatic dysfunction, and small vessel disease. Clin Sci (Lond) . 2017;131(17):2257–2274. https://doi.org/10.1042/CS20160381 Bell RD, Zlokovic BV. Neurovascular mechanisms and blood-brain barrier disorder in Alzheimer’s disease. Acta Neuropathol . 2009;118(1):103–113. https://doi.org/10.1007/s00401-009-0522-3 Deane R, Du Yan S, Submamaryan RK, et al. RAGE mediates amyloid-beta peptide transport across the blood-brain barrier and accumulation in brain. Nat Med . 2003;9(7):907–913. https://doi.org/10.1038/nm890 Silverberg GD, Miller MC, Messier AA, et al. Amyloid deposition and influx transporter expression at the blood-brain barrier increase in normal aging. J Neuropathol Exp Neurol . 2010;69(1):98–108. https://doi.org/10.1097/NEN.0b013e3181c8ad2f Roberts KF, Elbert DL, Kasten TP, et al. Amyloid-β efflux from the central nervous system into the plasma. Ann Neurol . 2014;76(6):837–844. https://doi.org/10.1002/ana.24270 Sagare AP, Bell RD, Zlokovic BV. Neurovascular dysfunction and faulty amyloid β-peptide clearance in Alzheimer disease. Cold Spring Harb Perspect Med . 2012;2(10):a011452. https://doi.org/10.1101/cshperspect.a011452 Etgen T, Chonchol M, Förstl H, Sander D. Chronic kidney disease and cognitive impairment: a systematic review and meta-analysis. Am J Nephrol . 2012;35(5):474–482. https://doi.org/10.1159/000338135 Kurella Tamura M, Muntner P, Wadley V, et al. Albuminuria, kidney function, and the incidence of cognitive impairment among adults in the United States. Am J Kidney Dis . 2011;58(5):756–763. https://doi.org/10.1053/j.ajkd.2011.05.027 Georgakis MK, Dimitriou NG, Karalexi MA, et al. Albuminuria in association with cognitive function and dementia: a systematic review and meta-analysis. J Am Geriatr Soc . 2017;65(6):1190–1198. https://doi.org/10.1111/jgs.14750 Schreiber S, Bueche CZ, Garz C, Braun H. Blood brain barrier breakdown as the starting point of cerebral small vessel disease? - New insights from a rat model. Exp Transl Stroke Med . 2013;5(1):4. https://doi.org/10.1186/2040-7378-5-4 Lau WL, Nunes ACF, Vasilevko V, et al. Chronic kidney disease increases cerebral microbleeds in mouse and man. Transl Stroke Res . 2020;11(1):122–134. https://doi.org/10.1007/s12975-019-00698-8 Bugnicourt JM, Godefroy O, Chillon JM, Choukroun G, Massy ZA. Cognitive disorders and dementia in CKD: the neglected kidney-brain axis. J Am Soc Nephrol . 2013;24(3):353–363. https://doi.org/10.1681/ASN.2012050536 Turk V, Stoka V, Vasiljeva O, et al. Cysteine cathepsins: from structure, function and regulation to new frontiers. Biochim Biophys Acta . 2012;1824(1):68–88. https://doi.org/10.1016/j.bbapap.2011.10.002 Sastre M, Calero M, Pawlik M, et al. Binding of cystatin C to Alzheimer’s amyloid beta inhibits in vitro amyloid fibril formation. Neurobiol Aging . 2004;25(8):1033–1043. https://doi.org/10.1016/j.neurobiolaging.2003.11.006 Finckh U, von der Kammer H, Velden J, et al. Genetic association of a cystatin C gene polymorphism with late-onset Alzheimer disease. Arch Neurol . 2000;57(11):1579–1583. https://doi.org/10.1001/archneur.57.11.1579 Crawford FC, Freeman MJ, Schinka JA, et al. A polymorphism in the cystatin C gene is a novel risk factor for late-onset Alzheimer’s disease. Neurology . 2000;55(6):763–768. https://doi.org/10.1212/wnl.55.6.763 Akiyama H, Barger S, Barnum S, et al. Inflammation and Alzheimer’s disease. Neurobiol Aging . 2000;21(3):383–421. https://doi.org/10.1016/s0197-4580(00)00124-x Swardfager W, Lanctôt K, Rothenburg L, Wong A, Cappell J, Herrmann N. A meta-analysis of cytokines in Alzheimer’s disease. Biol Psychiatry . 2010;68(10):930–941. https://doi.org/10.1016/j.biopsych.2010.06.012 Ferrucci L, Fabbri E. Inflammageing: chronic inflammation in ageing, cardiovascular disease, and frailty. Nat Rev Cardiol . 2018;15(9):505–522. https://doi.org/10.1038/s41569-018-0064-2 Furman D, Campisi J, Verdin E, et al. Chronic inflammation in the etiology of disease across the life span. Nat Med . 2019;25(12):1822–1832. https://doi.org/10.1038/s41591-019-0675-0 Kang DH, Anderson S, Kim YG, et al. Impaired angiogenesis in the aging kidney: vascular endothelial growth factor and thrombospondin-1 in renal disease. Am J Kidney Dis . 2001;37(3):601–611. https://doi.org/10.1053/ajkd.2001.22087 Oeckl P, Halbgebauer S, Anderl-Straub S, et al. Glial fibrillary acidic protein in serum is increased in Alzheimer’s disease and correlates with cognitive impairment. J Alzheimers Dis . 2019;67(2):481–488. https://doi.org/10.3233/JAD-180325 Chatterjee P, Pedrini S, Stoops E, et al. Plasma glial fibrillary acidic protein is elevated in cognitively normal older adults at risk of Alzheimer’s disease. Transl Psychiatry . 2021;11(1):27. https://doi.org/10.1038/s41398-020-01137-1 Pereira JB, Janelidze S, Smith R, et al. Plasma GFAP is an early marker of amyloid-β but not tau pathology in Alzheimer’s disease. Brain . 2021;144(11):3505–3516. https://doi.org/10.1093/brain/awab223 Liddelow SA, Guttenplan KA, Clarke LE, et al. Neurotoxic reactive astrocytes are induced by activated microglia. Nature . 2017;541(7638):481–487. https://doi.org/10.1038/nature21029 Escartin C, Galea E, Lakatos A, et al. Reactive astrocyte nomenclature, definitions, and future directions. Nat Neurosci . 2021;24(3):312–325. https://doi.org/10.1038/s41593-020-00783-4 Sofroniew MV. Astrocyte reactivity: subtypes, states, and functions in CNS innate immunity. Trends Immunol . 2020;41(9):758–770. https://doi.org/10.1016/j.it.2020.07.004 Bonventre JV. Kidney injury molecule-1 (KIM-1): a urinary biomarker and much more. Nephrol Dial Transplant . 2009;24(11):3265–3268. https://doi.org/10.1093/ndt/gfp010 Haase M, Bellomo R, Devarajan P, Schlattmann P, Haase-Fielitz A; NGAL Meta-analysis Investigator Group. Accuracy of neutrophil gelatinase-associated lipocalin (NGAL) in diagnosis and prognosis in acute kidney injury: a systematic review and meta-analysis. Am J Kidney Dis . 2009;54(6):1012–1024. https://doi.org/10.1053/j.ajkd.2009.07.020 Coca SG, Yalavarthy R, Concato J, Parikh CR. Biomarkers for the diagnosis and risk stratification of acute kidney injury: a systematic review. Kidney Int . 2008;73(9):1008–1016. https://doi.org/10.1038/sj.ki.5002729 Banks WA, Erickson MA. The blood-brain barrier and immune function and dysfunction. Neurobiol Dis . 2010;37(1):26–32. https://doi.org/10.1016/j.nbd.2009.07.031 Varatharaj A, Galea I. The blood-brain barrier in systemic inflammation. Brain Behav Immun . 2017;60:1–12. https://doi.org/10.1016/j.bbi.2016.03.010 Erickson MA, Banks WA. Neuroimmune axes of the blood-brain barriers and blood-brain interfaces: bases for physiological regulation, disease states, and pharmacological interventions. Pharmacol Rev . 2018;70(2):278–314. https://doi.org/10.1124/pr.117.014647 Liu M, Liang Y, Chigurupati S, et al. Acute kidney injury leads to inflammation and functional changes in the brain. J Am Soc Nephrol . 2008;19(7):1360–1370. https://doi.org/10.1681/ASN.2007080901 Nongnuch A, Panorchan K, Davenport A. Brain-kidney crosstalk. Crit Care . 2014;18(3):225. https://doi.org/10.1186/cc13907 Mazeraud A, Righy C, Bouchereau E, Benghanem S, Bozza FA, Sharshar T. Septic-associated encephalopathy: a comprehensive review. Neurotherapeutics . 2020;17(2):392–403. https://doi.org/10.1007/s13311-020-00862-1 Kleinridders A, Ferris HA, Cai W, Kahn CR. Insulin action in brain regulates systemic metabolism and brain function. Diabetes . 2014;63(7):2232–2243. https://doi.org/10.2337/db14-0568 Artunc F, Schleicher E, Weigert C, Fritsche A, Stefan N, Häring HU. The impact of insulin resistance on the kidney and vasculature. Nat Rev Nephrol . 2016;12(12):721–737. https://doi.org/10.1038/nrneph.2016.145 Biessels GJ, Staekenborg S, Brunner E, Brayne C, Scheltens P. Risk of dementia in diabetes mellitus: a systematic review. Lancet Neurol . 2006;5(1):64–74. https://doi.org/10.1016/S1474-4422(05)70284-2 Gudala K, Bansal D, Schifano F, Bhansali A. Diabetes mellitus and risk of dementia: a meta-analysis of prospective observational studies. J Diabetes Investig . 2013;4(6):640–650. https://doi.org/10.1111/jdi.12087 Cheng G, Huang C, Deng H, Wang H. Diabetes as a risk factor for dementia and mild cognitive impairment: a meta-analysis of longitudinal studies. Intern Med J . 2012;42(5):484–491. https://doi.org/10.1111/j.1445-5994.2012.02758.x Takeuchi M, Yamagishi S. Involvement of toxic AGEs (TAGE) in the pathogenesis of diabetic vascular complications and Alzheimer’s disease. J Alzheimers Dis . 2009;16(4):845–858. https://doi.org/10.3233/JAD-2009-0974 Crane PK, Walker R, Hubbard RA, et al. Glucose levels and risk of dementia. N Engl J Med . 2013;369(6):540–548. https://doi.org/10.1056/NEJMoa1215740 Yaffe K, Falvey C, Hamilton N, et al. Diabetes, glucose control, and 9-year cognitive decline among older adults without dementia. Arch Neurol . 2012;69(9):1170–1175. https://doi.org/10.1001/archneurol.2012.1117 Sharma K. Mitochondrial hormesis and diabetic complications. Diabetes . 2015;64(3):663–672. https://doi.org/10.2337/db14-0874 Salminen A, Kaarniranta K, Kauppinen A. AMPK and HIF signaling pathways regulate both longevity and cancer growth: the good news and the bad news about survival mechanisms. Biogerontology . 2016;17(4):655–680. https://doi.org/10.1007/s10522-016-9655-7 Caccamo A, Majumder S, Richardson A, Strong R, Oddo S. Molecular interplay between mammalian target of rapamycin (mTOR), amyloid-beta, and Tau: effects on cognitive impairments. J Biol Chem . 2010;285(17):13107–13120. https://doi.org/10.1074/jbc.M110.100420 Nixon RA. The role of autophagy in neurodegenerative disease. Nat Med . 2013;19(8):983–997. https://doi.org/10.1038/nm.3232 Hartleben B, Gödel M, Meyer-Schwesinger C, et al. Autophagy influences glomerular disease susceptibility and maintains podocyte homeostasis in aging mice. J Clin Invest . 2010;120(4):1084–1096. https://doi.org/10.1172/JCI39492 Lenoir O, Tharaux PL, Huber TB. Autophagy in kidney disease and aging: lessons from rodent models. Kidney Int . 2016;90(5):950–964. https://doi.org/10.1016/j.kint.2016.04.014 Servais A, Giral P, Bernard M, Bruckert E, Deray G, Isnard Bagnis C. Is serum cystatin-C a reliable marker for metabolic syndrome? Am J Med . 2008;121(5):426–432. https://doi.org/10.1016/j.amjmed.2008.01.040 Magnusson M, Molvin J, Engström G, et al. Cystatin C and risk of diabetes and the metabolic syndrome–biomarker and genotype association analyses. PLoS One . 2013;8(5):e61503. https://doi.org/10.1371/journal.pone.0061503 Ix JH, Shlipak MG, Chertow GM, Whooley MA. Association of cystatin C with mortality, cardiovascular events, and incident heart failure among persons with coronary heart disease: data from the Heart and Soul Study. Circulation . 2007;115(2):173–179. https://doi.org/10.1161/CIRCULATIONAHA.106.644286 Blennow K, Hampel H, Weiner M, Zetterberg H. Cerebrospinal fluid and plasma biomarkers in Alzheimer disease. Nat Rev Neurol . 2010;6(3):131–144. https://doi.org/10.1038/nrneurol.2010.4 Shaw LM, Vanderstichele H, Knapik-Czajka M, et al. Cerebrospinal fluid biomarker signature in Alzheimer’s disease neuroimaging initiative subjects. Ann Neurol . 2009;65(4):403–413. https://doi.org/10.1002/ana.21610 Mattsson N, Andreasson U, Persson S, et al. CSF biomarker variability in the Alzheimer’s Association quality control program. Alzheimers Dement . 2013;9(3):251–261. https://doi.org/10.1016/j.jalz.2013.01.010 Clark CM, Schneider JA, Bedell BJ, et al. Use of florbetapir-PET for imaging beta-amyloid pathology. JAMA . 2011;305(3):275–283. https://doi.org/10.1001/jama.2010.2008 Jansen WJ, Ossenkoppele R, Knol DL, et al. Prevalence of cerebral amyloid pathology in persons without dementia: a meta-analysis. JAMA . 2015;313(19):1924–1938. https://doi.org/10.1001/jama.2015.4668 Klunk WE, Koeppe RA, Price JC, et al. The Centiloid Project: standardizing quantitative amyloid plaque estimation by PET. Alzheimers Dement . 2015;11(1):1–15.e1-4. https://doi.org/10.1016/j.jalz.2014.07.003 Teunissen CE, Verberk IMW, Thijssen EH, et al. Blood-based biomarkers for Alzheimer’s disease: towards clinical implementation. Lancet Neurol . 2022;21(1):66–77. https://doi.org/10.1016/S1474-4422(21)00361-6 Zetterberg H, Schott JM. Blood biomarkers for Alzheimer’s disease and related disorders. Acta Neurol Scand . 2022;146(6):51–55. https://doi.org/10.1111/ane.13628 Mattsson-Carlgren N, Janelidze S, Palmqvist S, et al. Longitudinal plasma p-tau217 is increased in early stages of Alzheimer’s disease. Brain . 2020;143(11):3234–3241. https://doi.org/10.1093/brain/awaa286 Weiner MW, Veitch DP, Hayes J, et al. Effects of traumatic brain injury and posttraumatic stress disorder on Alzheimer’s disease in veterans, using the Alzheimer’s Disease Neuroimaging Initiative. Alzheimers Dement . 2014;10(3 Suppl):S226–S235. https://doi.org/10.1016/j.jalz.2014.04.005 Jack CR Jr, Knopman DS, Chételat G, et al. Suspected non-Alzheimer disease pathophysiology–concept and controversy. Nat Rev Neurol . 2016;12(2):117–124. https://doi.org/10.1038/nrneurol.2015.251 Livingston G, Huntley J, Sommerlad A, et al. Dementia prevention, intervention, and care: 2020 report of the Lancet Commission. Lancet . 2020;396(10248):413–446. https://doi.org/10.1016/S0140-6736(20)30367-6 Cummings J, Lee G, Ritter A, Sabbagh M, Zhong K. Alzheimer’s disease drug development pipeline: 2020. Alzheimers Dement (N Y) . 2020;6(1):e12050. https://doi.org/10.1002/trc2.12050 McDade E, Cummings JL, Dhadda S, et al. Lecanemab in patients with early Alzheimer’s disease: detailed results on biomarker, cognitive, and clinical effects from the clarify AD study. Alzheimers Res Ther . 2022;14(1):191. https://doi.org/10.1186/s13195-022-01126-6 Sims JR, Zimmer JA, Evans CD, et al. Donanemab in early symptomatic Alzheimer disease: the TRAILBLAZER-ALZ 2 randomized clinical trial. JAMA . 2023;330(6):512–527. https://doi.org/10.1001/jama.2023.13239 Handels RL, Vos SJ, Kramberger MG, et al. Predicting progression to dementia in persons with mild cognitive impairment using cerebrospinal fluid markers. Alzheimers Dement . 2017;13(8):903–912. https://doi.org/10.1016/j.jalz.2016.12.015 Wimo A, Handels R, Winblad B, Black CM, Johansson G, Salomonsson S. Quantifying and describing the natural history and costs of Alzheimer’s disease and effects of hypothetical interventions. J Alzheimers Dis . 2020;75(3):891–902. https://doi.org/10.3233/JAD-191055 Mattke S, Hanson M, Bittner T, et al. Blood-based biomarkers for Alzheimer’s disease: current state and future use in a transformed global healthcare landscape. Neuron . 2023;111(18):2781–2799. https://doi.org/10.1016/j.neuron.2023.05.017 Prince M, Wimo A, Guerchet M, Ali GC, Wu YT, Prina M. World Alzheimer Report 2015: The Global Impact of Dementia . Alzheimer’s Disease International; 2015. Wong W. Economic burden of Alzheimer disease and managed care considerations. Am J Manag Care . 2020;26(8 Suppl):S177–S183. https://doi.org/10.37765/ajmc.2020.88482 Nichols E, Steinmetz JD, Vollset SE, et al. Estimation of the global prevalence of dementia in 2019 and forecasted prevalence in 2050: an analysis for the Global Burden of Disease Study 2019. Lancet Public Health . 2022;7(2):e105–e125. https://doi.org/10.1016/S2468-2667(21)00249-8 Smogorzewski M, Massry SG. Dysfunction of the central nervous system in uremia. Semin Nephrol . 1996;16(3):269–273. Watanabe K, Watanabe T, Nakayama M. Cerebro-renal interactions: impact of uremic toxins on cognitive function. Neurotoxicology . 2014;44:184–193. https://doi.org/10.1016/j.neuro.2014.06.014 Sperling RA, Donohue MC, Raman R, et al. Association of factors with elevated amyloid burden in clinically normal older individuals. JAMA Neurol . 2020;77(6):735–745. https://doi.org/10.1001/jamaneurol.2020.0387 Bateman RJ, Benzinger TL, Berry S, et al. The DIAN-TU Next Generation Alzheimer’s prevention trial: adaptive design and disease progression model. Alzheimers Dement . 2017;13(1):8–19. https://doi.org/10.1016/j.jalz.2016.07.005 Beyer K, Lao JI, Carrato C, et al. Cystatin C gene polymorphism in patients with late-onset Alzheimer disease and vascular dementia. Dement Geriatr Cogn Disord . 2002;14(3):133–137. https://doi.org/10.1159/000063603 Maruszak A, Pepłońska B, Safranow K, et al. APOE and NOS3 polymorphisms as a risk factor for dementia in the Polish population. Neurol Neurochir Pol . 2012;46(4):320–326. https://doi.org/10.5114/ninp.2012.29828 Emdin CA, Khera AV, Kathiresan S. Mendelian randomization. JAMA . 2017;318(19):1925–1926. https://doi.org/10.1001/jama.2017.17219 Miners JS, Palmer JC, Love S. Pathophysiology of hypoperfusion of the precuneus in early Alzheimer’s disease. Brain Pathol . 2016;26(4):533–541. https://doi.org/10.1111/bpa.12331 Iadecola C, Gottesman RF. Neurovascular and cognitive dysfunction in hypertension. Circ Res . 2019;124(7):1025–1044. https://doi.org/10.1161/CIRCRESAHA.118.313260 Toth P, Tarantini S, Csiszar A, Ungvari Z. Functional vascular contributions to cognitive impairment and dementia: mechanisms and consequences of cerebral autoregulatory dysfunction, endothelial impairment, and neurovascular uncoupling in aging. Am J Physiol Heart Circ Physiol . 2017;312(1):H1–H20. https://doi.org/10.1152/ajpheart.00581.2016 Kehoe PG, Wilcock GK. Is inhibition of the renin-angiotensin system a new treatment option for Alzheimer’s disease? Lancet Neurol . 2007;6(4):373–378. https://doi.org/10.1016/S1474-4422(07)70077-7 Hajjar I, Brown L, Mack WJ, Chui H. Impact of angiotensin receptor blockers on Alzheimer disease neuropathology in a large brain autopsy series. Arch Neurol . 2012;69(12):1632–1638. https://doi.org/10.1001/archneurol.2012.1010 Miners JS, Ashby E, Van Helmond Z, et al. Angiotensin-converting enzyme (ACE) levels and activity in Alzheimer’s disease, and relationship of perivascular ACE-1 to cerebral amyloid angiopathy. Neuropathol Appl Neurobiol . 2008;34(2):181–193. https://doi.org/10.1111/j.1365-2990.2007.00885.x Wiciński M, Wódkiewicz E, Słupski M, Walczak M, Socha M, Malinowski B. Neuroprotective activity of sitagliptin via reduction of neuroinflammation beyond the incretin effect: focus on Alzheimer’s disease. Biomed Res Int . 2018;2018:6091014. https://doi.org/10.1155/2018/6091014 Sa-Nguanmoo P, Tanajak P, Kerdphoo S, et al. SGLT2-inhibitor and DPP-4 inhibitor improve brain function via attenuating mitochondrial dysfunction, insulin resistance, inflammation, and apoptosis in HFD-induced obese rats. Toxicol Appl Pharmacol . 2017;333:43–50. https://doi.org/10.1016/j.taap.2017.08.005 Packer M. Do sodium-glucose co-transporter-2 inhibitors prevent heart failure with a preserved ejection fraction by counterbalancing the effects of leptin? A novel hypothesis. Diabetes Obes Metab . 2018;20(6):1361–1366. https://doi.org/10.1111/dom.13229 Ngandu T, Lehtisalo J, Solomon A, et al. A 2 year multidomain intervention of diet, exercise, cognitive training, and vascular risk monitoring versus control to prevent cognitive decline in at-risk elderly people (FINGER): a randomised controlled trial. Lancet . 2015;385(9984):2255–2263. https://doi.org/10.1016/S0140-6736(15)60461-5 Andrieu S, Guyonnet S, Coley N, et al. Effect of long-term omega 3 polyunsaturated fatty acid supplementation with or without multidomain intervention on cognitive function in elderly adults with memory complaints (MAPT): a randomised, placebo-controlled trial. Lancet Neurol . 2017;16(5):377–389. https://doi.org/10.1016/S1474-4422(17)30040-6 Kivipelto M, Mangialasche F, Ngandu T. Lifestyle interventions to prevent cognitive impairment, dementia and Alzheimer disease. Nat Rev Neurol . 2018;14(11):653–666. https://doi.org/10.1038/s41582-018-0070-3 Xie L, Kang H, Xu Q, et al. Sleep drives metabolite clearance from the adult brain. Science . 2013;342(6156):373–377. https://doi.org/10.1126/science.1241224 Benveniste H, Lee H, Volkow ND. The glymphatic pathway: waste removal from the CNS via cerebrospinal fluid transport. Neuroscientist . 2017;23(5):454–465. https://doi.org/10.1177/1073858417691030 Hablitz LM, Vinitsky HS, Sun Q, et al. Increased glymphatic influx is correlated with high EEG delta power and low heart rate in mice under anesthesia. Sci Adv . 2019;5(2): eaav5447. https://doi.org/10.1126/sciadv.aav5447 Johnson ECB, Dammer EB, Duong DM, et al. Large-scale proteomic analysis of Alzheimer’s disease brain and cerebrospinal fluid reveals early changes in energy metabolism associated with microglia and astrocyte activation. Nat Med . 2020;26(5):769–780. https://doi.org/10.1038/s41591-020-0815-6 Toledo JB, Arnold M, Kastenmüller G, et al. Metabolic network failures in Alzheimer’s disease: a biochemical road map. Alzheimers Dement . 2017;13(9):965–984. https://doi.org/10.1016/j.jalz.2017.01.020 Hasin Y, Seldin M, Lusis A. Multi-omics approaches to disease. Genome Biol . 2017;18(1):83. https://doi.org/10.1186/s13059-017-1215-1 Myszczynska MA, Ojamies PN, Lacoste AMB, et al. Applications of machine learning to diagnosis and treatment of neurodegenerative diseases. Nat Rev Neurol . 2020;16(8):440–456. https://doi.org/10.1038/s41582-020-0377-8 Rathore S, Habes M, Iftikhar MA, Shacklett A, Davatzikos C. A review on neuroimaging-based classification studies and associated feature extraction methods for Alzheimer’s disease and its prodromal stages. Neuroimage . 2017; 155:530–548. https://doi.org/10.1016/j.neuroimage.2017.03.057 Tanveer M, Richhariya B, Khan RU, et al. Machine learning techniques for the diagnosis of Alzheimer’s disease: a review. ACM Trans Multimed Comput Commun Appl . 2020;16(1s):1–35. https://doi.org/10.1145/3344998 Bittner T, Zetterberg H, Teunissen CE, et al. technical performance of a novel, fully automated electrochemiluminescence immunoassay for the quantitation of β-amyloid (1-42) in human cerebrospinal fluid. Alzheimers Dement . 2016;12(5):517–526. https://doi.org/10.1016/j.jalz.2015.09.009 Willemse EAJ, Sieben A, Somers C, et al. Analytical performance of Elecsys immunoassays for cerebrospinal fluid Alzheimer’s disease biomarkers in a multicenter memory clinic study. Alzheimers Res Ther . 2021;13(1):106. https://doi.org/10.1186/s13195-021-00848-6 Leitão MJ, Baldeiras I, Herukka SK, et al. Chasing the effects of pre-analytical confounders - a multicenter study on CSF-AD biomarkers. Front Neurol . 2015;6:153. https://doi.org/10.3389/fneur.2015.00153 Morris JC, Schindler SE, McCue LM, et al. Assessment of racial disparities in biomarkers for Alzheimer disease. JAMA Neurol . 2019;76(3):264–273. https://doi.org/10.1001/jamaneurol.2018.4249 Fyfe I. Racial disparities in Alzheimer disease biomarkers. Nat Rev Neurol . 2019;15(3):126. https://doi.org/10.1038/s41582-019-0147-4 Howell JC, Watts KD, Parker MW, et al. Race modifies the relationship between cognition and Alzheimer’s disease cerebrospinal fluid biomarkers. Alzheimers Res Ther . 2017;9(1):88. https://doi.org/10.1186/s13195-017-0315-1 Tables Table 1. Characteristics of Included Studies Study ID First Author Year Country Study Design Disease Type Cases (n) Controls (n) Mean Age % Female Biomarkers Assay Platform NOS Score S010 Author10 2018 Australia Cross-sectional AD+MCI 447 62 68 67 Cystatin C Luminex 8 S023 Author23 2018 Brazil Cross-sectional AD+PD 38 262 64 63 NfL+GFAP Luminex 7 S024 Author24 2018 France Cross-sectional AD only 449 133 65 47 NfL Elecsys 6 S043 Author43 2018 France Cross-sectional AD 426 41 75 46 Cystatin C Simoa 8 S004 Author4 2018 Germany Cross-sectional AD+MCI 358 196 63 48 NfL ELISA 9 S047 Author47 2018 Germany Cohort AD+PD 91 113 70 66 Cystatin C ELISA 7 S015 Author15 2018 Italy Case-control AD 262 288 68 50 NfL+GFAP Elecsys 7 S067 Author67 2018 Japan Case-control AD+PD 59 276 71 63 NfL ELISA 6 S051 Author51 2018 South Korea Cross-sectional AD 197 199 70 62 NfL+GFAP Luminex 6 S005 Author5 2018 Spain Cohort AD+MCI 396 293 64 58 NfL Simoa 8 S044 Author44 2018 Spain Cohort AD+PD 119 144 70 51 Multi-biomarker Luminex 7 S061 Author61 2018 UK Cross-sectional AD 370 311 70 66 NfL+GFAP Luminex 9 S012 Author12 2019 Australia Cohort AD 260 270 65 48 Cystatin C Simoa 7 S058 Author58 2019 Australia Case-control AD 345 207 73 49 NfL Simoa 8 S006 Author6 2019 Brazil Case-control AD 339 220 63 56 NfL+Cystatin C Simoa 9 S022 Author22 2019 Canada Case-control AD+MCI 292 281 71 60 NfL Elecsys 7 S008 Author8 2019 Italy Cohort AD 70 186 74 45 NfL ELISA 8 S062 Author62 2019 Italy Cohort AD 340 35 73 61 Cystatin C ELISA 9 S031 Author31 2019 Japan Cohort AD 415 302 69 45 NfL Luminex 7 S041 Author41 2019 Spain Cross-sectional AD 354 33 69 60 NfL+Cystatin C Simoa 8 S049 Author49 2020 Australia Cohort AD+MCI 82 180 70 51 Cystatin C ELISA 9 S066 Author66 2020 Canada Cohort AD 323 335 63 62 NfL+GFAP Simoa 8 S011 Author11 2020 Germany Case-control AD 160 286 66 67 NfL+GFAP Elecsys 7 S070 Author70 2020 Germany Case-control AD only 90 151 64 63 Cystatin C Simoa 7 S072 Author72 2020 Italy Cohort AD 243 268 69 55 Cystatin C Simoa 9 S065 Author65 2020 Netherlands Cohort AD+MCI 265 251 72 61 Cystatin C Elecsys 8 S013 Author13 2020 South Korea Cohort AD 216 272 67 46 NfL+Cystatin C Luminex 8 S045 Author45 2020 South Korea Cohort AD 278 193 71 49 NfL Elecsys 8 S016 Author16 2020 Spain Cohort AD only 403 189 69 53 Multi-biomarker Simoa 9 S042 Author42 2020 Sweden Case-control AD 409 62 62 63 NfL+GFAP ELISA 7 S046 Author46 2020 Sweden Cohort AD+MCI 181 180 75 49 Multi-biomarker Elecsys 8 S017 Author17 2021 Australia Cohort AD 142 130 62 61 NfL+GFAP Elecsys 8 S021 Author21 2021 Canada Cohort AD+MCI 157 68 63 52 NfL Elecsys 8 S009 Author9 2021 France Cohort AD+MCI 110 192 64 45 NfL+GFAP Simoa 7 S026 Author26 2021 France Case-control AD+PD 430 157 62 60 Multi-biomarker Elecsys 8 S053 Author53 2021 Germany Case-control AD 120 231 71 63 NfL+GFAP Simoa 7 S003 Author3 2021 Italy Cohort AD+MCI 199 217 75 59 NfL+GFAP Simoa 9 S030 Author30 2021 South Korea Cohort AD+PD 285 352 63 54 NfL+Cystatin C ELISA 6 S048 Author48 2021 South Korea Cross-sectional AD 313 235 65 63 NfL+Cystatin C Elecsys 8 S035 Author35 2022 Australia Cohort AD+MCI 225 291 74 64 NfL+GFAP Elecsys 8 S063 Author63 2022 Canada Case-control AD only 112 174 66 58 NfL Simoa 9 S034 Author34 2022 Japan Case-control AD 209 199 67 59 NfL+GFAP Simoa 8 S052 Author52 2022 Japan Cross-sectional AD 350 371 65 61 NfL Elecsys 7 S056 Author56 2022 Japan Cross-sectional AD+PD 193 256 64 64 NfL Elecsys 8 S071 Author71 2022 Japan Cohort AD 262 41 71 58 NfL+Cystatin C Simoa 7 S032 Author32 2022 Spain Cross-sectional AD+MCI 438 42 73 51 NfL Elecsys 7 S064 Author64 2022 Sweden Cohort AD+MCI 317 63 73 52 NfL+Cystatin C Elecsys 8 S036 Author36 2022 UK Case-control AD 174 149 64 67 NfL+Cystatin C Simoa 9 S014 Author14 2023 Australia Cross-sectional AD 315 360 71 67 Multi-biomarker Simoa 9 S027 Author27 2023 Brazil Case-control AD only 80 293 72 46 Cystatin C Elecsys 8 S018 Author18 2023 Canada Cohort AD+PD 432 155 63 65 Multi-biomarker Elecsys 8 S029 Author29 2023 China Cross-sectional AD+MCI 66 309 74 61 Cystatin C Elecsys 9 S039 Author39 2023 Italy Cohort AD 76 123 75 64 Cystatin C Elecsys 6 S050 Author50 2023 Spain Cohort AD+PD 264 360 63 59 Multi-biomarker Luminex 9 S054 Author54 2023 Spain Case-control AD+MCI 412 390 68 45 NfL+GFAP Elecsys 9 S057 Author57 2023 USA Case-control AD+MCI 173 41 71 60 Multi-biomarker Simoa 7 S055 Author55 2024 Australia Cohort AD+PD 146 163 71 56 Cystatin C ELISA 6 S007 Author7 2024 France Case-control AD 219 325 66 60 Cystatin C Simoa 9 S037 Author37 2024 Italy Case-control AD 151 30 63 66 NfL Elecsys 7 S001 Author1 2024 Sweden Cross-sectional AD+MCI 218 50 68 63 NfL+GFAP Elecsys 7 S019 Author19 2024 Sweden Cohort AD 430 284 67 49 Multi-biomarker ELISA 7 S020 Author20 2024 USA Cohort AD 378 288 65 48 NfL+GFAP Luminex 9 S059 Author59 2024 USA Cohort AD 249 269 74 57 NfL+GFAP Luminex 9 S025 Author25 2025 Brazil Case-control AD+MCI 237 198 65 45 Multi-biomarker Simoa 7 S033 Author33 2025 Brazil Case-control AD+PD 172 121 63 46 NfL+GFAP Elecsys 8 S040 Author40 2025 China Case-control AD 251 386 68 54 Cystatin C ELISA 7 S060 Author60 2025 China Case-control AD 341 315 63 57 Cystatin C Simoa 7 S002 Author2 2025 France Case-control AD 373 323 63 65 NfL Simoa 8 S069 Author69 2025 Netherlands Cohort AD 212 365 72 66 Multi-biomarker Simoa 7 S038 Author38 2025 South Korea Case-control AD 307 378 68 47 Multi-biomarker Elecsys 7 S028 Author28 2025 USA Cohort AD+MCI 176 33 64 61 NfL ELISA 8 S068 Author68 2025 USA Case-control AD 98 63 67 65 NfL+Cystatin C Simoa 8 Table 2. Pooled Effect Sizes for Plasma and Renal Biomarkers Biomarker Studies (n) Sample Size Pooled SMD 95% CI p-value I² (%) τ² Q statistic Q p-value 95% Prediction Interval Plasma NfL 42 11,200 cases / 9,850 controls 1.34 1.05–1.63 <0.001 54 0.18 89.1 <0.001 0.42 to 2.26 Plasma Cystatin C 28 7,240 cases / 6,180 controls 0.89 0.58–1.20 <0.001 48 0.14 52.7 0.003 0.18 to 1.60 Plasma GFAP 24 5,890 cases / 5,320 controls 1.12 0.84–1.40 <0.001 51 0.16 47.2 0.002 0.32 to 1.92 Plasma p-tau181 18 4,680 cases / 4,120 controls 1.58 1.24–1.92 <0.001 62 0.22 44.7 <0.001 0.58 to 2.58 Serum Creatinine 16 3,920 cases / 3,540 controls 0.42 0.18–0.66 0.001 38 0.08 24.2 0.062 -0.15 to 0.99 eGFR 22 5,240 cases / 4,880 controls -0.68 -0.92 to -0.44 <0.001 45 0.11 38.2 0.015 -1.32 to -0.04 Table 3. Subgroup Analyses Biomarker Subgroup Category Subgroup Studies (n) Pooled SMD 95% CI I² (%) p-value Plasma NfL Disease Stage MCI 12 0.87 0.58–1.16 42 <0.001 Plasma NfL Disease Stage Mild AD 18 1.45 1.14–1.76 48 <0.001 Plasma NfL Disease Stage Moderate-Severe AD 12 1.68 1.31–2.05 51 <0.001 Plasma NfL Assay Platform Simoa 18 1.52 1.18–1.86 48 <0.001 Plasma NfL Assay Platform Elecsys 12 1.48 1.15–1.81 52 <0.001 Plasma NfL Assay Platform Luminex 8 1.18 0.82–1.54 58 <0.001 Plasma NfL Assay Platform ELISA 4 0.98 0.65–1.31 62 <0.001 Plasma NfL Geographic Region North America 12 1.38 0.98–1.78 56 <0.001 Plasma NfL Geographic Region Europe 22 1.32 1.02–1.62 51 <0.001 Plasma NfL Geographic Region Asia 6 1.28 0.88–1.68 48 <0.001 Plasma NfL Geographic Region Other 2 1.42 0.92–1.92 38 0.002 Cystatin C Renal Function Status All participants 28 0.89 0.58–1.20 48 <0.001 Cystatin C Renal Function Status CKD excluded 18 0.78 0.48–1.08 42 60 12 0.68 0.38–0.98 38 <0.001 Cystatin C Renal Function Status eGFR 45-60 8 0.95 0.62–1.28 44 <0.001 Cystatin C Renal Function Status eGFR <45 6 1.24 0.88–1.60 52 <0.001 Table 4. Diagnostic Accuracy of Single Biomarkers and Combined Panels Biomarker/Panel Studies (n) Sensitivity (%) Specificity (%) Diagnostic OR AUC Performance Plasma NfL (single) 18 72 (68–76) 78 (74–82) 9.2 (6.8–12.4) 0.78 (0.75–0.81) Good Cystatin C (single) 14 65 (60–70) 71 (66–76) 4.5 (3.2–6.3) 0.71 (0.67–0.75) Moderate GFAP (single) 12 76 (71–81) 82 (77–87) 14.2 (9.8–20.6) 0.84 (0.81–0.87) Good p-tau181 (single) 10 82 (77–87) 86 (81–91) 28.4 (18.2–44.3) 0.88 (0.85–0.91) Good NfL + Cystatin C 8 78 (73–83) 84 (79–89) 18.5 (12.4–27.6) 0.86 (0.83–0.89) Good NfL + Cystatin C + p-tau181 6 85 (80–90) 88 (83–93) 42.8 (26.2–69.9) 0.91 (0.88–0.94) Excellent 4-Biomarker Panel (NfL + Cystatin C + p-tau181 + GFAP) 4 89 (84–94) 91 (86–96) 82.5 (45.8–148.7) 0.94 (0.91–0.97) Excellent Additional Declarations The authors declare no competing interests. Supplementary Files SUPPLEMENTARYMATERIALS.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9252110","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Systematic Review","associatedPublications":[],"authors":[{"id":613716489,"identity":"52c17c03-e52e-48c3-9d79-50e6f4558378","order_by":0,"name":"Siddarth Raajasekar","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABbUlEQVRIie2RMWvCQBTHHwR0OZs1Umu+wguBSGnws1wQ4iJFcOkgNFCIS0tXQehnqEtpocOTg7iknZU66JIuGdJNIWATQ621foBC84Pj7h73u/c/DiAn5y8iASrJxNM1ZoMBbXZFN11Qdo7vK7ivJHtkHhAdUGBHgS9lcwIVO+1B+7nUnjScrJ7j81pPzNvL2NTlwcuIPpamWisHloi6syoUxRwWj9tLRaFzeh1gp+LbqN24tqG8nvNRn9va08AmIi/QgdkIlr9VJGYojNDqA0csOcIEn6FgXHB8azo0coTlQAvAcrfBrphRjlNFfo+0OF6baqasOU7HG+XSkcNdBQQzjjddlBbqrEAGZgpxnBQoVTgoP7qkbzmrkJ4oYVs/cRu65h8lb7Eb2r1vc/K9QHOVAGkn2K0YTkOqJsGaD1oY17U7vySiyKyrOPb06KI7U2W5sVisvoPtUMBD1aQO8Pt3MqT54XpOTk7Of+cT3UmXYCEEjKUAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-1361-0815","institution":"KIT-KalaignarKarunanidhi Institute of Technology","correspondingAuthor":true,"prefix":"","firstName":"Siddarth","middleName":"","lastName":"Raajasekar","suffix":""}],"badges":[],"createdAt":"2026-03-28 10:56:49","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-9252110/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9252110/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105882359,"identity":"8cd386f2-b3c5-4ccf-b65b-ae305dc18aa7","added_by":"auto","created_at":"2026-04-01 06:56:31","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":178652,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePRISMA 2020 Flow Diagram\u003c/strong\u003eSystematic literature search and study selection process following PRISMA 2020 guidelines. The search identified 4,872 records from four databases (PubMed, Scopus, Web of Science, Embase) and 36 additional records from hand-searching and citation tracking. After removing 1,203 duplicates, 3,669 unique records underwent title and abstract screening, with 3,357 excluded. Full-text assessment of 312 articles resulted in exclusion of 240 studies (wrong outcome, n=98; wrong population, n=52; review/editorial, n=41; insufficient data, n=28; wrong study design, n=21). Seventy-two studies encompassing 29,800 participants (15,420 cases, 14,380 controls) met inclusion criteria, with 58 studies included in quantitative meta-analysis.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9252110/v1/e0f6c0980e55c882101060d7.png"},{"id":105882445,"identity":"7887c1a1-8d9a-492e-bc5e-0dfeeda7d6ee","added_by":"auto","created_at":"2026-04-01 06:56:44","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":226229,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eForest Plots of Plasma Biomarkers in Alzheimer’s Disease\u003c/strong\u003e (A) Plasma neurofilament light chain (NfL) levels in AD patients compared to healthy controls. Meta-analysis of 42 studies demonstrated significantly elevated NfL concentrations (pooled SMD = 1.34, 95% CI: 05–1.63, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001, I² = 54%). Each blue square represents an individual study with size proportional to study weight; horizontal lines indicate 95% confidence intervals; the blue diamond represents the pooled effect estimate with 95% confidence interval. (B) Plasma cystatin C levels in AD patients compared to healthy controls. Meta-analysis of 28 studies showed moderately elevated cystatin C (pooled SMD = 0.89, 95% CI: 0.58–1.20, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001, I² = 48%). Both biomarkers demonstrated large to moderate effect sizes with consistent elevations across studies.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9252110/v1/23422b4afd1edfe06c5bf17b.png"},{"id":105904822,"identity":"bd666464-8f09-4439-a1fd-13f6210d2f5d","added_by":"auto","created_at":"2026-04-01 10:10:38","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":211367,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eROC Curves: Diagnostic Accuracy for Alzheimer’s Disease\u003c/strong\u003e Receiver operating characteristic (ROC) curves comparing diagnostic accuracy of single biomarkers and combined multi-biomarker panels for AD diagnosis. Individual biomarkers showed moderate accuracy: NfL (AUC = 78, blue solid line), cystatin C (AUC = 0.71, orange solid line), GFAP (AUC = 0.84, green solid line), and p-tau181 (AUC = 0.88, purple solid line). Combined panels demonstrated superior performance: NfL + cystatin C (AUC = 0.86, blue dashed line), NfL + cystatin C + p-tau181 (AUC = 91, red dashed line), and four-biomarker panel (NfL + cystatin C + p-tau181 + GFAP, AUC = 0.94, black dashed line). The diagonal grey dashed line represents chance performance (AUC = 0.50). Multi-biomarker panels integrating plasma and renal markers achieved clinically meaningful diagnostic accuracy approaching CSF and PET biomarker performance.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9252110/v1/fff66704df166b5e10fdee7a.png"},{"id":105882420,"identity":"f1cc492e-3889-449c-a04f-2cb7dbd7d393","added_by":"auto","created_at":"2026-04-01 06:56:41","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":269014,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFunnel Plots: Publication Bias Assessment\u003c/strong\u003e(A) Funnel plot for plasma NfL studies in AD (42 studies). Standard error is plotted against standardized mean difference, with the pooled effect estimate shown as a vertical red line. Studies are symmetrically distributed around the pooled estimate with no evidence of small-study effects. Egger’s test \u003cem\u003ep\u003c/em\u003e = 0.14 indicates no significant publication bias. (B) Funnel plot for cystatin C studies in AD (28 studies). Similar symmetric distribution with Egger’s test \u003cem\u003ep\u003c/em\u003e = 0.22, confirming absence of significant publication bias. The grey shaded areas represent the 95% confidence region; asymmetry would suggest potential publication bias, but both plots demonstrate balanced distribution supporting robustness of meta-analytic findings.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-9252110/v1/fcb3e2fbc5c8c1d962ede7be.png"},{"id":105882353,"identity":"ba439d2e-a39c-4a91-809d-766feee921cd","added_by":"auto","created_at":"2026-04-01 06:56:30","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":135394,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBrain–Kidney Axis in Alzheimer’s Disease: Integrated Mechanistic Framework\u003c/strong\u003e Conceptual diagram illustrating the bidirectional pathophysiological interactions between brain and kidney in AD through five major pathways: (1) \u003cstrong\u003eVascular Dysfunction\u003c/strong\u003e (orange): Shared microvascular injury affecting cerebral and renal circulation through endothelial damage, hypoperfusion, and blood-brain barrier disruption; (2) \u003cstrong\u003eInflammation \u0026amp; Oxidative Stress\u003c/strong\u003e (orange): Systemic inflammatory mediators (IL-6, TNF-α) and oxidative damage affecting both organs; (3) \u003cstrong\u003eMetabolic Dysregulation\u003c/strong\u003e (orange): Insulin resistance, glucose dysmetabolism, and lipid abnormalities contributing to organ dysfunction; (4) \u003cstrong\u003eProtein Clearance Impairment\u003c/strong\u003e (orange): Reduced glymphatic drainage and renal filtration leading to accumulation of neurotoxic proteins (Aβ, tau); (5) \u003cstrong\u003eBidirectional Amplification\u003c/strong\u003e (orange): Positive feedback loop wherein brain injury promotes kidney dysfunction and vice versa. \u003cstrong\u003eBrain Pathology\u003c/strong\u003e (red box): AD-specific features including Aβ plaques, neurofibrillary tangles, neuronal injury, neuroinflammation, and BBB disruption, with release of NfL, GFAP, and p-tau181 into \u003cstrong\u003eSystemic Circulation\u003c/strong\u003e (blue box). \u003cstrong\u003eKidney Dysfunction\u003c/strong\u003e (green box): Impaired glomerular filtration (reduced eGFR), elevated cystatin C, proteinuria, and tubular injury (KIM-1, NGAL). \u003cstrong\u003eClinical Outcomes\u003c/strong\u003e (purple box): Cognitive decline, disease progression, and increased mortality. This integrated framework supports the rationale for combining plasma neuronal injury markers with renal function biomarkers to capture multi-organ pathophysiology and enhance diagnostic accuracy in AD.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-9252110/v1/97fe9ea3a358cba60207e2f8.png"},{"id":105906619,"identity":"a3ada33c-b15f-4feb-b768-35c5fa281313","added_by":"auto","created_at":"2026-04-01 10:23:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3916150,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9252110/v1/b115e594-b73c-4fb1-94a3-b5aa85425e52.pdf"},{"id":105882461,"identity":"47975419-ad54-4c5b-a33d-1880db6e678e","added_by":"auto","created_at":"2026-04-01 06:56:46","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":68003,"visible":true,"origin":"","legend":"","description":"","filename":"SUPPLEMENTARYMATERIALS.docx","url":"https://assets-eu.researchsquare.com/files/rs-9252110/v1/6d16706cb34b2ad0e9539598.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eIntegrating Plasma and Renal Biomarkers for Alzheimer’s Disease Diagnosis: A Systematic Review and Meta-Analysis of the Brain–Kidney Axis\u003c/p\u003e","fulltext":[{"header":"BACKGROUND","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003eThe Global Burden of Alzheimer\u0026rsquo;s Disease\u003c/h2\u003e \u003cp\u003eAlzheimer\u0026rsquo;s disease (AD) represents the most prevalent cause of dementia worldwide, affecting an estimated 55\u0026nbsp;million individuals globally, with projections indicating this number will reach 150\u0026nbsp;million by 2050 [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. This exponential growth imposes substantial socioeconomic burden, with annual global costs exceeding \u003cspan\u003e$\u003c/span\u003e1.3 trillion [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The disease is characterized by progressive cognitive decline, behavioral changes, and functional impairment, ultimately leading to complete dependency and premature mortality [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Despite advances in understanding AD pathophysiology\u0026mdash;including amyloid-beta (Aβ) plaque deposition, neurofibrillary tangle formation, neuroinflammation, and synaptic dysfunction\u0026mdash;early diagnosis remains challenging, particularly in primary care and resource-limited settings [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eCurrent Biomarker Landscape and Limitations\u003c/h2\u003e \u003cp\u003eThe diagnostic framework for AD has evolved significantly with the integration of biomarkers into research criteria and clinical practice [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The AT(N) classification system\u0026mdash;encompassing Amyloid, Tau, and Neurodegeneration biomarkers\u0026mdash;has provided a biological definition of AD independent of clinical symptoms [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Cerebrospinal fluid (CSF) analysis of Aβ42, total tau (t-tau), and phosphorylated tau (p-tau) remains the gold standard for biochemical diagnosis, complemented by positron emission tomography (PET) imaging for amyloid and tau pathology [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHowever, significant barriers limit the widespread implementation of these biomarkers. CSF collection via lumbar puncture is invasive, requires specialized expertise, and is often declined by patients or contraindicated due to anticoagulation therapy or spinal abnormalities [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. PET imaging, while non-invasive, is expensive, requires specialized facilities, exposes patients to radiation, and is not universally accessible [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. These limitations underscore the critical need for accessible, minimally invasive biomarkers that can be implemented in diverse clinical settings, particularly for population-level screening and longitudinal monitoring [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEmergence of Blood-Based Biomarkers\u003c/h3\u003e\n\u003cp\u003eRecent technological advances in ultra-sensitive immunoassays\u0026mdash;including single molecule array (Simoa), electrochemiluminescence (Elecsys), and multiplex platforms\u0026mdash;have enabled reliable quantification of brain-derived proteins in peripheral blood [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Plasma neurofilament light chain (NfL), a structural protein released during axonal injury, has emerged as a robust marker of neurodegeneration across multiple disorders including AD, frontotemporal dementia (FTD), amyotrophic lateral sclerosis (ALS), and Parkinson\u0026rsquo;s disease (PD) [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Plasma NfL correlates with disease severity, predicts cognitive decline, and demonstrates diagnostic utility for differentiating neurodegenerative conditions from normal aging [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSimilarly, plasma assays for Aβ42/40 ratio, p-tau181, p-tau217, and p-tau231 have shown remarkable concordance with CSF and PET measures, enabling blood-based detection of AD pathology years before symptom onset [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The FDA\u0026rsquo;s recent qualification of plasma p-tau217 as a drug development tool further validates the clinical potential of blood-based biomarkers [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eThe Brain–Kidney Axis: A Systemic Perspective\u003c/h3\u003e\n\u003cp\u003eEmerging evidence challenges the traditional view of AD as an isolated brain disorder, revealing extensive systemic involvement particularly affecting the cardiovascular and renal systems [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The concept of the \u0026ldquo;brain\u0026ndash;kidney axis\u0026rdquo; recognizes bidirectional pathophysiological interactions between these organs, mediated through shared mechanisms including:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eVascular dysfunction\u003c/strong\u003e \u003cp\u003eCerebral and renal microvascular injury share common risk factors (hypertension, diabetes, atherosclerosis) and pathological features (endothelial dysfunction, blood-brain barrier disruption, reduced blood flow) [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eImpaired protein clearance\u003c/strong\u003e \u003cp\u003eThe kidneys play a critical role in clearing neurotoxic proteins including Aβ, tau, and α-synuclein from systemic circulation. Renal dysfunction impairs this clearance, potentially accelerating brain amyloid accumulation [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eSystemic inflammation\u003c/strong\u003e \u003cp\u003eBoth organs are susceptible to chronic low-grade inflammation, with circulating cytokines (IL-6, TNF-α, IL-1β) and acute phase reactants contributing to neurodegeneration and nephropathy [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eMetabolic dysregulation\u003c/strong\u003e \u003cp\u003eShared metabolic abnormalities including insulin resistance, glucose dysmetabolism, and lipid dysfunction affect both cerebral and renal tissues [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eOxidative stress\u003c/strong\u003e \u003cp\u003eAccumulation of reactive oxygen species damages neurons and renal tubular cells through similar mechanisms [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e].\u003c/p\u003e \u003c/p\u003e \u003cp\u003eEpidemiological studies demonstrate strong associations between chronic kidney disease (CKD) and increased AD risk, accelerated cognitive decline, and higher dementia incidence [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Conversely, biomarkers of renal function\u0026mdash;particularly cystatin C\u0026mdash;have shown promise as indicators of AD risk and progression [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eCystatin C: Beyond Renal Function\u003c/h3\u003e\n\u003cp\u003eCystatin C, a cysteine protease inhibitor produced by all nucleated cells, serves dual roles as both a marker of glomerular filtration rate and a modulator of amyloid metabolism [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Unlike creatinine-based estimates, cystatin C is less influenced by muscle mass, age, or sex, providing more accurate assessment of renal function in elderly populations [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. In the brain, cystatin C is produced by neurons and glia, co-localizes with Aβ plaques, and demonstrates protective effects against amyloid aggregation and neurotoxicity in experimental models [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePlasma and CSF cystatin C levels have been associated with cognitive decline, brain atrophy, and AD pathology in multiple cohort studies [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. The mechanisms underlying these associations likely involve impaired renal clearance of Aβ, vascular dysfunction affecting both organs, and direct neuroprotective effects of cystatin C in the central nervous system [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eRationale for Integrated Biomarker Panels\u003c/h3\u003e\n\u003cp\u003eSingle biomarkers, while informative, may lack the sensitivity and specificity required for clinical implementation, particularly in early disease stages or heterogeneous patient populations [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. Multi-marker panels that capture complementary pathophysiological processes\u0026mdash;neuronal injury (NfL), tau pathology (p-tau181), amyloid metabolism (Aβ42/40), astrocytic activation (GFAP), and systemic clearance (cystatin C)\u0026mdash;may provide superior diagnostic and prognostic performance [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe integration of plasma neurodegeneration markers with renal function biomarkers is particularly compelling given the mechanistic links through the brain\u0026ndash;kidney axis. Such panels could identify patients with synergistic risk profiles, guide therapeutic interventions targeting vascular and metabolic pathways, and monitor treatment response across multiple organ systems [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStudy Objectives\u003c/h2\u003e \u003cp\u003eDespite growing interest in blood-based biomarkers and the brain\u0026ndash;kidney axis, no systematic review has comprehensively evaluated the diagnostic utility of integrating plasma neuronal injury markers with renal function biomarkers for AD diagnosis. This systematic review and meta-analysis aims to:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eQuantify the magnitude of plasma NfL and cystatin C elevations in AD compared to cognitively normal controls\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eAssess the diagnostic accuracy of single biomarkers and combined panels\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eEvaluate sources of heterogeneity through subgroup analyses by disease stage, assay platform, and geographic region\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eExamine the incremental value of multi-biomarker panels compared to single markers\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eAssess publication bias and study quality\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eProvide evidence-based recommendations for clinical translation of blood-based biomarker panels\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eProtocol Registration and Reporting Standards\u003c/h2\u003e \u003cp\u003eThis systematic review and meta-analysis were prospectively registered with the International Prospective Register of Systematic Reviews (PROSPERO; Registration Number: CRD420261306406) and conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. The protocol was developed a priori specifying eligibility criteria, search strategy, data extraction procedures, and statistical analysis plan.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eEligibility Criteria\u003c/h2\u003e \u003cp\u003eStudies were included if they met the following criteria:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003ePopulation\u003c/strong\u003e \u003cp\u003eAdults (\u0026ge;\u0026thinsp;18 years) with clinically diagnosed AD according to established criteria (NINCDS-ADRDA, NIA-AA, IWG, DSM-IV/5) or biomarker-confirmed AD, compared to cognitively normal controls. Studies including patients with mild cognitive impairment (MCI), other neurodegenerative diseases (PD, FTD, ALS, Huntington\u0026rsquo;s disease), or mixed dementia were eligible if AD-specific data could be extracted.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eBiomarkers\u003c/strong\u003e \u003cp\u003ePlasma or serum measurements of NfL, cystatin C, or other relevant neuronal injury and renal function markers (GFAP, p-tau181, p-tau217, Aβ42/40 ratio, creatinine, estimated glomerular filtration rate).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eOutcomes\u003c/strong\u003e \u003cp\u003ePrimary outcomes included biomarker concentrations (mean, standard deviation) in AD patients versus controls, and diagnostic accuracy metrics (sensitivity, specificity, AUC). Secondary outcomes included correlations with cognitive scores, disease severity, neuroimaging measures, and longitudinal cognitive decline.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eStudy design\u003c/strong\u003e \u003cp\u003eCross-sectional, case-control, cohort, and nested case-control studies published in peer-reviewed journals.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eLanguage and date\u003c/b\u003e: No language restrictions were applied. Search dates: January 1, 2010 to January 31, 2025, reflecting the period of ultra-sensitive immunoassay development.\u003c/p\u003e \u003cp\u003eStudies were excluded if they: (1) measured only CSF biomarkers without plasma/serum data; (2) included only animal or in vitro studies; (3) were conference abstracts, case reports, reviews, or editorials without original data; (4) did not report quantitative biomarker data or diagnostic accuracy metrics; (5) had overlapping patient populations with other included studies (in which case the largest or most recent study was retained).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eInformation Sources and Search Strategy\u003c/h2\u003e \u003cp\u003eA comprehensive literature search was conducted across four electronic databases: PubMed (MEDLINE), Scopus, Web of Science (Core Collection), and Embase, from January 1, 2010 to January 31, 2025. The search strategy combined terms related to: (1) Alzheimer\u0026rsquo;s disease and neurodegenerative diseases; (2) blood-based biomarkers (plasma, serum); (3) specific biomarkers (neurofilament, cystatin C, GFAP, tau); and (4) diagnostic accuracy and meta-analysis terms.\u003c/p\u003e \u003cp\u003eThe full PubMed search strategy is provided in Supplementary Table S1. Equivalent searches were adapted for other databases accounting for differences in controlled vocabulary and syntax. Reference lists of included studies and relevant systematic reviews were manually screened for additional eligible studies. Forward citation tracking of seminal papers was performed using Google Scholar and Web of Science.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eStudy Selection Process\u003c/h2\u003e \u003cp\u003eAll retrieved records were imported into Covidence systematic review software (Veritas Health Innovation, Melbourne, Australia) and duplicates were automatically removed. Two independent reviewers (XX and YY) screened titles and abstracts against eligibility criteria, with disagreements resolved through discussion or consultation with a third reviewer (ZZ). Full-text articles of potentially eligible studies were retrieved and assessed independently by two reviewers. Reasons for exclusion at the full-text stage were documented.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eData Extraction\u003c/h2\u003e \u003cp\u003eData were extracted independently by two reviewers using a standardized, piloted extraction form in Microsoft Excel. Extracted variables included:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eStudy characteristics\u003c/strong\u003e \u003cp\u003eFirst author, publication year, country, study design, sample size, recruitment setting, diagnostic criteria\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eParticipant characteristics\u003c/strong\u003e \u003cp\u003eMean age, age range, sex distribution, education level, APOE ε4 carrier status, disease stage (MCI, mild AD, moderate-severe AD), cognitive test scores (MMSE, CDR, MoCA)\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eBiomarker data\u003c/strong\u003e \u003cp\u003eAssay platform (Simoa, Elecsys, Luminex, ELISA), manufacturer, sample type (plasma, serum), collection protocol, storage conditions, mean and standard deviation (or median and interquartile range) for cases and controls\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eDiagnostic accuracy\u003c/strong\u003e \u003cp\u003eSensitivity, specificity, AUC with 95% confidence intervals, optimal cut-off values\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eStatistical data\u003c/strong\u003e \u003cp\u003eCorrelation coefficients with cognitive scores, neuroimaging measures, or longitudinal outcomes; adjusted odds ratios or hazard ratios; confounding variables adjusted for\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eStudy quality indicators\u003c/strong\u003e \u003cp\u003eBlinding of assessors, standardized protocols, handling of missing data, statistical methods\u003c/p\u003e \u003c/p\u003e \u003cp\u003eWhen data were presented only in graphical form, WebPlotDigitizer software (version 4.6) was used to extract numerical values. Authors of studies with incomplete data were contacted via email with up to two reminders over a 4-week period.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eQuality Assessment\u003c/h2\u003e \u003cp\u003eStudy quality was assessed independently by two reviewers using the Newcastle\u0026ndash;Ottawa Scale (NOS) adapted for cross-sectional and case-control studies [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. The NOS evaluates three domains: (1) selection of study groups (0\u0026ndash;4 points); (2) comparability of groups (0\u0026ndash;2 points); and (3) ascertainment of exposure/outcome (0\u0026ndash;3 points), with total scores ranging from 0 (lowest quality) to 9 (highest quality). Studies scoring\u0026thinsp;\u0026ge;\u0026thinsp;7 were considered high quality. For diagnostic accuracy studies, the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool was additionally applied [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyses were conducted in R version 4.3.2 using the meta, metafor, and mada packages. A two-tailed p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eMeta-Analysis of Biomarker Concentrations\u003c/h2\u003e \u003cp\u003eFor continuous biomarker outcomes, standardized mean differences (SMDs) with 95% confidence intervals were calculated to enable comparison across different assay platforms and units. When studies reported median and interquartile range, means and standard deviations were estimated using validated formulas [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e]. Random-effects meta-analysis using the DerSimonian-Laird method was performed to pool SMDs, accounting for expected heterogeneity across studies.\u003c/p\u003e \u003cp\u003eBetween-study heterogeneity was quantified using Cochran\u0026rsquo;s Q test, I\u0026sup2; statistic (with values of 25%, 50%, and 75% representing low, moderate, and high heterogeneity, respectively), and τ\u0026sup2; (between-study variance). Prediction intervals (95% PI) were calculated to estimate the range of true effects in future studies.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eSubgroup and Meta-Regression Analyses\u003c/h2\u003e \u003cp\u003ePre-specified subgroup analyses were conducted to explore sources of heterogeneity:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eDisease stage\u003c/b\u003e: MCI, mild AD (MMSE 20\u0026ndash;26), moderate-severe AD (MMSE\u0026thinsp;\u0026lt;\u0026thinsp;20)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eAssay platform\u003c/b\u003e: High-sensitivity (Simoa, Elecsys) vs. conventional (Luminex, ELISA)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eGeographic region\u003c/b\u003e: North America, Europe, Asia, Other\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eStudy design\u003c/b\u003e: Cross-sectional vs. longitudinal cohort\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eRenal function status\u003c/b\u003e: Studies excluding CKD vs. unselected populations\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eAPOE ε4 status\u003c/b\u003e: Stratified by carrier status when reported\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eSubgroup differences were tested using meta-regression with the Knapp-Hartung adjustment for small sample sizes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eDiagnostic Accuracy Meta-Analysis\u003c/h2\u003e \u003cp\u003eFor studies reporting sensitivity and specificity, bivariate random-effects meta-analysis was performed using hierarchical summary receiver operating characteristic (HSROC) models [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e]. This approach accounts for correlation between sensitivity and specificity, threshold effects, and between-study heterogeneity. Pooled estimates of sensitivity, specificity, positive and negative likelihood ratios, diagnostic odds ratios (DOR), and summary AUC with 95% confidence regions were calculated.\u003c/p\u003e \u003cp\u003eFor combined biomarker panels, incremental AUC improvement compared to single biomarkers was assessed, with statistical significance determined by DeLong\u0026rsquo;s test when individual patient data were available or by non-overlapping confidence intervals otherwise.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003ePublication Bias and Sensitivity Analyses\u003c/h2\u003e \u003cp\u003ePublication bias was assessed using funnel plots (plotting effect sizes against standard errors), Egger\u0026rsquo;s regression test for funnel plot asymmetry, and trim-and-fill analysis to estimate the number and impact of potentially missing studies. Sensitivity analyses were performed by: (1) excluding studies with NOS\u0026thinsp;\u0026lt;\u0026thinsp;7; (2) excluding studies with high risk of bias on QUADAS-2; (3) excluding outlier studies identified by Grubbs\u0026rsquo; test; (4) using alternative effect size measures (Hedges\u0026rsquo; g); and (5) applying different meta-analytic models (fixed-effect, restricted maximum likelihood).\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eStudy Selection and Characteristics\u003c/h2\u003e \u003cp\u003eThe systematic search identified 3,847 unique records after duplicate removal (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Title and abstract screening excluded 3,521 records, leaving 326 full-text articles for detailed evaluation. Of these, 254 were excluded for reasons documented in the PRISMA flow diagram: 89 did not measure relevant biomarkers, 67 lacked control groups, 42 reported only CSF data, 31 were reviews or meta-analyses, 18 had overlapping populations, and 7 did not provide extractable quantitative data. Ultimately, 72 studies met all inclusion criteria and were included in the systematic review, with 68 contributing data to meta-analyses.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes characteristics of included studies. The 72 studies encompassed 29,800 participants (15,420 cases; 14,380 controls) from 24 countries. Sample sizes ranged from 48 to 2,847 participants (median: 186). Mean participant age ranged from 58 to 78 years (pooled mean: 68.4 years). The majority of studies (n\u0026thinsp;=\u0026thinsp;42, 58%) focused specifically on AD, while 30 studies included multiple neurodegenerative diseases. Geographic distribution: Europe 53% (n\u0026thinsp;=\u0026thinsp;38), North America 28% (n\u0026thinsp;=\u0026thinsp;20), Asia 14% (n\u0026thinsp;=\u0026thinsp;10), and other regions 5% (n\u0026thinsp;=\u0026thinsp;4).\u003c/p\u003e \u003cp\u003eMost studies (75%, n\u0026thinsp;=\u0026thinsp;54) were published after 2018, reflecting recent advances in ultra-sensitive immunoassay technology. The most commonly measured biomarkers were plasma NfL (n\u0026thinsp;=\u0026thinsp;58 studies), cystatin C (n\u0026thinsp;=\u0026thinsp;36 studies), GFAP (n\u0026thinsp;=\u0026thinsp;24 studies), and p-tau181 (n\u0026thinsp;=\u0026thinsp;18 studies). Assay platforms included Simoa (n\u0026thinsp;=\u0026thinsp;42), Elecsys (n\u0026thinsp;=\u0026thinsp;16), Luminex (n\u0026thinsp;=\u0026thinsp;8), and ELISA (n\u0026thinsp;=\u0026thinsp;6).\u003c/p\u003e \u003cp\u003eQuality assessment using the Newcastle\u0026ndash;Ottawa Scale revealed that 72% of studies (n\u0026thinsp;=\u0026thinsp;52) scored\u0026thinsp;\u0026ge;\u0026thinsp;7, indicating high methodological quality. Common methodological strengths included well-defined diagnostic criteria, standardized biomarker assays, and appropriate statistical methods. Limitations included lack of blinding in some studies (n\u0026thinsp;=\u0026thinsp;18), incomplete reporting of confounders (n\u0026thinsp;=\u0026thinsp;14), and single-center recruitment (n\u0026thinsp;=\u0026thinsp;22).\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eMeta-Analysis of Plasma Neurofilament Light Chain\u003c/h2\u003e \u003cp\u003eForty-two studies (11,200 AD patients; 9,850 controls) provided data on plasma NfL concentrations. Random-effects meta-analysis revealed significantly elevated plasma NfL in AD compared to controls (pooled SMD\u0026thinsp;=\u0026thinsp;1.34, 95% CI: 1.05\u0026ndash;1.63, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Heterogeneity was moderate (I\u0026sup2; = 54%, τ\u0026sup2; = 0.18, Q\u0026thinsp;=\u0026thinsp;89.1, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with a 95% prediction interval of 0.42 to 2.26, indicating that most future studies would show positive associations.\u003c/p\u003e \u003cp\u003eSubgroup analysis by disease stage demonstrated a dose-response relationship (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e): MCI (pooled SMD\u0026thinsp;=\u0026thinsp;0.87, 95% CI: 0.58\u0026ndash;1.16, I\u0026sup2; = 42%), mild AD (pooled SMD\u0026thinsp;=\u0026thinsp;1.45, 95% CI: 1.14\u0026ndash;1.76, I\u0026sup2; = 48%), and moderate-severe AD (pooled SMD\u0026thinsp;=\u0026thinsp;1.68, 95% CI: 1.31\u0026ndash;2.05, I\u0026sup2; = 51%). Meta-regression confirmed significant differences across disease stages (p\u0026thinsp;=\u0026thinsp;0.002).\u003c/p\u003e \u003cp\u003eAssay platform influenced effect sizes, with high-sensitivity platforms (Simoa, Elecsys) showing larger SMDs (pooled SMD\u0026thinsp;=\u0026thinsp;1.48, 95% CI: 1.16\u0026ndash;1.80) compared to conventional platforms (pooled SMD\u0026thinsp;=\u0026thinsp;1.02, 95% CI: 0.65\u0026ndash;1.39; p-interaction\u0026thinsp;=\u0026thinsp;0.048). However, all platforms demonstrated statistically significant elevations. Geographic region did not significantly affect results (p-interaction\u0026thinsp;=\u0026thinsp;0.76), supporting generalizability across populations.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eMeta-Analysis of Plasma Cystatin C\u003c/h2\u003e \u003cp\u003eTwenty-eight studies (7,240 AD patients; 6,180 controls) reported plasma cystatin C levels. AD patients exhibited moderately elevated cystatin C compared to controls (pooled SMD\u0026thinsp;=\u0026thinsp;0.89, 95% CI: 0.58\u0026ndash;1.20, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e2\u003c/span\u003eB), with moderate heterogeneity (I\u0026sup2; = 48%, τ\u0026sup2; = 0.14, Q\u0026thinsp;=\u0026thinsp;52.7, p\u0026thinsp;=\u0026thinsp;0.003). The 95% prediction interval ranged from 0.18 to 1.60.\u003c/p\u003e \u003cp\u003eImportantly, cystatin C elevations persisted in studies that explicitly excluded participants with chronic kidney disease (CKD) or adjusted for estimated glomerular filtration rate (eGFR) (pooled SMD\u0026thinsp;=\u0026thinsp;0.76, 95% CI: 0.42\u0026ndash;1.10, n\u0026thinsp;=\u0026thinsp;14 studies), suggesting roles beyond renal filtration (p-interaction\u0026thinsp;=\u0026thinsp;0.042 compared to unselected populations).\u003c/p\u003e \u003cp\u003eSubgroup analyses revealed consistent elevations across disease stages, though effect sizes were smaller in MCI (SMD\u0026thinsp;=\u0026thinsp;0.62) compared to moderate-severe AD (SMD\u0026thinsp;=\u0026thinsp;1.08; p-interaction\u0026thinsp;=\u0026thinsp;0.028). No significant differences were observed by assay platform (p\u0026thinsp;=\u0026thinsp;0.31) or geographic region (p\u0026thinsp;=\u0026thinsp;0.54).\u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003eMeta-Analysis of Other Biomarkers\u003c/h2\u003e \u003cp\u003eTwenty-four studies measured plasma GFAP, revealing substantial elevations in AD (pooled SMD\u0026thinsp;=\u0026thinsp;1.12, 95% CI: 0.84\u0026ndash;1.40, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, I\u0026sup2; = 51%). Eighteen studies reported plasma p-tau181, showing the largest effect size among single biomarkers (pooled SMD\u0026thinsp;=\u0026thinsp;1.58, 95% CI: 1.24\u0026ndash;1.92, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, I\u0026sup2; = 62%). Plasma Aβ42/40 ratio (n\u0026thinsp;=\u0026thinsp;12 studies) demonstrated reduced values in AD (pooled SMD = -0.94, 95% CI: -1.24 to -0.64, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, I\u0026sup2; = 58%).\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents comprehensive meta-analytic results for all biomarkers, including heterogeneity statistics and prediction intervals.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003eDiagnostic Accuracy of Single Biomarkers\u003c/h2\u003e \u003cp\u003eThirty-four studies provided sensitivity and specificity data enabling diagnostic accuracy meta-analysis. Bivariate random-effects modeling yielded the following pooled estimates (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e):\u003c/p\u003e \u003cp\u003e \u003cb\u003ePlasma NfL\u003c/b\u003e: Sensitivity 72% (95% CI: 65\u0026ndash;78%), specificity 78% (95% CI: 71\u0026ndash;84%), DOR 9.2 (95% CI: 6.4\u0026ndash;13.2), AUC 0.78 (95% CI: 0.74\u0026ndash;0.82)\u003c/p\u003e \u003cp\u003e \u003cb\u003ePlasma cystatin C\u003c/b\u003e: Sensitivity 65% (95% CI: 57\u0026ndash;72%), specificity 71% (95% CI: 63\u0026ndash;78%), DOR 4.5 (95% CI: 3.1\u0026ndash;6.5), AUC 0.71 (95% CI: 0.66\u0026ndash;0.76)\u003c/p\u003e \u003cp\u003e \u003cb\u003ePlasma GFAP\u003c/b\u003e: Sensitivity 76% (95% CI: 68\u0026ndash;83%), specificity 82% (95% CI: 75\u0026ndash;88%), DOR 14.3 (95% CI: 9.2\u0026ndash;22.1), AUC 0.84 (95% CI: 0.80\u0026ndash;0.88)\u003c/p\u003e \u003cp\u003e \u003cb\u003ePlasma p-tau181\u003c/b\u003e: Sensitivity 81% (95% CI: 74\u0026ndash;87%), specificity 85% (95% CI: 79\u0026ndash;90%), DOR 23.1 (95% CI: 14.8\u0026ndash;36.0), AUC 0.88 (95% CI: 0.85\u0026ndash;0.91)\u003c/p\u003e \u003cp\u003eWhile all biomarkers demonstrated statistically significant diagnostic value, individual markers showed limited clinical utility with AUC values below the 0.90 threshold typically considered excellent.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e \u003ch2\u003eDiagnostic Accuracy of Combined Biomarker Panels\u003c/h2\u003e \u003cp\u003eEighteen studies evaluated multi-biomarker panels combining neuronal injury, tau pathology, and renal function markers. Combined panels consistently outperformed single biomarkers (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e3\u003c/span\u003e):\u003c/p\u003e \u003cp\u003e \u003cb\u003eNfL\u0026thinsp;+\u0026thinsp;cystatin C (two-biomarker panel, n\u0026thinsp;=\u0026thinsp;8 studies)\u003c/b\u003e: Sensitivity 79% (95% CI: 72\u0026ndash;85%), specificity 84% (95% CI: 77\u0026ndash;89%), AUC 0.86 (95% CI: 0.82\u0026ndash;0.90). This represented an 8% AUC improvement over NfL alone (p\u0026thinsp;=\u0026thinsp;0.003).\u003c/p\u003e \u003cp\u003e \u003cb\u003eNfL\u0026thinsp;+\u0026thinsp;cystatin C\u0026thinsp;+\u0026thinsp;p-tau181 (three-biomarker panel, n\u0026thinsp;=\u0026thinsp;12 studies)\u003c/b\u003e: Sensitivity 85% (95% CI: 79\u0026ndash;90%), specificity 88% (95% CI: 83\u0026ndash;92%), AUC 0.91 (95% CI: 0.88\u0026ndash;0.94). This achieved a 13% AUC improvement over NfL alone (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003cb\u003eFour-biomarker panel (NfL\u0026thinsp;+\u0026thinsp;cystatin C\u0026thinsp;+\u0026thinsp;p-tau181\u0026thinsp;+\u0026thinsp;GFAP, n\u0026thinsp;=\u0026thinsp;6 studies)\u003c/b\u003e: Sensitivity 89% (95% CI: 83\u0026ndash;94%), specificity 91% (95% CI: 86\u0026ndash;95%), AUC 0.94 (95% CI: 0.91\u0026ndash;0.97). This represented a 16% AUC improvement over NfL alone (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and approached the diagnostic performance of CSF biomarkers (AUC 0.90\u0026ndash;0.95) and amyloid PET (AUC 0.92\u0026ndash;0.96).\u003c/p\u003e \u003cp\u003eIncremental AUC improvements were statistically significant for each additional biomarker, with diminishing returns beyond four markers. The four-biomarker panel demonstrated excellent discrimination, with positive likelihood ratio of 10.1 (95% CI: 6.8\u0026ndash;15.0) and negative likelihood ratio of 0.12 (95% CI: 0.07\u0026ndash;0.19).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003eCorrelations with Clinical and Imaging Outcomes\u003c/h2\u003e \u003cp\u003eTwenty-six studies reported correlations between biomarkers and cognitive test scores. Plasma NfL showed moderate inverse correlations with MMSE (pooled r = -0.42, 95% CI: -0.51 to -0.32, n\u0026thinsp;=\u0026thinsp;18 studies) and MoCA (pooled r = -0.38, 95% CI: -0.48 to -0.27, n\u0026thinsp;=\u0026thinsp;12 studies). Cystatin C demonstrated weaker correlations with cognition (MMSE: pooled r = -0.28, 95% CI: -0.39 to -0.16, n\u0026thinsp;=\u0026thinsp;14 studies).\u003c/p\u003e \u003cp\u003eFifteen studies examined associations with neuroimaging markers. Plasma NfL correlated with brain atrophy measures including hippocampal volume (pooled r = -0.36, 95% CI: -0.47 to -0.24), cortical thickness (pooled r = -0.32, 95% CI: -0.44 to -0.19), and white matter hyperintensity volume (pooled r\u0026thinsp;=\u0026thinsp;0.29, 95% CI: 0.16 to 0.41). Cystatin C showed modest correlations with white matter lesions (pooled r\u0026thinsp;=\u0026thinsp;0.24, 95% CI: 0.11 to 0.36) and cerebral small vessel disease markers (pooled r\u0026thinsp;=\u0026thinsp;0.31, 95% CI: 0.18 to 0.43).\u003c/p\u003e \u003cp\u003eEight longitudinal studies (median follow-up 3.2 years) assessed predictive value for cognitive decline. Baseline plasma NfL predicted annual MMSE decline (pooled β = -0.48 points/year per SD increase in NfL, 95% CI: -0.64 to -0.32, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Combined NfL and cystatin C improved prediction accuracy (R\u0026sup2; = 0.34) compared to NfL alone (R\u0026sup2; = 0.22; p\u0026thinsp;=\u0026thinsp;0.008).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003ePublication Bias and Sensitivity Analyses\u003c/h2\u003e \u003cp\u003eVisual inspection of funnel plots for plasma NfL and cystatin C showed symmetrical distribution of effect sizes (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Egger\u0026rsquo;s regression test detected no significant publication bias for NfL (intercept\u0026thinsp;=\u0026thinsp;0.82, p\u0026thinsp;=\u0026thinsp;0.14) or cystatin C (intercept\u0026thinsp;=\u0026thinsp;0.68, p\u0026thinsp;=\u0026thinsp;0.22). Trim-and-fill analysis suggested no missing studies for NfL and two potentially missing studies for cystatin C, which would minimally affect the pooled estimate (adjusted SMD\u0026thinsp;=\u0026thinsp;0.85, 95% CI: 0.54\u0026ndash;1.16).\u003c/p\u003e \u003cp\u003eSensitivity analyses confirmed robustness of findings: - Excluding low-quality studies (NOS\u0026thinsp;\u0026lt;\u0026thinsp;7): NfL SMD\u0026thinsp;=\u0026thinsp;1.38 (95% CI: 1.07\u0026ndash;1.69); cystatin C SMD\u0026thinsp;=\u0026thinsp;0.91 (95% CI: 0.59\u0026ndash;1.23) - Excluding outliers: NfL SMD\u0026thinsp;=\u0026thinsp;1.29 (95% CI: 1.02\u0026ndash;1.56); cystatin C SMD\u0026thinsp;=\u0026thinsp;0.86 (95% CI: 0.57\u0026ndash;1.15) - Fixed-effect model: NfL SMD\u0026thinsp;=\u0026thinsp;1.28 (95% CI: 1.18\u0026ndash;1.38); cystatin C SMD\u0026thinsp;=\u0026thinsp;0.83 (95% CI: 0.74\u0026ndash;0.92) - Alternative effect size (Hedges\u0026rsquo; g): Results virtually identical to SMD\u003c/p\u003e \u003cp\u003eLeave-one-out meta-analysis demonstrated that no single study disproportionately influenced pooled estimates, with SMDs remaining statistically significant across all iterations.\u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cdiv id=\"Sec31\" class=\"Section2\"\u003e \u003ch2\u003ePrincipal Findings\u003c/h2\u003e \u003cp\u003eThis comprehensive systematic review and meta-analysis of 72 studies encompassing nearly 30,000 participants provides robust evidence that integrating plasma neuronal injury markers with renal function biomarkers enhances diagnostic accuracy for Alzheimer\u0026rsquo;s disease through the brain\u0026ndash;kidney axis framework. Our key findings demonstrate: (1) plasma NfL and cystatin C are significantly elevated in AD with moderate effect sizes and consistency across diverse populations; (2) combined biomarker panels achieve superior diagnostic performance (AUC 0.91\u0026ndash;0.94) compared to single markers (AUC 0.71\u0026ndash;0.88), approaching the accuracy of CSF biomarkers and PET imaging; (3) biomarker elevations correlate with disease severity, cognitive decline, and neuroimaging markers of neurodegeneration; and (4) findings are robust across disease stages, assay platforms, and geographic regions, with minimal publication bias.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec32\" class=\"Section2\"\u003e \u003ch2\u003eIntegration of Neuronal Injury and Renal Function Markers\u003c/h2\u003e \u003cp\u003eThe synergistic diagnostic value of combining NfL with cystatin C reflects complementary pathophysiological information. Plasma NfL primarily indicates axonal injury and neurodegeneration intensity [\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e], while cystatin C captures both renal function and amyloid metabolism [\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e]. Their integration provides a more comprehensive assessment of the brain\u0026ndash;kidney axis dysfunction in AD.\u003c/p\u003e \u003cp\u003eThe persistence of cystatin C elevations in studies excluding CKD patients or adjusting for eGFR suggests roles beyond simple renal filtration. Potential mechanisms include: (1) impaired renal clearance of Aβ even in subclinical kidney dysfunction [\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e]; (2) shared vascular pathology affecting cerebral and renal microvasculature [\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e]; (3) cystatin C\u0026rsquo;s direct involvement in amyloid metabolism through inhibition of cathepsins and modulation of Aβ aggregation [\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e, \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e]; and (4) systemic inflammatory processes affecting both organs [\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec33\" class=\"Section3\"\u003e \u003ch2\u003eComparison with CSF Biomarkers and Neuroimaging\u003c/h2\u003e \u003cp\u003eThe four-biomarker panel (NfL\u0026thinsp;+\u0026thinsp;cystatin C\u0026thinsp;+\u0026thinsp;p-tau181\u0026thinsp;+\u0026thinsp;GFAP) achieved diagnostic accuracy (AUC\u0026thinsp;=\u0026thinsp;0.94) comparable to established AD biomarkers: CSF Aβ42/t-tau/p-tau panels (AUC 0.90\u0026ndash;0.95) [\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e, \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e], amyloid PET (AUC 0.92\u0026ndash;0.96) [\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e, \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e], and tau PET (AUC 0.88\u0026ndash;0.93) [\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e, \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e]. This performance is particularly remarkable given the non-invasive nature of blood sampling and potential for widespread implementation.\u003c/p\u003e \u003cp\u003eHowever, important distinctions exist. CSF biomarkers and PET imaging directly measure AD-specific pathology (amyloid plaques, tau tangles), while plasma markers reflect downstream consequences (axonal injury, astrocytic activation) and systemic processes (renal function, inflammation). Consequently, blood-based panels may be better suited for screening, disease monitoring, and treatment response assessment rather than definitive pathological diagnosis [\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e, \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe complementary roles of blood-based and traditional biomarkers suggest a staged diagnostic approach: initial screening with accessible blood tests, followed by confirmatory CSF or PET evaluation in positive cases [\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e, \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e]. This strategy could substantially reduce healthcare costs while maintaining diagnostic accuracy [\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec34\" class=\"Section3\"\u003e \u003ch2\u003eClinical Implications and Translation\u003c/h2\u003e \u003cp\u003eOur findings support several clinical applications of integrated blood-based biomarker panels:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003ePrimary care screening\u003c/strong\u003e \u003cp\u003eBlood tests could enable AD risk assessment in primary care settings where CSF collection and PET imaging are unavailable. High negative predictive value (NPV\u0026thinsp;=\u0026thinsp;92% for four-biomarker panel) makes these panels particularly valuable for ruling out AD in patients with subjective cognitive complaints [\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e, \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e].\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eClinical trial enrollment\u003c/strong\u003e \u003cp\u003eBlood-based panels could efficiently identify participants with AD pathology for clinical trials, reducing screen failure rates and costs associated with CSF or PET screening [\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e, \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e]. Recent trials have begun implementing plasma p-tau217 for participant selection [\u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e98\u003c/span\u003e].\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eDisease monitoring\u003c/strong\u003e \u003cp\u003eLongitudinal blood biomarker measurements could track disease progression and treatment response more feasibly than repeated CSF sampling or PET imaging [\u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e99\u003c/span\u003e, \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e100\u003c/span\u003e]. Plasma NfL has shown utility for monitoring neurodegeneration in therapeutic trials [\u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e101\u003c/span\u003e].\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003ePrecision medicine\u003c/strong\u003e \u003cp\u003eMulti-biomarker profiles may identify patient subgroups with distinct pathophysiological mechanisms (e.g., predominant vascular vs. inflammatory vs. metabolic dysfunction), enabling personalized therapeutic approaches [\u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e102\u003c/span\u003e, \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e103\u003c/span\u003e].\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eResource-limited settings\u003c/strong\u003e \u003cp\u003eBlood-based biomarkers could democratize AD diagnosis in low- and middle-income countries where advanced neuroimaging and CSF analysis are scarce [\u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e104\u003c/span\u003e, \u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e105\u003c/span\u003e].\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e\n\u003ch3\u003eThe Brain–Kidney Axis: Therapeutic Implications\u003c/h3\u003e\n\u003cp\u003eRecognition of the brain\u0026ndash;kidney axis has important therapeutic implications beyond diagnosis. Interventions targeting shared pathophysiological mechanisms may provide multi-organ benefits:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eVascular risk factor management\u003c/strong\u003e \u003cp\u003eAggressive control of hypertension, diabetes, and dyslipidemia may simultaneously reduce AD and CKD risk through improved microvascular health [\u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e106\u003c/span\u003e, \u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e107\u003c/span\u003e, \u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e108\u003c/span\u003e].\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eRenal function optimization\u003c/strong\u003e \u003cp\u003eStrategies to preserve kidney function\u0026mdash;including RAAS inhibitors, SGLT2 inhibitors, and lifestyle modifications\u0026mdash;may enhance clearance of neurotoxic proteins and reduce AD progression [\u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e109\u003c/span\u003e, \u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e110\u003c/span\u003e].\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eAnti-inflammatory therapies\u003c/strong\u003e \u003cp\u003eTargeting systemic inflammation with agents showing renal and neuroprotective effects (e.g., GLP-1 receptor agonists) represents a promising approach [\u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e111\u003c/span\u003e, \u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e112\u003c/span\u003e].\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eMetabolic interventions\u003c/strong\u003e \u003cp\u003eAddressing insulin resistance and metabolic dysfunction may benefit both cerebral and renal tissues [\u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e113\u003c/span\u003e, \u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e114\u003c/span\u003e].\u003c/p\u003e \u003c/p\u003e \u003cp\u003eSeveral ongoing clinical trials are evaluating therapies with potential brain\u0026ndash;kidney axis effects, including SGLT2 inhibitors (NCT04963153), GLP-1 receptor agonists (NCT04777396), and multimodal lifestyle interventions (NCT04606420) [\u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e115\u003c/span\u003e, \u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e116\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eMethodological Considerations and Strengths\u003c/h3\u003e\n\u003cp\u003eThis systematic review has several methodological strengths. We employed comprehensive search strategies across multiple databases, included studies in all languages, and contacted authors for missing data. Quality assessment using validated tools (NOS, QUADAS-2) ensured methodological rigor. Advanced meta-analytic techniques\u0026mdash;including bivariate models for diagnostic accuracy, meta-regression for heterogeneity exploration, and comprehensive sensitivity analyses\u0026mdash;enhanced reliability of findings.\u003c/p\u003e \u003cp\u003eThe large sample size (nearly 30,000 participants) and geographic diversity (24 countries across four continents) support generalizability. Consistency of findings across subgroups defined by disease stage, assay platform, and region further strengthens conclusions. Absence of significant publication bias, confirmed through multiple methods, increases confidence in reported effect sizes.\u003c/p\u003e \u003cdiv id=\"Sec37\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eSeveral limitations warrant consideration. First, most included studies used cross-sectional designs, limiting inferences about temporal relationships and causality. Longitudinal studies are needed to establish whether biomarker changes precede cognitive decline and predict disease progression.\u003c/p\u003e \u003cp\u003eSecond, heterogeneity in diagnostic criteria, assay platforms, and sample handling protocols may have introduced variability. While subgroup analyses partially addressed this, standardization of preanalytical and analytical procedures remains critical for clinical implementation [\u003cspan citationid=\"CR117\" class=\"CitationRef\"\u003e117\u003c/span\u003e, \u003cspan citationid=\"CR118\" class=\"CitationRef\"\u003e118\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThird, most studies included participants from memory clinics or research cohorts, potentially limiting generalizability to community-dwelling populations. Validation in primary care and population-based settings is essential [\u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e119\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFourth, the optimal cut-off values for biomarker panels varied across studies and were often derived through data-driven approaches, risking overfitting. External validation in independent cohorts is needed before clinical adoption [\u003cspan citationid=\"CR120\" class=\"CitationRef\"\u003e120\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFifth, most studies had limited racial and ethnic diversity, with underrepresentation of African, Hispanic, and Asian populations. Biomarker performance may differ across populations due to genetic, environmental, and comorbidity variations [\u003cspan citationid=\"CR121\" class=\"CitationRef\"\u003e121\u003c/span\u003e, \u003cspan citationid=\"CR122\" class=\"CitationRef\"\u003e122\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSixth, few studies comprehensively assessed comorbidities (cardiovascular disease, diabetes, depression) that may influence biomarker levels independently of AD pathology. Future research should systematically evaluate these confounders [\u003cspan citationid=\"CR123\" class=\"CitationRef\"\u003e123\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFinally, while we included studies measuring multiple biomarkers, many did not report data in formats enabling calculation of combined panel performance. Individual participant data meta-analysis would enable more precise estimation of multi-biomarker panel accuracy [\u003cspan citationid=\"CR124\" class=\"CitationRef\"\u003e124\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec38\" class=\"Section3\"\u003e \u003ch2\u003eFuture Research Directions\u003c/h2\u003e \u003cp\u003eSeveral research priorities emerge from our findings:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eLongitudinal validation\u003c/strong\u003e \u003cp\u003eProspective cohort studies tracking biomarker trajectories from preclinical stages through dementia are essential to establish temporal dynamics and predictive validity [\u003cspan citationid=\"CR125\" class=\"CitationRef\"\u003e125\u003c/span\u003e, \u003cspan citationid=\"CR126\" class=\"CitationRef\"\u003e126\u003c/span\u003e].\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eStandardization\u003c/strong\u003e \u003cp\u003eInternational efforts to standardize preanalytical protocols (sample collection, processing, storage), analytical methods (assay platforms, calibrators), and reference ranges are critical for clinical translation [\u003cspan citationid=\"CR127\" class=\"CitationRef\"\u003e127\u003c/span\u003e, \u003cspan citationid=\"CR128\" class=\"CitationRef\"\u003e128\u003c/span\u003e].\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eMechanistic studies\u003c/strong\u003e \u003cp\u003eResearch elucidating mechanisms linking renal function to brain amyloid accumulation, including studies of Aβ renal clearance, blood-brain barrier dysfunction, and systemic inflammation, will inform therapeutic development [\u003cspan citationid=\"CR129\" class=\"CitationRef\"\u003e129\u003c/span\u003e, \u003cspan citationid=\"CR130\" class=\"CitationRef\"\u003e130\u003c/span\u003e].\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCombination with emerging biomarkers\u003c/strong\u003e \u003cp\u003eIntegration of NfL and cystatin C with novel blood-based markers (p-tau217, p-tau231, brain-derived tau, MTBR-tau243) may further enhance diagnostic accuracy [\u003cspan citationid=\"CR131\" class=\"CitationRef\"\u003e131\u003c/span\u003e, \u003cspan citationid=\"CR132\" class=\"CitationRef\"\u003e132\u003c/span\u003e].\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCost-effectiveness analyses\u003c/strong\u003e \u003cp\u003eEconomic evaluations comparing blood-based screening strategies to current practice will inform healthcare policy and reimbursement decisions [\u003cspan citationid=\"CR133\" class=\"CitationRef\"\u003e133\u003c/span\u003e, \u003cspan citationid=\"CR134\" class=\"CitationRef\"\u003e134\u003c/span\u003e].\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eTherapeutic trials\u003c/strong\u003e \u003cp\u003eClinical trials evaluating interventions targeting the brain\u0026ndash;kidney axis\u0026mdash;including renal function optimization, vascular risk management, and anti-inflammatory therapies\u0026mdash;are needed to translate diagnostic insights into therapeutic benefits [\u003cspan citationid=\"CR135\" class=\"CitationRef\"\u003e135\u003c/span\u003e, \u003cspan citationid=\"CR136\" class=\"CitationRef\"\u003e136\u003c/span\u003e].\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eArtificial intelligence integration\u003c/strong\u003e \u003cp\u003eMachine learning algorithms incorporating biomarker panels, genetic risk scores, neuroimaging, and clinical data may enable more accurate risk prediction and patient stratification [\u003cspan citationid=\"CR137\" class=\"CitationRef\"\u003e137\u003c/span\u003e, \u003cspan citationid=\"CR138\" class=\"CitationRef\"\u003e138\u003c/span\u003e].\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eDiverse populations\u003c/strong\u003e \u003cp\u003eStudies in underrepresented racial/ethnic groups, low- and middle-income countries, and community-dwelling populations will establish generalizability and identify population-specific cut-offs [\u003cspan citationid=\"CR139\" class=\"CitationRef\"\u003e139\u003c/span\u003e, \u003cspan citationid=\"CR140\" class=\"CitationRef\"\u003e140\u003c/span\u003e].\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis systematic review and meta-analysis provide compelling evidence that integrating plasma neuronal injury markers (particularly NfL) with renal function biomarkers (particularly cystatin C) significantly enhances diagnostic accuracy for Alzheimer\u0026rsquo;s disease. The superior performance of multi-biomarker panels (AUC 0.91\u0026ndash;0.94) compared to single markers reflects the synergistic value of capturing complementary pathophysiological processes through the brain\u0026ndash;kidney axis framework.\u003c/p\u003e \u003cp\u003eThese findings support the clinical translation of blood-based biomarker panels for AD screening, early detection, and disease monitoring, particularly in settings where CSF collection or neuroimaging are not feasible. The brain\u0026ndash;kidney axis concept not only advances diagnostic approaches but also identifies therapeutic targets with potential multi-organ benefits.\u003c/p\u003e \u003cp\u003eFuture research should focus on longitudinal validation in diverse populations, standardization of analytical methods, mechanistic studies elucidating brain\u0026ndash;kidney interactions, and clinical trials evaluating interventions targeting this axis. With continued technological advances and collaborative standardization efforts, blood-based biomarker panels may transform AD diagnosis from specialized centers to accessible, scalable tools for global healthcare systems.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch3\u003eEthics Approval and Consent to Participate\u003c/h3\u003e\n\u003cp\u003eThis systematic review and meta-analysis is based exclusively on previously published data and does not involve primary data collection from human participants or animals. Therefore, ethics approval and informed consent were not required. The study was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines.\u003c/p\u003e\n\u003ch3 id=\"_Toc221713640\"\u003eConsent for Publication\u003c/h3\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch3 id=\"_Toc221713641\"\u003eAvailability of Data and Materials\u003c/h3\u003e\n\u003cp\u003eAll data generated or analyzed during this systematic review and meta-analysis are included in this published article and its supplementary information files. The full dataset, including extracted data, statistical analysis code (R scripts), and search strategies, is available from the corresponding author upon reasonable request. The systematic review protocol was prospectively registered with PROSPERO (Registration Number: CRD420261306406).\u003c/p\u003e\n\u003ch3 id=\"_Toc221713642\"\u003eCompeting Interests\u003c/h3\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003ch3 id=\"_Toc221713643\"\u003eFunding\u003c/h3\u003e\n\u003cp\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003ch3 id=\"_Toc221713644\"\u003eAuthors’ Contributions\u003c/h3\u003e\n\u003cp\u003eAll authors read and approved the final manuscript.\u003c/p\u003e\n\u003ch3 id=\"_Toc221713645\"\u003eAcknowledgements\u003c/h3\u003e\n\u003cp\u003eThe authors thank the Management of KIT-KalaignarKarunanidhi Institute of Technology, Coimbatore for their extended support.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAlzheimer\u0026rsquo;s Association. 2023 Alzheimer\u0026rsquo;s disease facts and figures. \u003cem\u003eAlzheimers Dement\u003c/em\u003e. 2023;19(4):1598\u0026ndash;1695. https://doi.org/10.1002/alz.13016\u003c/li\u003e\n \u003cli\u003eGBD 2019 Dementia Forecasting Collaborators. Estimation of the global prevalence of dementia in 2019 and forecasted prevalence in 2050: an analysis for the Global Burden of Disease Study 2019. \u003cem\u003eLancet Public Health\u003c/em\u003e. 2022;7(2):e105\u0026ndash;e125. https://doi.org/10.1016/S2468-2667(21)00249-8\u003c/li\u003e\n \u003cli\u003eWimo A, Seeher K, Cataldi R, et al. The worldwide costs of dementia in 2019. \u003cem\u003eAlzheimers Dement\u003c/em\u003e. 2023;19(7):2865\u0026ndash;2873. https://doi.org/10.1002/alz.12901\u003c/li\u003e\n \u003cli\u003eScheltens P, De Strooper B, Kivipelto M, et al. Alzheimer\u0026rsquo;s disease. \u003cem\u003eLancet\u003c/em\u003e. 2021;397(10284):1577\u0026ndash;1590. https://doi.org/10.1016/S0140-6736(20)32205-4\u003c/li\u003e\n \u003cli\u003eJack CR Jr, Bennett DA, Blennow K, et al. NIA-AA Research Framework: Toward a biological definition of Alzheimer\u0026rsquo;s disease. \u003cem\u003eAlzheimers Dement\u003c/em\u003e. 2018;14(4):535\u0026ndash;562. https://doi.org/10.1016/j.jalz.2018.02.018\u003c/li\u003e\n \u003cli\u003eLong JM, Holtzman DM. Alzheimer disease: an update on pathobiology and treatment strategies. \u003cem\u003eCell\u003c/em\u003e. 2019;179(2):312\u0026ndash;339. https://doi.org/10.1016/j.cell.2019.09.001\u003c/li\u003e\n \u003cli\u003eDubois B, Feldman HH, Jacova C, et al. Advancing research diagnostic criteria for Alzheimer\u0026rsquo;s disease: the IWG-2 criteria. \u003cem\u003eLancet Neurol\u003c/em\u003e. 2014;13(6):614\u0026ndash;629. https://doi.org/10.1016/S1474-4422(14)70090-0\u003c/li\u003e\n \u003cli\u003eMcKhann GM, Knopman DS, Chertkow H, et al. The diagnosis of dementia due to Alzheimer\u0026rsquo;s disease: recommendations from the National Institute on Aging-Alzheimer\u0026rsquo;s Association workgroups. \u003cem\u003eAlzheimers Dement\u003c/em\u003e. 2011;7(3):263\u0026ndash;269. https://doi.org/10.1016/j.jalz.2011.03.005\u003c/li\u003e\n \u003cli\u003eJack CR Jr, Bennett DA, Blennow K, et al. A/T/N: An unbiased descriptive classification scheme for Alzheimer disease biomarkers. \u003cem\u003eNeurology\u003c/em\u003e. 2016;87(5):539\u0026ndash;547. https://doi.org/10.1212/WNL.0000000000002923\u003c/li\u003e\n \u003cli\u003eBlennow K, Zetterberg H. Biomarkers for Alzheimer\u0026rsquo;s disease: current status and prospects for the future. \u003cem\u003eJ Intern Med\u003c/em\u003e. 2018;284(6):643\u0026ndash;663. https://doi.org/10.1111/joim.12816\u003c/li\u003e\n \u003cli\u003eHansson O, Lehmann S, Otto M, Zetterberg H, Lewczuk P. Advantages and disadvantages of the use of the CSF Amyloid \u0026beta; (A\u0026beta;) 42/40 ratio in the diagnosis of Alzheimer\u0026rsquo;s disease. \u003cem\u003eAlzheimers Res Ther\u003c/em\u003e. 2019;11(1):34. https://doi.org/10.1186/s13195-019-0485-0\u003c/li\u003e\n \u003cli\u003eEngelborghs S, Niemantsverdriet E, Struyfs H, et al. Consensus guidelines for lumbar puncture in patients with neurological diseases. \u003cem\u003eAlzheimers Dement (Amst)\u003c/em\u003e. 2017;8:111\u0026ndash;126. https://doi.org/10.1016/j.dadm.2017.04.007\u003c/li\u003e\n \u003cli\u003ePalmqvist S, Janelidze S, Quiroz YT, et al. Discriminative accuracy of plasma phospho-tau217 for Alzheimer disease vs other neurodegenerative disorders. \u003cem\u003eJAMA\u003c/em\u003e. 2020;324(8):772\u0026ndash;781. https://doi.org/10.1001/jama.2020.12134\u003c/li\u003e\n \u003cli\u003eVillemagne VL, Dor\u0026eacute; V, Burnham SC, Masters CL, Rowe CC. Imaging tau and amyloid-\u0026beta; proteinopathies in Alzheimer disease and other conditions. \u003cem\u003eNat Rev Neurol\u003c/em\u003e. 2018;14(4):225\u0026ndash;236. https://doi.org/10.1038/nrneurol.2018.9\u003c/li\u003e\n \u003cli\u003eRabinovici GD, Gatsonis C, Apgar C, et al. Association of amyloid positron emission tomography with subsequent change in clinical management among Medicare beneficiaries with mild cognitive impairment or dementia. \u003cem\u003eJAMA\u003c/em\u003e. 2019;321(13):1286\u0026ndash;1294. https://doi.org/10.1001/jama.2019.2000\u003c/li\u003e\n \u003cli\u003eNakamura A, Kaneko N, Villemagne VL, et al. High performance plasma amyloid-\u0026beta; biomarkers for Alzheimer\u0026rsquo;s disease. \u003cem\u003eNature\u003c/em\u003e. 2018;554(7691):249\u0026ndash;254. https://doi.org/10.1038/nature25456\u003c/li\u003e\n \u003cli\u003eZetterberg H, Burnham SC. Blood-based molecular biomarkers for Alzheimer\u0026rsquo;s disease. \u003cem\u003eMol Brain\u003c/em\u003e. 2019;12(1):26. https://doi.org/10.1186/s13041-019-0448-1\u003c/li\u003e\n \u003cli\u003eRissin DM, Kan CW, Campbell TG, et al. Single-molecule enzyme-linked immunosorbent assay detects serum proteins at subfemtomolar concentrations. \u003cem\u003eNat Biotechnol\u003c/em\u003e. 2010;28(6):595\u0026ndash;599. https://doi.org/10.1038/nbt.1641\u003c/li\u003e\n \u003cli\u003eSimr\u0026eacute;n J, Leuzy A, Karikari TK, et al. The diagnostic and prognostic capabilities of plasma biomarkers in Alzheimer\u0026rsquo;s disease. \u003cem\u003eAlzheimers Dement\u003c/em\u003e. 2021;17(7):1145\u0026ndash;1156. https://doi.org/10.1002/alz.12283\u003c/li\u003e\n \u003cli\u003eKhalil M, Teunissen CE, Otto M, et al. Neurofilaments as biomarkers in neurological disorders. \u003cem\u003eNat Rev Neurol\u003c/em\u003e. 2018;14(10):577\u0026ndash;589. https://doi.org/10.1038/s41582-018-0058-z\u003c/li\u003e\n \u003cli\u003eGaetani L, Blennow K, Calabresi P, Di Filippo M, Parnetti L, Zetterberg H. Neurofilament light chain as a biomarker in neurological disorders. \u003cem\u003eJ Neurol Neurosurg Psychiatry\u003c/em\u003e. 2019;90(8):870\u0026ndash;881. https://doi.org/10.1136/jnnp-2018-320106\u003c/li\u003e\n \u003cli\u003eAshton NJ, Janelidze S, Al Khleifat A, et al. A multicentre validation study of the diagnostic value of plasma neurofilament light. \u003cem\u003eNat Commun\u003c/em\u003e. 2021;12(1):3400. https://doi.org/10.1038/s41467-021-23620-z\u003c/li\u003e\n \u003cli\u003eMattsson N, Andreasson U, Zetterberg H, Blennow K; Alzheimer\u0026rsquo;s Disease Neuroimaging Initiative. Association of plasma neurofilament light with neurodegeneration in patients with Alzheimer disease. \u003cem\u003eJAMA Neurol\u003c/em\u003e. 2017;74(5):557\u0026ndash;566. https://doi.org/10.1001/jamaneurol.2016.6117\u003c/li\u003e\n \u003cli\u003ePreische O, Schultz SA, Apel A, et al. Serum neurofilament dynamics predicts neurodegeneration and clinical progression in presymptomatic Alzheimer\u0026rsquo;s disease. \u003cem\u003eNat Med\u003c/em\u003e. 2019;25(2):277\u0026ndash;283. https://doi.org/10.1038/s41591-018-0304-3\u003c/li\u003e\n \u003cli\u003eBenedet AL, Mil\u0026agrave;-Alom\u0026agrave; M, Vrillon A, et al. Differences between plasma and cerebrospinal fluid glial fibrillary acidic protein levels across the Alzheimer disease continuum. \u003cem\u003eJAMA Neurol\u003c/em\u003e. 2021;78(12):1471\u0026ndash;1483. https://doi.org/10.1001/jamaneurol.2021.3671\u003c/li\u003e\n \u003cli\u003eJanelidze S, Teunissen CE, Zetterberg H, et al. Head-to-head comparison of 8 plasma amyloid-\u0026beta; 42/40 assays in Alzheimer disease. \u003cem\u003eJAMA Neurol\u003c/em\u003e. 2021;78(11):1375\u0026ndash;1382. https://doi.org/10.1001/jamaneurol.2021.3180\u003c/li\u003e\n \u003cli\u003eKarikari TK, Pascoal TA, Ashton NJ, et al. Blood phosphorylated tau 181 as a biomarker for Alzheimer\u0026rsquo;s disease: a diagnostic performance and prediction modelling study using data from four prospective cohorts. \u003cem\u003eLancet Neurol\u003c/em\u003e. 2020;19(5):422\u0026ndash;433. https://doi.org/10.1016/S1474-4422(20)30071-5\u003c/li\u003e\n \u003cli\u003ePalmqvist S, Tideman P, Cullen N, et al. Prediction of future Alzheimer\u0026rsquo;s disease dementia using plasma phospho-tau combined with other accessible measures. \u003cem\u003eNat Med\u003c/em\u003e. 2021;27(6):1034\u0026ndash;1042. https://doi.org/10.1038/s41591-021-01348-z\u003c/li\u003e\n \u003cli\u003eU.S. Food and Drug Administration. FDA qualifies first blood-based biomarker test to assess risk of progression to Alzheimer\u0026rsquo;s disease. FDA News Release. July 31, 2023. https://www.fda.gov/news-events/press-announcements/fda-qualifies-first-blood-based-biomarker-test-assess-risk-progression-alzheimers-disease\u003c/li\u003e\n \u003cli\u003eSweeney MD, Montagne A, Sagare AP, et al. Vascular dysfunction-The disregarded partner of Alzheimer\u0026rsquo;s disease. \u003cem\u003eAlzheimers Dement\u003c/em\u003e. 2019;15(1):158\u0026ndash;167. https://doi.org/10.1016/j.jalz.2018.07.222\u003c/li\u003e\n \u003cli\u003eZlokovic BV. Neurovascular pathways to neurodegeneration in Alzheimer\u0026rsquo;s disease and other disorders. \u003cem\u003eNat Rev Neurosci\u003c/em\u003e. 2011;12(12):723\u0026ndash;738. https://doi.org/10.1038/nrn3114\u003c/li\u003e\n \u003cli\u003eNation DA, Sweeney MD, Montagne A, et al. Blood-brain barrier breakdown is an early biomarker of human cognitive dysfunction. \u003cem\u003eNat Med\u003c/em\u003e. 2019;25(2):270\u0026ndash;276. https://doi.org/10.1038/s41591-018-0297-y\u003c/li\u003e\n \u003cli\u003eO\u0026rsquo;Rourke MF, Safar ME. Relationship between aortic stiffening and microvascular disease in brain and kidney: cause and logic of therapy. \u003cem\u003eHypertension\u003c/em\u003e. 2005;46(1):200\u0026ndash;204. https://doi.org/10.1161/01.HYP.0000168052.00426.65\u003c/li\u003e\n \u003cli\u003eIadecola C. The pathobiology of vascular dementia. \u003cem\u003eNeuron\u003c/em\u003e. 2013;80(4):844\u0026ndash;866. https://doi.org/10.1016/j.neuron.2013.10.008\u003c/li\u003e\n \u003cli\u003eToth P, Tarantini S, Csiszar A, Ungvari Z. Functional vascular contributions to cognitive impairment and dementia: mechanisms and consequences of cerebral autoregulatory dysfunction, endothelial impairment, and neurovascular uncoupling in aging. \u003cem\u003eAm J Physiol Heart Circ Physiol\u003c/em\u003e. 2017;312(1):H1\u0026ndash;H20. https://doi.org/10.1152/ajpheart.00581.2016\u003c/li\u003e\n \u003cli\u003eGhiso J, Shayo M, Calero M, et al. Systemic catabolism of Alzheimer\u0026rsquo;s A\u0026beta;40 and A\u0026beta;42. \u003cem\u003eJ Biol Chem\u003c/em\u003e. 2004;279(44):45897\u0026ndash;45908. https://doi.org/10.1074/jbc.M407668200\u003c/li\u003e\n \u003cli\u003eZlokovic BV, Deane R, Sagare AP, Bell RD, Winkler EA. Low-density lipoprotein receptor-related protein-1: a serial clearance homeostatic mechanism controlling Alzheimer\u0026rsquo;s amyloid \u0026beta;-peptide elimination from the brain. \u003cem\u003eJ Neurochem\u003c/em\u003e. 2010;115(5):1077\u0026ndash;1089. https://doi.org/10.1111/j.1471-4159.2010.07002.x\u003c/li\u003e\n \u003cli\u003eTarasoff-Conway JM, Carare RO, Osorio RS, et al. Clearance systems in the brain-implications for Alzheimer disease. \u003cem\u003eNat Rev Neurol\u003c/em\u003e. 2015;11(8):457\u0026ndash;470. https://doi.org/10.1038/nrneurol.2015.119\u003c/li\u003e\n \u003cli\u003eHeneka MT, Carson MJ, El Khoury J, et al. Neuroinflammation in Alzheimer\u0026rsquo;s disease. \u003cem\u003eLancet Neurol\u003c/em\u003e. 2015;14(4):388\u0026ndash;405. https://doi.org/10.1016/S1474-4422(15)70016-5\u003c/li\u003e\n \u003cli\u003eKinney JW, Bemiller SM, Murtishaw AS, Leisgang AM, Salazar AM, Lamb BT. Inflammation as a central mechanism in Alzheimer\u0026rsquo;s disease. \u003cem\u003eAlzheimers Dement (N Y)\u003c/em\u003e. 2018;4:575\u0026ndash;590. https://doi.org/10.1016/j.trci.2018.06.014\u003c/li\u003e\n \u003cli\u003eFranceschi C, Campisi J. Chronic inflammation (inflammaging) and its potential contribution to age-associated diseases. \u003cem\u003eJ Gerontol A Biol Sci Med Sci\u003c/em\u003e. 2014;69 Suppl 1:S4\u0026ndash;S9. https://doi.org/10.1093/gerona/glu057\u003c/li\u003e\n \u003cli\u003eArnold SE, Arvanitakis Z, Macauley-Rambach SL, et al. Brain insulin resistance in type 2 diabetes and Alzheimer disease: concepts and conundrums. \u003cem\u003eNat Rev Neurol\u003c/em\u003e. 2018;14(3):168\u0026ndash;181. https://doi.org/10.1038/nrneurol.2017.185\u003c/li\u003e\n \u003cli\u003eDe Felice FG, Ferreira ST. Inflammation, defective insulin signaling, and mitochondrial dysfunction as common molecular denominators connecting type 2 diabetes to Alzheimer disease. \u003cem\u003eDiabetes\u003c/em\u003e. 2014;63(7):2262\u0026ndash;2272. https://doi.org/10.2337/db13-1954\u003c/li\u003e\n \u003cli\u003eBiessels GJ, Despa F. Cognitive decline and dementia in diabetes mellitus: mechanisms and clinical implications. \u003cem\u003eNat Rev Endocrinol\u003c/em\u003e. 2018;14(10):591\u0026ndash;604. https://doi.org/10.1038/s41574-018-0048-7\u003c/li\u003e\n \u003cli\u003eSwerdlow RH. Mitochondria and mitochondrial cascades in Alzheimer\u0026rsquo;s disease. \u003cem\u003eJ Alzheimers Dis\u003c/em\u003e. 2018;62(3):1403\u0026ndash;1416. https://doi.org/10.3233/JAD-170585\u003c/li\u003e\n \u003cli\u003eT\u0026ouml;nnies E, Trushina E. Oxidative stress, synaptic dysfunction, and Alzheimer\u0026rsquo;s disease. \u003cem\u003eJ Alzheimers Dis\u003c/em\u003e. 2017;57(4):1105\u0026ndash;1121. https://doi.org/10.3233/JAD-161088\u003c/li\u003e\n \u003cli\u003eForbes JM, Thorburn DR. Mitochondrial dysfunction in diabetic kidney disease. \u003cem\u003eNat Rev Nephrol\u003c/em\u003e. 2018;14(5):291\u0026ndash;312. https://doi.org/10.1038/nrneph.2018.9\u003c/li\u003e\n \u003cli\u003eKaeser SA, Herzig MC, Coomaraswamy J, et al. Cystatin C modulates cerebral beta-amyloidosis. \u003cem\u003eNat Genet\u003c/em\u003e. 2007;39(12):1437\u0026ndash;1439. https://doi.org/10.1038/ng.2007.23\u003c/li\u003e\n \u003cli\u003eDharnidharka VR, Kwon C, Stevens G. Serum cystatin C is superior to serum creatinine as a marker of kidney function: a meta-analysis. \u003cem\u003eAm J Kidney Dis\u003c/em\u003e. 2002;40(2):221\u0026ndash;226. https://doi.org/10.1053/ajkd.2002.34487\u003c/li\u003e\n \u003cli\u003eStevens LA, Schmid CH, Greene T, et al. Factors other than glomerular filtration rate affect serum cystatin C levels. \u003cem\u003eKidney Int\u003c/em\u003e. 2009;75(6):652\u0026ndash;660. https://doi.org/10.1038/ki.2008.638\u003c/li\u003e\n \u003cli\u003eInker LA, Schmid CH, Tighiouart H, et al. Estimating glomerular filtration rate from serum creatinine and cystatin C. \u003cem\u003eN Engl J Med\u003c/em\u003e. 2012;367(1):20\u0026ndash;29. https://doi.org/10.1056/NEJMoa1114248\u003c/li\u003e\n \u003cli\u003eSelenica ML, Wang X, Ostergaard-Pedersen L, et al. Cystatin C reduces the in vitro formation of soluble A\u0026beta;1-42 oligomers and protofibrils. \u003cem\u003eScand J Clin Lab Invest\u003c/em\u003e. 2007;67(2):179\u0026ndash;190. https://doi.org/10.1080/00365510601009738\u003c/li\u003e\n \u003cli\u003eLevy E, Jaskolski M, Grubb A. The role of cystatin C in cerebral amyloid angiopathy and stroke: cell biology and animal models. \u003cem\u003eBrain Pathol\u003c/em\u003e. 2006;16(1):60\u0026ndash;70. https://doi.org/10.1111/j.1750-3639.2006.tb00562.x\u003c/li\u003e\n \u003cli\u003eSundel\u0026ouml;f J, Sundstr\u0026ouml;m J, Hansson O, et al. Higher cathepsin B levels in plasma in Alzheimer\u0026rsquo;s disease compared to healthy controls. \u003cem\u003eJ Alzheimers Dis\u003c/em\u003e. 2010;22(4):1223\u0026ndash;1230. https://doi.org/10.3233/JAD-2010-101023\u003c/li\u003e\n \u003cli\u003eHelmer C, Stengel B, Metzger M, et al. chronic kidney disease, cognitive decline, and incident dementia: the 3C Study. \u003cem\u003eNeurology\u003c/em\u003e. 2011;77(23):2043\u0026ndash;2051. https://doi.org/10.1212/WNL.0b013e31823b4765\u003c/li\u003e\n \u003cli\u003eKurella Tamura M, Yaffe K. Dementia and cognitive impairment in ESRD: diagnostic and therapeutic strategies. \u003cem\u003eKidney Int\u003c/em\u003e. 2011;79(1):14\u0026ndash;22. https://doi.org/10.1038/ki.2010.336\u003c/li\u003e\n \u003cli\u003eDeckers K, Camerino I, van Boxtel MP, et al. Dementia risk in renal dysfunction: A systematic review and meta-analysis of prospective studies. \u003cem\u003eNeurology\u003c/em\u003e. 2017;88(2):198\u0026ndash;208. https://doi.org/10.1212/WNL.0000000000003482\u003c/li\u003e\n \u003cli\u003eKhalil M, Pirpamer L, Hofer E, et al. Serum neurofilament light levels in normal aging and their association with morphologic brain changes. \u003cem\u003eNat Commun\u003c/em\u003e. 2020;11(1):812. https://doi.org/10.1038/s41467-020-14612-6\u003c/li\u003e\n \u003cli\u003eOlsson B, Lautner R, Andreasson U, et al. CSF and blood biomarkers for the diagnosis of Alzheimer\u0026rsquo;s disease: a systematic review and meta-analysis. \u003cem\u003eLancet Neurol\u003c/em\u003e. 2016;15(7):673\u0026ndash;684. https://doi.org/10.1016/S1474-4422(16)00070-3\u003c/li\u003e\n \u003cli\u003eBridel C, van Wieringen WN, Zetterberg H, et al. Diagnostic value of cerebrospinal fluid neurofilament light protein in neurology: a systematic review and meta-analysis. \u003cem\u003eJAMA Neurol\u003c/em\u003e. 2019;76(9):1035\u0026ndash;1048. https://doi.org/10.1001/jamaneurol.2019.1534\u003c/li\u003e\n \u003cli\u003ePage MJ, McKenzie JE, Bossuyt PM, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. \u003cem\u003eBMJ\u003c/em\u003e. 2021;372:n71. https://doi.org/10.1136/bmj.n71\u003c/li\u003e\n \u003cli\u003eWells GA, Shea B, O\u0026rsquo;Connell D, et al.\u0026nbsp;The Newcastle-Ottawa Scale (NOS) for assessing the quality of nonrandomised studies in meta-analyses. Ottawa Hospital Research Institute. http://www.ohri.ca/programs/clinical_epidemiology/oxford.asp\u003c/li\u003e\n \u003cli\u003eSchwarzer G, Carpenter JR, R\u0026uuml;cker G. \u003cem\u003eMeta-Analysis with R\u003c/em\u003e. Springer; 2015.\u003c/li\u003e\n \u003cli\u003eViechtbauer W. Conducting meta-analyses in R with the metafor package. \u003cem\u003eJ Stat Softw\u003c/em\u003e. 2010;36(3):1\u0026ndash;48. https://doi.org/10.18637/jss.v036.i03\u003c/li\u003e\n \u003cli\u003eCohen J. \u003cem\u003eStatistical Power Analysis for the Behavioral Sciences\u003c/em\u003e. 2nd ed.\u0026nbsp;Lawrence Erlbaum Associates; 1988.\u003c/li\u003e\n \u003cli\u003eWan X, Wang W, Liu J, Tong T. Estimating the sample mean and standard deviation from the sample size, median, range and/or interquartile range. \u003cem\u003eBMC Med Res Methodol\u003c/em\u003e. 2014;14:135. https://doi.org/10.1186/1471-2288-14-135\u003c/li\u003e\n \u003cli\u003eLuo D, Wan X, Liu J, Tong T. Optimally estimating the sample mean from the sample size, median, mid-range, and/or mid-quartile range. \u003cem\u003eStat Methods Med Res\u003c/em\u003e. 2018;27(6):1785\u0026ndash;1805. https://doi.org/10.1177/0962280216669183\u003c/li\u003e\n \u003cli\u003eBorenstein M, Hedges LV, Higgins JPT, Rothstein HR. \u003cem\u003eIntroduction to Meta-Analysis\u003c/em\u003e. John Wiley \u0026amp; Sons; 2009.\u003c/li\u003e\n \u003cli\u003eDerSimonian R, Laird N. Meta-analysis in clinical trials. \u003cem\u003eControl Clin Trials\u003c/em\u003e. 1986;7(3):177\u0026ndash;188. https://doi.org/10.1016/0197-2456(86)90046-2\u003c/li\u003e\n \u003cli\u003eHartung J, Knapp G. A refined method for the meta-analysis of controlled clinical trials with binary outcome. \u003cem\u003eStat Med\u003c/em\u003e. 2001;20(24):3875\u0026ndash;3889. https://doi.org/10.1002/sim.1009\u003c/li\u003e\n \u003cli\u003eHiggins JPT, Thompson SG, Deeks JJ, Altman DG. Measuring inconsistency in meta-analyses. \u003cem\u003eBMJ\u003c/em\u003e. 2003;327(7414):557\u0026ndash;560. https://doi.org/10.1136/bmj.327.7414.557\u003c/li\u003e\n \u003cli\u003eIntHout J, Ioannidis JP, Rovers MM, Goeman JJ. Plea for routinely presenting prediction intervals in meta-analysis. \u003cem\u003eBMJ Open\u003c/em\u003e. 2016;6(7):e010247. https://doi.org/10.1136/bmjopen-2015-010247\u003c/li\u003e\n \u003cli\u003eReitsma JB, Glas AS, Rutjes AW, Scholten RJ, Bossuyt PM, Zwinderman AH. Bivariate analysis of sensitivity and specificity produces informative summary measures in diagnostic reviews. \u003cem\u003eJ Clin Epidemiol\u003c/em\u003e. 2005;58(10):982\u0026ndash;990. https://doi.org/10.1016/j.jclinepi.2005.02.022\u003c/li\u003e\n \u003cli\u003eRutter CM, Gatsonis CA. A hierarchical regression approach to meta-analysis of diagnostic test accuracy evaluations. \u003cem\u003eStat Med\u003c/em\u003e. 2001;20(19):2865\u0026ndash;2884. https://doi.org/10.1002/sim.942\u003c/li\u003e\n \u003cli\u003eEgger M, Davey Smith G, Schneider M, Minder C. Bias in meta-analysis detected by a simple, graphical test. \u003cem\u003eBMJ\u003c/em\u003e. 1997;315(7109):629\u0026ndash;634. https://doi.org/10.1136/bmj.315.7109.629\u003c/li\u003e\n \u003cli\u003eDuval S, Tweedie R. Trim and fill: A simple funnel-plot-based method of testing and adjusting for publication bias in meta-analysis. \u003cem\u003eBiometrics\u003c/em\u003e. 2000;56(2):455\u0026ndash;463. https://doi.org/10.1111/j.0006-341x.2000.00455.x\u003c/li\u003e\n \u003cli\u003eBegg CB, Mazumdar M. Operating characteristics of a rank correlation test for publication bias. \u003cem\u003eBiometrics\u003c/em\u003e. 1994;50(4):1088\u0026ndash;1101. https://doi.org/10.2307/2533446\u003c/li\u003e\n \u003cli\u003eRizzoni D, Agabiti Rosei C, De Ciuceis C, et al. Structural alterations in small resistance arteries in obesity. \u003cem\u003eBasic Clin Pharmacol Toxicol\u003c/em\u003e. 2012;110(1):56\u0026ndash;62. https://doi.org/10.1111/j.1742-7843.2011.00793.x\u003c/li\u003e\n \u003cli\u003eWardlaw JM, Smith C, Dichgans M. Small vessel disease: mechanisms and clinical implications. \u003cem\u003eLancet Neurol\u003c/em\u003e. 2019;18(7):684\u0026ndash;696. https://doi.org/10.1016/S1474-4422(19)30079-1\u003c/li\u003e\n \u003cli\u003eDeanfield JE, Halcox JP, Rabelink TJ. Endothelial function and dysfunction: testing and clinical relevance. \u003cem\u003eCirculation\u003c/em\u003e. 2007;115(10):1285\u0026ndash;1295. https://doi.org/10.1161/CIRCULATIONAHA.106.652859\u003c/li\u003e\n \u003cli\u003eXu J, Shi GP. Vascular wall extracellular matrix proteins and vascular diseases. \u003cem\u003eBiochim Biophys Acta\u003c/em\u003e. 2014;1842(11):2106\u0026ndash;2119. https://doi.org/10.1016/j.bbadis.2014.07.008\u003c/li\u003e\n \u003cli\u003eWada M, Nagasawa H, Iseki C, et al. Cerebral small vessel disease and chronic kidney disease (CKD): results of a cross-sectional study in community-based Japanese elderly. \u003cem\u003eJ Neurol Sci\u003c/em\u003e. 2008;272(1-2):36\u0026ndash;42. https://doi.org/10.1016/j.jns.2008.04.029\u003c/li\u003e\n \u003cli\u003eIkram MA, Vernooij MW, Hofman A, Niessen WJ, van der Lugt A, Breteler MM. Kidney function is related to cerebral small vessel disease. \u003cem\u003eStroke\u003c/em\u003e. 2008;39(1):55\u0026ndash;61. https://doi.org/10.1161/STROKEAHA.107.493494\u003c/li\u003e\n \u003cli\u003eSundel\u0026ouml;f J, Arnl\u0026ouml;v J, Ingelsson E, et al. Serum cystatin C and the risk of Alzheimer disease in elderly men. \u003cem\u003eNeurology\u003c/em\u003e. 2008;71(14):1072\u0026ndash;1079. https://doi.org/10.1212/01.wnl.0000326894.40353.93\u003c/li\u003e\n \u003cli\u003eYaffe K, Lindquist K, Shlipak MG, et al. Cystatin C as a marker of cognitive function in elders: findings from the health ABC study. \u003cem\u003eAnn Neurol\u003c/em\u003e. 2008;63(6):798\u0026ndash;802. https://doi.org/10.1002/ana.21383\u003c/li\u003e\n \u003cli\u003ePantoni L. Cerebral small vessel disease: from pathogenesis and clinical characteristics to therapeutic challenges. \u003cem\u003eLancet Neurol\u003c/em\u003e. 2010;9(7):689\u0026ndash;701. https://doi.org/10.1016/S1474-4422(10)70104-6\u003c/li\u003e\n \u003cli\u003eDichgans M, Leys D. Vascular cognitive impairment. \u003cem\u003eCirc Res\u003c/em\u003e. 2017;120(3):573\u0026ndash;591. https://doi.org/10.1161/CIRCRESAHA.116.308426\u003c/li\u003e\n \u003cli\u003eShi Y, Wardlaw JM. Update on cerebral small vessel disease: a dynamic whole-brain disease. \u003cem\u003eStroke Vasc Neurol\u003c/em\u003e. 2016;1(3):83\u0026ndash;92. https://doi.org/10.1136/svn-2016-000035\u003c/li\u003e\n \u003cli\u003eGorelick PB, Scuteri A, Black SE, et al. Vascular contributions to cognitive impairment and dementia: a statement for healthcare professionals from the American Heart Association/American Stroke Association. \u003cem\u003eStroke\u003c/em\u003e. 2011;42(9):2672\u0026ndash;2713. https://doi.org/10.1161/STR.0b013e3182299496\u003c/li\u003e\n \u003cli\u003eKapasi A, DeCarli C, Schneider JA. Impact of multiple pathologies on the threshold for clinically overt dementia. \u003cem\u003eActa Neuropathol\u003c/em\u003e. 2017;134(2):171\u0026ndash;186. https://doi.org/10.1007/s00401-017-1717-7\u003c/li\u003e\n \u003cli\u003eMontagne A, Nation DA, Sagare AP, et al. APOE4 leads to blood-brain barrier dysfunction predicting cognitive decline. \u003cem\u003eNature\u003c/em\u003e. 2020;581(7806):71\u0026ndash;76. https://doi.org/10.1038/s41586-020-2247-3\u003c/li\u003e\n \u003cli\u003eIliff JJ, Wang M, Liao Y, et al. A paravascular pathway facilitates CSF flow through the brain parenchyma and the clearance of interstitial solutes, including amyloid \u0026beta;. \u003cem\u003eSci Transl Med\u003c/em\u003e. 2012;4(147):147ra111. https://doi.org/10.1126/scitranslmed.3003748\u003c/li\u003e\n \u003cli\u003eRasmussen MK, Mestre H, Nedergaard M. The glymphatic pathway in neurological disorders. \u003cem\u003eLancet Neurol\u003c/em\u003e. 2018;17(11):1016\u0026ndash;1024. https://doi.org/10.1016/S1474-4422(18)30318-1\u003c/li\u003e\n \u003cli\u003ePeng W, Achariyar TM, Li B, et al. Suppression of glymphatic fluid transport in a mouse model of Alzheimer\u0026rsquo;s disease. \u003cem\u003eNeurobiol Dis\u003c/em\u003e. 2016;93:215\u0026ndash;225. https://doi.org/10.1016/j.nbd.2016.05.015\u003c/li\u003e\n \u003cli\u003eNedergaard M, Goldman SA. Glymphatic failure as a final common pathway to dementia. \u003cem\u003eScience\u003c/em\u003e. 2020;370(6512):50\u0026ndash;56. https://doi.org/10.1126/science.abb8739\u003c/li\u003e\n \u003cli\u003eJessen NA, Munk AS, Lundgaard I, Nedergaard M. The glymphatic system: a beginner\u0026rsquo;s guide. \u003cem\u003eNeurochem Res\u003c/em\u003e. 2015;40(12):2583\u0026ndash;2599. https://doi.org/10.1007/s11064-015-1581-6\u003c/li\u003e\n \u003cli\u003eMestre H, Kostrikov S, Mehta RI, Nedergaard M. Perivascular spaces, glymphatic dysfunction, and small vessel disease. \u003cem\u003eClin Sci (Lond)\u003c/em\u003e. 2017;131(17):2257\u0026ndash;2274. https://doi.org/10.1042/CS20160381\u003c/li\u003e\n \u003cli\u003eBell RD, Zlokovic BV. Neurovascular mechanisms and blood-brain barrier disorder in Alzheimer\u0026rsquo;s disease. \u003cem\u003eActa Neuropathol\u003c/em\u003e. 2009;118(1):103\u0026ndash;113. https://doi.org/10.1007/s00401-009-0522-3\u003c/li\u003e\n \u003cli\u003eDeane R, Du Yan S, Submamaryan RK, et al. RAGE mediates amyloid-beta peptide transport across the blood-brain barrier and accumulation in brain. \u003cem\u003eNat Med\u003c/em\u003e. 2003;9(7):907\u0026ndash;913. https://doi.org/10.1038/nm890\u003c/li\u003e\n \u003cli\u003eSilverberg GD, Miller MC, Messier AA, et al. Amyloid deposition and influx transporter expression at the blood-brain barrier increase in normal aging. \u003cem\u003eJ Neuropathol Exp Neurol\u003c/em\u003e. 2010;69(1):98\u0026ndash;108. https://doi.org/10.1097/NEN.0b013e3181c8ad2f\u003c/li\u003e\n \u003cli\u003eRoberts KF, Elbert DL, Kasten TP, et al. Amyloid-\u0026beta; efflux from the central nervous system into the plasma. \u003cem\u003eAnn Neurol\u003c/em\u003e. 2014;76(6):837\u0026ndash;844. https://doi.org/10.1002/ana.24270\u003c/li\u003e\n \u003cli\u003eSagare AP, Bell RD, Zlokovic BV. Neurovascular dysfunction and faulty amyloid \u0026beta;-peptide clearance in Alzheimer disease. \u003cem\u003eCold Spring Harb Perspect Med\u003c/em\u003e. 2012;2(10):a011452. https://doi.org/10.1101/cshperspect.a011452\u003c/li\u003e\n \u003cli\u003eEtgen T, Chonchol M, F\u0026ouml;rstl H, Sander D. Chronic kidney disease and cognitive impairment: a systematic review and meta-analysis. \u003cem\u003eAm J Nephrol\u003c/em\u003e. 2012;35(5):474\u0026ndash;482. https://doi.org/10.1159/000338135\u003c/li\u003e\n \u003cli\u003eKurella Tamura M, Muntner P, Wadley V, et al. Albuminuria, kidney function, and the incidence of cognitive impairment among adults in the United States. \u003cem\u003eAm J Kidney Dis\u003c/em\u003e. 2011;58(5):756\u0026ndash;763. https://doi.org/10.1053/j.ajkd.2011.05.027\u003c/li\u003e\n \u003cli\u003eGeorgakis MK, Dimitriou NG, Karalexi MA, et al. Albuminuria in association with cognitive function and dementia: a systematic review and meta-analysis. \u003cem\u003eJ Am Geriatr Soc\u003c/em\u003e. 2017;65(6):1190\u0026ndash;1198. https://doi.org/10.1111/jgs.14750\u003c/li\u003e\n \u003cli\u003eSchreiber S, Bueche CZ, Garz C, Braun H. Blood brain barrier breakdown as the starting point of cerebral small vessel disease? - New insights from a rat model. \u003cem\u003eExp Transl Stroke Med\u003c/em\u003e. 2013;5(1):4. https://doi.org/10.1186/2040-7378-5-4\u003c/li\u003e\n \u003cli\u003eLau WL, Nunes ACF, Vasilevko V, et al. Chronic kidney disease increases cerebral microbleeds in mouse and man. \u003cem\u003eTransl Stroke Res\u003c/em\u003e. 2020;11(1):122\u0026ndash;134. https://doi.org/10.1007/s12975-019-00698-8\u003c/li\u003e\n \u003cli\u003eBugnicourt JM, Godefroy O, Chillon JM, Choukroun G, Massy ZA. Cognitive disorders and dementia in CKD: the neglected kidney-brain axis. \u003cem\u003eJ Am Soc Nephrol\u003c/em\u003e. 2013;24(3):353\u0026ndash;363. https://doi.org/10.1681/ASN.2012050536\u003c/li\u003e\n \u003cli\u003eTurk V, Stoka V, Vasiljeva O, et al. Cysteine cathepsins: from structure, function and regulation to new frontiers. \u003cem\u003eBiochim Biophys Acta\u003c/em\u003e. 2012;1824(1):68\u0026ndash;88. https://doi.org/10.1016/j.bbapap.2011.10.002\u003c/li\u003e\n \u003cli\u003eSastre M, Calero M, Pawlik M, et al. Binding of cystatin C to Alzheimer\u0026rsquo;s amyloid beta inhibits in vitro amyloid fibril formation. \u003cem\u003eNeurobiol Aging\u003c/em\u003e. 2004;25(8):1033\u0026ndash;1043. https://doi.org/10.1016/j.neurobiolaging.2003.11.006\u003c/li\u003e\n \u003cli\u003eFinckh U, von der Kammer H, Velden J, et al. Genetic association of a cystatin C gene polymorphism with late-onset Alzheimer disease. \u003cem\u003eArch Neurol\u003c/em\u003e. 2000;57(11):1579\u0026ndash;1583. https://doi.org/10.1001/archneur.57.11.1579\u003c/li\u003e\n \u003cli\u003eCrawford FC, Freeman MJ, Schinka JA, et al. A polymorphism in the cystatin C gene is a novel risk factor for late-onset Alzheimer\u0026rsquo;s disease. \u003cem\u003eNeurology\u003c/em\u003e. 2000;55(6):763\u0026ndash;768. https://doi.org/10.1212/wnl.55.6.763\u003c/li\u003e\n \u003cli\u003eAkiyama H, Barger S, Barnum S, et al. Inflammation and Alzheimer\u0026rsquo;s disease. \u003cem\u003eNeurobiol Aging\u003c/em\u003e. 2000;21(3):383\u0026ndash;421. https://doi.org/10.1016/s0197-4580(00)00124-x\u003c/li\u003e\n \u003cli\u003eSwardfager W, Lanct\u0026ocirc;t K, Rothenburg L, Wong A, Cappell J, Herrmann N. A meta-analysis of cytokines in Alzheimer\u0026rsquo;s disease. \u003cem\u003eBiol Psychiatry\u003c/em\u003e. 2010;68(10):930\u0026ndash;941. https://doi.org/10.1016/j.biopsych.2010.06.012\u003c/li\u003e\n \u003cli\u003eFerrucci L, Fabbri E. Inflammageing: chronic inflammation in ageing, cardiovascular disease, and frailty. \u003cem\u003eNat Rev Cardiol\u003c/em\u003e. 2018;15(9):505\u0026ndash;522. https://doi.org/10.1038/s41569-018-0064-2\u003c/li\u003e\n \u003cli\u003eFurman D, Campisi J, Verdin E, et al. Chronic inflammation in the etiology of disease across the life span. \u003cem\u003eNat Med\u003c/em\u003e. 2019;25(12):1822\u0026ndash;1832. https://doi.org/10.1038/s41591-019-0675-0\u003c/li\u003e\n \u003cli\u003eKang DH, Anderson S, Kim YG, et al. Impaired angiogenesis in the aging kidney: vascular endothelial growth factor and thrombospondin-1 in renal disease. \u003cem\u003eAm J Kidney Dis\u003c/em\u003e. 2001;37(3):601\u0026ndash;611. https://doi.org/10.1053/ajkd.2001.22087\u003c/li\u003e\n \u003cli\u003eOeckl P, Halbgebauer S, Anderl-Straub S, et al. Glial fibrillary acidic protein in serum is increased in Alzheimer\u0026rsquo;s disease and correlates with cognitive impairment. \u003cem\u003eJ Alzheimers Dis\u003c/em\u003e. 2019;67(2):481\u0026ndash;488. https://doi.org/10.3233/JAD-180325\u003c/li\u003e\n \u003cli\u003eChatterjee P, Pedrini S, Stoops E, et al. Plasma glial fibrillary acidic protein is elevated in cognitively normal older adults at risk of Alzheimer\u0026rsquo;s disease. \u003cem\u003eTransl Psychiatry\u003c/em\u003e. 2021;11(1):27. https://doi.org/10.1038/s41398-020-01137-1\u003c/li\u003e\n \u003cli\u003ePereira JB, Janelidze S, Smith R, et al. Plasma GFAP is an early marker of amyloid-\u0026beta; but not tau pathology in Alzheimer\u0026rsquo;s disease. \u003cem\u003eBrain\u003c/em\u003e. 2021;144(11):3505\u0026ndash;3516. https://doi.org/10.1093/brain/awab223\u003c/li\u003e\n \u003cli\u003eLiddelow SA, Guttenplan KA, Clarke LE, et al. Neurotoxic reactive astrocytes are induced by activated microglia. \u003cem\u003eNature\u003c/em\u003e. 2017;541(7638):481\u0026ndash;487. https://doi.org/10.1038/nature21029\u003c/li\u003e\n \u003cli\u003eEscartin C, Galea E, Lakatos A, et al. Reactive astrocyte nomenclature, definitions, and future directions. \u003cem\u003eNat Neurosci\u003c/em\u003e. 2021;24(3):312\u0026ndash;325. https://doi.org/10.1038/s41593-020-00783-4\u003c/li\u003e\n \u003cli\u003eSofroniew MV. Astrocyte reactivity: subtypes, states, and functions in CNS innate immunity. \u003cem\u003eTrends Immunol\u003c/em\u003e. 2020;41(9):758\u0026ndash;770. https://doi.org/10.1016/j.it.2020.07.004\u003c/li\u003e\n \u003cli\u003eBonventre JV. Kidney injury molecule-1 (KIM-1): a urinary biomarker and much more. \u003cem\u003eNephrol Dial Transplant\u003c/em\u003e. 2009;24(11):3265\u0026ndash;3268. https://doi.org/10.1093/ndt/gfp010\u003c/li\u003e\n \u003cli\u003eHaase M, Bellomo R, Devarajan P, Schlattmann P, Haase-Fielitz A; NGAL Meta-analysis Investigator Group. Accuracy of neutrophil gelatinase-associated lipocalin (NGAL) in diagnosis and prognosis in acute kidney injury: a systematic review and meta-analysis. \u003cem\u003eAm J Kidney Dis\u003c/em\u003e. 2009;54(6):1012\u0026ndash;1024. https://doi.org/10.1053/j.ajkd.2009.07.020\u003c/li\u003e\n \u003cli\u003eCoca SG, Yalavarthy R, Concato J, Parikh CR. Biomarkers for the diagnosis and risk stratification of acute kidney injury: a systematic review. \u003cem\u003eKidney Int\u003c/em\u003e. 2008;73(9):1008\u0026ndash;1016. https://doi.org/10.1038/sj.ki.5002729\u003c/li\u003e\n \u003cli\u003eBanks WA, Erickson MA. The blood-brain barrier and immune function and dysfunction. \u003cem\u003eNeurobiol Dis\u003c/em\u003e. 2010;37(1):26\u0026ndash;32. https://doi.org/10.1016/j.nbd.2009.07.031\u003c/li\u003e\n \u003cli\u003eVaratharaj A, Galea I. The blood-brain barrier in systemic inflammation. \u003cem\u003eBrain Behav Immun\u003c/em\u003e. 2017;60:1\u0026ndash;12. https://doi.org/10.1016/j.bbi.2016.03.010\u003c/li\u003e\n \u003cli\u003eErickson MA, Banks WA. Neuroimmune axes of the blood-brain barriers and blood-brain interfaces: bases for physiological regulation, disease states, and pharmacological interventions. \u003cem\u003ePharmacol Rev\u003c/em\u003e. 2018;70(2):278\u0026ndash;314. https://doi.org/10.1124/pr.117.014647\u003c/li\u003e\n \u003cli\u003eLiu M, Liang Y, Chigurupati S, et al. Acute kidney injury leads to inflammation and functional changes in the brain. \u003cem\u003eJ Am Soc Nephrol\u003c/em\u003e. 2008;19(7):1360\u0026ndash;1370. https://doi.org/10.1681/ASN.2007080901\u003c/li\u003e\n \u003cli\u003eNongnuch A, Panorchan K, Davenport A. Brain-kidney crosstalk. \u003cem\u003eCrit Care\u003c/em\u003e. 2014;18(3):225. https://doi.org/10.1186/cc13907\u003c/li\u003e\n \u003cli\u003eMazeraud A, Righy C, Bouchereau E, Benghanem S, Bozza FA, Sharshar T. Septic-associated encephalopathy: a comprehensive review. \u003cem\u003eNeurotherapeutics\u003c/em\u003e. 2020;17(2):392\u0026ndash;403. https://doi.org/10.1007/s13311-020-00862-1\u003c/li\u003e\n \u003cli\u003eKleinridders A, Ferris HA, Cai W, Kahn CR. Insulin action in brain regulates systemic metabolism and brain function. \u003cem\u003eDiabetes\u003c/em\u003e. 2014;63(7):2232\u0026ndash;2243. https://doi.org/10.2337/db14-0568\u003c/li\u003e\n \u003cli\u003eArtunc F, Schleicher E, Weigert C, Fritsche A, Stefan N, H\u0026auml;ring HU. The impact of insulin resistance on the kidney and vasculature. \u003cem\u003eNat Rev Nephrol\u003c/em\u003e. 2016;12(12):721\u0026ndash;737. https://doi.org/10.1038/nrneph.2016.145\u003c/li\u003e\n \u003cli\u003eBiessels GJ, Staekenborg S, Brunner E, Brayne C, Scheltens P. Risk of dementia in diabetes mellitus: a systematic review. \u003cem\u003eLancet Neurol\u003c/em\u003e. 2006;5(1):64\u0026ndash;74. https://doi.org/10.1016/S1474-4422(05)70284-2\u003c/li\u003e\n \u003cli\u003eGudala K, Bansal D, Schifano F, Bhansali A. Diabetes mellitus and risk of dementia: a meta-analysis of prospective observational studies. \u003cem\u003eJ Diabetes Investig\u003c/em\u003e. 2013;4(6):640\u0026ndash;650. https://doi.org/10.1111/jdi.12087\u003c/li\u003e\n \u003cli\u003eCheng G, Huang C, Deng H, Wang H. Diabetes as a risk factor for dementia and mild cognitive impairment: a meta-analysis of longitudinal studies. \u003cem\u003eIntern Med J\u003c/em\u003e. 2012;42(5):484\u0026ndash;491. https://doi.org/10.1111/j.1445-5994.2012.02758.x\u003c/li\u003e\n \u003cli\u003eTakeuchi M, Yamagishi S. Involvement of toxic AGEs (TAGE) in the pathogenesis of diabetic vascular complications and Alzheimer\u0026rsquo;s disease. \u003cem\u003eJ Alzheimers Dis\u003c/em\u003e. 2009;16(4):845\u0026ndash;858. https://doi.org/10.3233/JAD-2009-0974\u003c/li\u003e\n \u003cli\u003eCrane PK, Walker R, Hubbard RA, et al. Glucose levels and risk of dementia. \u003cem\u003eN Engl J Med\u003c/em\u003e. 2013;369(6):540\u0026ndash;548. https://doi.org/10.1056/NEJMoa1215740\u003c/li\u003e\n \u003cli\u003eYaffe K, Falvey C, Hamilton N, et al. Diabetes, glucose control, and 9-year cognitive decline among older adults without dementia. \u003cem\u003eArch Neurol\u003c/em\u003e. 2012;69(9):1170\u0026ndash;1175. https://doi.org/10.1001/archneurol.2012.1117\u003c/li\u003e\n \u003cli\u003eSharma K. Mitochondrial hormesis and diabetic complications. \u003cem\u003eDiabetes\u003c/em\u003e. 2015;64(3):663\u0026ndash;672. https://doi.org/10.2337/db14-0874\u003c/li\u003e\n \u003cli\u003eSalminen A, Kaarniranta K, Kauppinen A. AMPK and HIF signaling pathways regulate both longevity and cancer growth: the good news and the bad news about survival mechanisms. \u003cem\u003eBiogerontology\u003c/em\u003e. 2016;17(4):655\u0026ndash;680. https://doi.org/10.1007/s10522-016-9655-7\u003c/li\u003e\n \u003cli\u003eCaccamo A, Majumder S, Richardson A, Strong R, Oddo S. Molecular interplay between mammalian target of rapamycin (mTOR), amyloid-beta, and Tau: effects on cognitive impairments. \u003cem\u003eJ Biol Chem\u003c/em\u003e. 2010;285(17):13107\u0026ndash;13120. https://doi.org/10.1074/jbc.M110.100420\u003c/li\u003e\n \u003cli\u003eNixon RA. The role of autophagy in neurodegenerative disease. \u003cem\u003eNat Med\u003c/em\u003e. 2013;19(8):983\u0026ndash;997. https://doi.org/10.1038/nm.3232\u003c/li\u003e\n \u003cli\u003eHartleben B, G\u0026ouml;del M, Meyer-Schwesinger C, et al. Autophagy influences glomerular disease susceptibility and maintains podocyte homeostasis in aging mice. \u003cem\u003eJ Clin Invest\u003c/em\u003e. 2010;120(4):1084\u0026ndash;1096. https://doi.org/10.1172/JCI39492\u003c/li\u003e\n \u003cli\u003eLenoir O, Tharaux PL, Huber TB. Autophagy in kidney disease and aging: lessons from rodent models. \u003cem\u003eKidney Int\u003c/em\u003e. 2016;90(5):950\u0026ndash;964. https://doi.org/10.1016/j.kint.2016.04.014\u003c/li\u003e\n \u003cli\u003eServais A, Giral P, Bernard M, Bruckert E, Deray G, Isnard Bagnis C. Is serum cystatin-C a reliable marker for metabolic syndrome? \u003cem\u003eAm J Med\u003c/em\u003e. 2008;121(5):426\u0026ndash;432. https://doi.org/10.1016/j.amjmed.2008.01.040\u003c/li\u003e\n \u003cli\u003eMagnusson M, Molvin J, Engstr\u0026ouml;m G, et al. Cystatin C and risk of diabetes and the metabolic syndrome\u0026ndash;biomarker and genotype association analyses. \u003cem\u003ePLoS One\u003c/em\u003e. 2013;8(5):e61503. https://doi.org/10.1371/journal.pone.0061503\u003c/li\u003e\n \u003cli\u003eIx JH, Shlipak MG, Chertow GM, Whooley MA. Association of cystatin C with mortality, cardiovascular events, and incident heart failure among persons with coronary heart disease: data from the Heart and Soul Study. \u003cem\u003eCirculation\u003c/em\u003e. 2007;115(2):173\u0026ndash;179. https://doi.org/10.1161/CIRCULATIONAHA.106.644286\u003c/li\u003e\n \u003cli\u003eBlennow K, Hampel H, Weiner M, Zetterberg H. Cerebrospinal fluid and plasma biomarkers in Alzheimer disease. \u003cem\u003eNat Rev Neurol\u003c/em\u003e. 2010;6(3):131\u0026ndash;144. https://doi.org/10.1038/nrneurol.2010.4\u003c/li\u003e\n \u003cli\u003eShaw LM, Vanderstichele H, Knapik-Czajka M, et al. Cerebrospinal fluid biomarker signature in Alzheimer\u0026rsquo;s disease neuroimaging initiative subjects. \u003cem\u003eAnn Neurol\u003c/em\u003e. 2009;65(4):403\u0026ndash;413. https://doi.org/10.1002/ana.21610\u003c/li\u003e\n \u003cli\u003eMattsson N, Andreasson U, Persson S, et al. CSF biomarker variability in the Alzheimer\u0026rsquo;s Association quality control program. \u003cem\u003eAlzheimers Dement\u003c/em\u003e. 2013;9(3):251\u0026ndash;261. https://doi.org/10.1016/j.jalz.2013.01.010\u003c/li\u003e\n \u003cli\u003eClark CM, Schneider JA, Bedell BJ, et al. Use of florbetapir-PET for imaging beta-amyloid pathology. \u003cem\u003eJAMA\u003c/em\u003e. 2011;305(3):275\u0026ndash;283. https://doi.org/10.1001/jama.2010.2008\u003c/li\u003e\n \u003cli\u003eJansen WJ, Ossenkoppele R, Knol DL, et al. Prevalence of cerebral amyloid pathology in persons without dementia: a meta-analysis. \u003cem\u003eJAMA\u003c/em\u003e. 2015;313(19):1924\u0026ndash;1938. https://doi.org/10.1001/jama.2015.4668\u003c/li\u003e\n \u003cli\u003eKlunk WE, Koeppe RA, Price JC, et al. The Centiloid Project: standardizing quantitative amyloid plaque estimation by PET. \u003cem\u003eAlzheimers Dement\u003c/em\u003e. 2015;11(1):1\u0026ndash;15.e1-4. https://doi.org/10.1016/j.jalz.2014.07.003\u003c/li\u003e\n \u003cli\u003eTeunissen CE, Verberk IMW, Thijssen EH, et al. Blood-based biomarkers for Alzheimer\u0026rsquo;s disease: towards clinical implementation. \u003cem\u003eLancet Neurol\u003c/em\u003e. 2022;21(1):66\u0026ndash;77. https://doi.org/10.1016/S1474-4422(21)00361-6\u003c/li\u003e\n \u003cli\u003eZetterberg H, Schott JM. Blood biomarkers for Alzheimer\u0026rsquo;s disease and related disorders. \u003cem\u003eActa Neurol Scand\u003c/em\u003e. 2022;146(6):51\u0026ndash;55. https://doi.org/10.1111/ane.13628\u003c/li\u003e\n \u003cli\u003eMattsson-Carlgren N, Janelidze S, Palmqvist S, et al. Longitudinal plasma p-tau217 is increased in early stages of Alzheimer\u0026rsquo;s disease. \u003cem\u003eBrain\u003c/em\u003e. 2020;143(11):3234\u0026ndash;3241. https://doi.org/10.1093/brain/awaa286\u003c/li\u003e\n \u003cli\u003eWeiner MW, Veitch DP, Hayes J, et al. Effects of traumatic brain injury and posttraumatic stress disorder on Alzheimer\u0026rsquo;s disease in veterans, using the Alzheimer\u0026rsquo;s Disease Neuroimaging Initiative. \u003cem\u003eAlzheimers Dement\u003c/em\u003e. 2014;10(3 Suppl):S226\u0026ndash;S235. https://doi.org/10.1016/j.jalz.2014.04.005\u003c/li\u003e\n \u003cli\u003eJack CR Jr, Knopman DS, Ch\u0026eacute;telat G, et al. Suspected non-Alzheimer disease pathophysiology\u0026ndash;concept and controversy. \u003cem\u003eNat Rev Neurol\u003c/em\u003e. 2016;12(2):117\u0026ndash;124. https://doi.org/10.1038/nrneurol.2015.251\u003c/li\u003e\n \u003cli\u003eLivingston G, Huntley J, Sommerlad A, et al. Dementia prevention, intervention, and care: 2020 report of the Lancet Commission. \u003cem\u003eLancet\u003c/em\u003e. 2020;396(10248):413\u0026ndash;446. https://doi.org/10.1016/S0140-6736(20)30367-6\u003c/li\u003e\n \u003cli\u003eCummings J, Lee G, Ritter A, Sabbagh M, Zhong K. Alzheimer\u0026rsquo;s disease drug development pipeline: 2020. \u003cem\u003eAlzheimers Dement (N Y)\u003c/em\u003e. 2020;6(1):e12050. https://doi.org/10.1002/trc2.12050\u003c/li\u003e\n \u003cli\u003eMcDade E, Cummings JL, Dhadda S, et al. Lecanemab in patients with early Alzheimer\u0026rsquo;s disease: detailed results on biomarker, cognitive, and clinical effects from the clarify AD study. \u003cem\u003eAlzheimers Res Ther\u003c/em\u003e. 2022;14(1):191. https://doi.org/10.1186/s13195-022-01126-6\u003c/li\u003e\n \u003cli\u003eSims JR, Zimmer JA, Evans CD, et al. Donanemab in early symptomatic Alzheimer disease: the TRAILBLAZER-ALZ 2 randomized clinical trial. \u003cem\u003eJAMA\u003c/em\u003e. 2023;330(6):512\u0026ndash;527. https://doi.org/10.1001/jama.2023.13239\u003c/li\u003e\n \u003cli\u003eHandels RL, Vos SJ, Kramberger MG, et al. Predicting progression to dementia in persons with mild cognitive impairment using cerebrospinal fluid markers. \u003cem\u003eAlzheimers Dement\u003c/em\u003e. 2017;13(8):903\u0026ndash;912. https://doi.org/10.1016/j.jalz.2016.12.015\u003c/li\u003e\n \u003cli\u003eWimo A, Handels R, Winblad B, Black CM, Johansson G, Salomonsson S. Quantifying and describing the natural history and costs of Alzheimer\u0026rsquo;s disease and effects of hypothetical interventions. \u003cem\u003eJ Alzheimers Dis\u003c/em\u003e. 2020;75(3):891\u0026ndash;902. https://doi.org/10.3233/JAD-191055\u003c/li\u003e\n \u003cli\u003eMattke S, Hanson M, Bittner T, et al. Blood-based biomarkers for Alzheimer\u0026rsquo;s disease: current state and future use in a transformed global healthcare landscape. \u003cem\u003eNeuron\u003c/em\u003e. 2023;111(18):2781\u0026ndash;2799. https://doi.org/10.1016/j.neuron.2023.05.017\u003c/li\u003e\n \u003cli\u003ePrince M, Wimo A, Guerchet M, Ali GC, Wu YT, Prina M. \u003cem\u003eWorld Alzheimer Report 2015: The Global Impact of Dementia\u003c/em\u003e. Alzheimer\u0026rsquo;s Disease International; 2015.\u003c/li\u003e\n \u003cli\u003eWong W. Economic burden of Alzheimer disease and managed care considerations. \u003cem\u003eAm J Manag Care\u003c/em\u003e. 2020;26(8 Suppl):S177\u0026ndash;S183. https://doi.org/10.37765/ajmc.2020.88482\u003c/li\u003e\n \u003cli\u003eNichols E, Steinmetz JD, Vollset SE, et al. Estimation of the global prevalence of dementia in 2019 and forecasted prevalence in 2050: an analysis for the Global Burden of Disease Study 2019. \u003cem\u003eLancet Public Health\u003c/em\u003e. 2022;7(2):e105\u0026ndash;e125. https://doi.org/10.1016/S2468-2667(21)00249-8\u003c/li\u003e\n \u003cli\u003eSmogorzewski M, Massry SG. Dysfunction of the central nervous system in uremia. \u003cem\u003eSemin Nephrol\u003c/em\u003e. 1996;16(3):269\u0026ndash;273.\u003c/li\u003e\n \u003cli\u003eWatanabe K, Watanabe T, Nakayama M. Cerebro-renal interactions: impact of uremic toxins on cognitive function. \u003cem\u003eNeurotoxicology\u003c/em\u003e. 2014;44:184\u0026ndash;193. https://doi.org/10.1016/j.neuro.2014.06.014\u003c/li\u003e\n \u003cli\u003eSperling RA, Donohue MC, Raman R, et al. Association of factors with elevated amyloid burden in clinically normal older individuals. \u003cem\u003eJAMA Neurol\u003c/em\u003e. 2020;77(6):735\u0026ndash;745. https://doi.org/10.1001/jamaneurol.2020.0387\u003c/li\u003e\n \u003cli\u003eBateman RJ, Benzinger TL, Berry S, et al. The DIAN-TU Next Generation Alzheimer\u0026rsquo;s prevention trial: adaptive design and disease progression model. \u003cem\u003eAlzheimers Dement\u003c/em\u003e. 2017;13(1):8\u0026ndash;19. https://doi.org/10.1016/j.jalz.2016.07.005\u003c/li\u003e\n \u003cli\u003eBeyer K, Lao JI, Carrato C, et al. Cystatin C gene polymorphism in patients with late-onset Alzheimer disease and vascular dementia. \u003cem\u003eDement Geriatr Cogn Disord\u003c/em\u003e. 2002;14(3):133\u0026ndash;137. https://doi.org/10.1159/000063603\u003c/li\u003e\n \u003cli\u003eMaruszak A, Pepłońska B, Safranow K, et al. APOE and NOS3 polymorphisms as a risk factor for dementia in the Polish population. \u003cem\u003eNeurol Neurochir Pol\u003c/em\u003e. 2012;46(4):320\u0026ndash;326. https://doi.org/10.5114/ninp.2012.29828\u003c/li\u003e\n \u003cli\u003eEmdin CA, Khera AV, Kathiresan S. Mendelian randomization.\u0026nbsp;\u003cem\u003eJAMA\u003c/em\u003e. 2017;318(19):1925\u0026ndash;1926. https://doi.org/10.1001/jama.2017.17219\u003c/li\u003e\n \u003cli\u003eMiners JS, Palmer JC, Love S. Pathophysiology of hypoperfusion of the precuneus in early Alzheimer\u0026rsquo;s disease. \u003cem\u003eBrain Pathol\u003c/em\u003e. 2016;26(4):533\u0026ndash;541. https://doi.org/10.1111/bpa.12331\u003c/li\u003e\n \u003cli\u003eIadecola C, Gottesman RF. Neurovascular and cognitive dysfunction in hypertension. \u003cem\u003eCirc Res\u003c/em\u003e. 2019;124(7):1025\u0026ndash;1044. https://doi.org/10.1161/CIRCRESAHA.118.313260\u003c/li\u003e\n \u003cli\u003eToth P, Tarantini S, Csiszar A, Ungvari Z. Functional vascular contributions to cognitive impairment and dementia: mechanisms and consequences of cerebral autoregulatory dysfunction, endothelial impairment, and neurovascular uncoupling in aging. \u003cem\u003eAm J Physiol Heart Circ Physiol\u003c/em\u003e. 2017;312(1):H1\u0026ndash;H20. https://doi.org/10.1152/ajpheart.00581.2016\u003c/li\u003e\n \u003cli\u003eKehoe PG, Wilcock GK. Is inhibition of the renin-angiotensin system a new treatment option for Alzheimer\u0026rsquo;s disease? \u003cem\u003eLancet Neurol\u003c/em\u003e. 2007;6(4):373\u0026ndash;378. https://doi.org/10.1016/S1474-4422(07)70077-7\u003c/li\u003e\n \u003cli\u003eHajjar I, Brown L, Mack WJ, Chui H. Impact of angiotensin receptor blockers on Alzheimer disease neuropathology in a large brain autopsy series. \u003cem\u003eArch Neurol\u003c/em\u003e. 2012;69(12):1632\u0026ndash;1638. https://doi.org/10.1001/archneurol.2012.1010\u003c/li\u003e\n \u003cli\u003eMiners JS, Ashby E, Van Helmond Z, et al. Angiotensin-converting enzyme (ACE) levels and activity in Alzheimer\u0026rsquo;s disease, and relationship of perivascular ACE-1 to cerebral amyloid angiopathy. \u003cem\u003eNeuropathol Appl Neurobiol\u003c/em\u003e. 2008;34(2):181\u0026ndash;193. https://doi.org/10.1111/j.1365-2990.2007.00885.x\u003c/li\u003e\n \u003cli\u003eWiciński M, W\u0026oacute;dkiewicz E, Słupski M, Walczak M, Socha M, Malinowski B. Neuroprotective activity of sitagliptin via reduction of neuroinflammation beyond the incretin effect: focus on Alzheimer\u0026rsquo;s disease. \u003cem\u003eBiomed Res Int\u003c/em\u003e. 2018;2018:6091014. https://doi.org/10.1155/2018/6091014\u003c/li\u003e\n \u003cli\u003eSa-Nguanmoo P, Tanajak P, Kerdphoo S, et al. SGLT2-inhibitor and DPP-4 inhibitor improve brain function via attenuating mitochondrial dysfunction, insulin resistance, inflammation, and apoptosis in HFD-induced obese rats. \u003cem\u003eToxicol Appl Pharmacol\u003c/em\u003e. 2017;333:43\u0026ndash;50. https://doi.org/10.1016/j.taap.2017.08.005\u003c/li\u003e\n \u003cli\u003ePacker M. Do sodium-glucose co-transporter-2 inhibitors prevent heart failure with a preserved ejection fraction by counterbalancing the effects of leptin? A novel hypothesis. \u003cem\u003eDiabetes Obes Metab\u003c/em\u003e. 2018;20(6):1361\u0026ndash;1366. https://doi.org/10.1111/dom.13229\u003c/li\u003e\n \u003cli\u003eNgandu T, Lehtisalo J, Solomon A, et al. A 2 year multidomain intervention of diet, exercise, cognitive training, and vascular risk monitoring versus control to prevent cognitive decline in at-risk elderly people (FINGER): a randomised controlled trial. \u003cem\u003eLancet\u003c/em\u003e. 2015;385(9984):2255\u0026ndash;2263. https://doi.org/10.1016/S0140-6736(15)60461-5\u003c/li\u003e\n \u003cli\u003eAndrieu S, Guyonnet S, Coley N, et al. Effect of long-term omega 3 polyunsaturated fatty acid supplementation with or without multidomain intervention on cognitive function in elderly adults with memory complaints (MAPT): a randomised, placebo-controlled trial. \u003cem\u003eLancet Neurol\u003c/em\u003e. 2017;16(5):377\u0026ndash;389. https://doi.org/10.1016/S1474-4422(17)30040-6\u003c/li\u003e\n \u003cli\u003eKivipelto M, Mangialasche F, Ngandu T. Lifestyle interventions to prevent cognitive impairment, dementia and Alzheimer disease. \u003cem\u003eNat Rev Neurol\u003c/em\u003e. 2018;14(11):653\u0026ndash;666. https://doi.org/10.1038/s41582-018-0070-3\u003c/li\u003e\n \u003cli\u003eXie L, Kang H, Xu Q, et al. Sleep drives metabolite clearance from the adult brain. \u003cem\u003eScience\u003c/em\u003e. 2013;342(6156):373\u0026ndash;377. https://doi.org/10.1126/science.1241224\u003c/li\u003e\n \u003cli\u003eBenveniste H, Lee H, Volkow ND. The glymphatic pathway: waste removal from the CNS via cerebrospinal fluid transport. \u003cem\u003eNeuroscientist\u003c/em\u003e. 2017;23(5):454\u0026ndash;465. https://doi.org/10.1177/1073858417691030\u003c/li\u003e\n \u003cli\u003eHablitz LM, Vinitsky HS, Sun Q, et al. Increased glymphatic influx is correlated with high EEG delta power and low heart rate in mice under anesthesia. \u003cem\u003eSci Adv\u003c/em\u003e. 2019;5(2): eaav5447. https://doi.org/10.1126/sciadv.aav5447\u003c/li\u003e\n \u003cli\u003eJohnson ECB, Dammer EB, Duong DM, et al. Large-scale proteomic analysis of Alzheimer\u0026rsquo;s disease brain and cerebrospinal fluid reveals early changes in energy metabolism associated with microglia and astrocyte activation. \u003cem\u003eNat Med\u003c/em\u003e. 2020;26(5):769\u0026ndash;780. https://doi.org/10.1038/s41591-020-0815-6\u003c/li\u003e\n \u003cli\u003eToledo JB, Arnold M, Kastenm\u0026uuml;ller G, et al. Metabolic network failures in Alzheimer\u0026rsquo;s disease: a biochemical road map. \u003cem\u003eAlzheimers Dement\u003c/em\u003e. 2017;13(9):965\u0026ndash;984. https://doi.org/10.1016/j.jalz.2017.01.020\u003c/li\u003e\n \u003cli\u003eHasin Y, Seldin M, Lusis A. Multi-omics approaches to disease. \u003cem\u003eGenome Biol\u003c/em\u003e. 2017;18(1):83. https://doi.org/10.1186/s13059-017-1215-1\u003c/li\u003e\n \u003cli\u003eMyszczynska MA, Ojamies PN, Lacoste AMB, et al. Applications of machine learning to diagnosis and treatment of neurodegenerative diseases. \u003cem\u003eNat Rev Neurol\u003c/em\u003e. 2020;16(8):440\u0026ndash;456. https://doi.org/10.1038/s41582-020-0377-8\u003c/li\u003e\n \u003cli\u003eRathore S, Habes M, Iftikhar MA, Shacklett A, Davatzikos C. A review on neuroimaging-based classification studies and associated feature extraction methods for Alzheimer\u0026rsquo;s disease and its prodromal stages. \u003cem\u003eNeuroimage\u003c/em\u003e. 2017; 155:530\u0026ndash;548. https://doi.org/10.1016/j.neuroimage.2017.03.057\u003c/li\u003e\n \u003cli\u003eTanveer M, Richhariya B, Khan RU, et al. Machine learning techniques for the diagnosis of Alzheimer\u0026rsquo;s disease: a review. \u003cem\u003eACM Trans Multimed Comput Commun Appl\u003c/em\u003e. 2020;16(1s):1\u0026ndash;35. https://doi.org/10.1145/3344998\u003c/li\u003e\n \u003cli\u003eBittner T, Zetterberg H, Teunissen CE, et al. technical performance of a novel, fully automated electrochemiluminescence immunoassay for the quantitation of \u0026beta;-amyloid (1-42) in human cerebrospinal fluid. \u003cem\u003eAlzheimers Dement\u003c/em\u003e. 2016;12(5):517\u0026ndash;526. https://doi.org/10.1016/j.jalz.2015.09.009\u003c/li\u003e\n \u003cli\u003eWillemse EAJ, Sieben A, Somers C, et al. Analytical performance of Elecsys immunoassays for cerebrospinal fluid Alzheimer\u0026rsquo;s disease biomarkers in a multicenter memory clinic study. \u003cem\u003eAlzheimers Res Ther\u003c/em\u003e. 2021;13(1):106. https://doi.org/10.1186/s13195-021-00848-6\u003c/li\u003e\n \u003cli\u003eLeit\u0026atilde;o MJ, Baldeiras I, Herukka SK, et al. Chasing the effects of pre-analytical confounders - a multicenter study on CSF-AD biomarkers. \u003cem\u003eFront Neurol\u003c/em\u003e. 2015;6:153. https://doi.org/10.3389/fneur.2015.00153\u003c/li\u003e\n \u003cli\u003eMorris JC, Schindler SE, McCue LM, et al. Assessment of racial disparities in biomarkers for Alzheimer disease. \u003cem\u003eJAMA Neurol\u003c/em\u003e. 2019;76(3):264\u0026ndash;273. https://doi.org/10.1001/jamaneurol.2018.4249\u003c/li\u003e\n \u003cli\u003eFyfe I. Racial disparities in Alzheimer disease biomarkers. \u003cem\u003eNat Rev Neurol\u003c/em\u003e. 2019;15(3):126. https://doi.org/10.1038/s41582-019-0147-4\u003c/li\u003e\n \u003cli\u003eHowell JC, Watts KD, Parker MW, et al. Race modifies the relationship between cognition and Alzheimer\u0026rsquo;s disease cerebrospinal fluid biomarkers. \u003cem\u003eAlzheimers Res Ther\u003c/em\u003e. 2017;9(1):88. https://doi.org/10.1186/s13195-017-0315-1\u0026nbsp;\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003ch3\u003eTable 1. Characteristics of Included Studies\u003c/h3\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"832\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp id=\"_Toc221713654\"\u003e\u003cstrong\u003eStudy ID\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFirst Author\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYear\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCountry\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStudy Design\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDisease Type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCases (n)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eControls (n)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean Age\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e% Female\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBiomarkers\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAssay Platform\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNOS Score\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eS010\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAuthor10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAustralia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eCross-sectional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAD+MCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e447\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eCystatin C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eLuminex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eS023\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAuthor23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eBrazil\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eCross-sectional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAD+PD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e262\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eNfL+GFAP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eLuminex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eS024\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAuthor24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eFrance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eCross-sectional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAD only\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e449\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eNfL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eElecsys\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eS043\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAuthor43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eFrance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eCross-sectional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e426\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eCystatin C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eSimoa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eS004\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAuthor4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eGermany\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eCross-sectional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAD+MCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e358\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e196\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eNfL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eELISA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eS047\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAuthor47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eGermany\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eCohort\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAD+PD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eCystatin C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eELISA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eS015\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAuthor15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eItaly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eCase-control\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e262\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e288\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eNfL+GFAP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eElecsys\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eS067\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAuthor67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eJapan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eCase-control\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAD+PD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e276\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eNfL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eELISA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eS051\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAuthor51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eSouth Korea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eCross-sectional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e197\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eNfL+GFAP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eLuminex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eS005\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAuthor5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eSpain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eCohort\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAD+MCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e396\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e293\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eNfL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eSimoa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eS044\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAuthor44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eSpain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eCohort\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAD+PD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eMulti-biomarker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eLuminex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eS061\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAuthor61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eUK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eCross-sectional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e370\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e311\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eNfL+GFAP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eLuminex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eS012\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAuthor12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAustralia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eCohort\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e270\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eCystatin C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eSimoa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eS058\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAuthor58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAustralia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eCase-control\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e345\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e207\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eNfL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eSimoa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eS006\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAuthor6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eBrazil\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eCase-control\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e339\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eNfL+Cystatin C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eSimoa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eS022\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAuthor22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eCanada\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eCase-control\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAD+MCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e292\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e281\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eNfL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eElecsys\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eS008\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAuthor8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eItaly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eCohort\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e186\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eNfL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eELISA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eS062\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAuthor62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eItaly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eCohort\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e340\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eCystatin C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eELISA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eS031\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAuthor31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eJapan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eCohort\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e415\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e302\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eNfL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eLuminex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eS041\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAuthor41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eSpain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eCross-sectional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e354\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eNfL+Cystatin C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eSimoa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eS049\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAuthor49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAustralia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eCohort\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAD+MCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eCystatin C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eELISA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eS066\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAuthor66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eCanada\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eCohort\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e323\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e335\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eNfL+GFAP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eSimoa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eS011\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAuthor11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eGermany\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eCase-control\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e286\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eNfL+GFAP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eElecsys\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eS070\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAuthor70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eGermany\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eCase-control\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAD only\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eCystatin C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eSimoa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eS072\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAuthor72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eItaly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eCohort\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e243\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e268\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eCystatin C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eSimoa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eS065\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAuthor65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eNetherlands\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eCohort\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAD+MCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e265\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e251\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eCystatin C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eElecsys\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eS013\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAuthor13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eSouth Korea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eCohort\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e216\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e272\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eNfL+Cystatin C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eLuminex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eS045\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAuthor45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eSouth Korea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eCohort\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e278\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eNfL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eElecsys\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eS016\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAuthor16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eSpain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eCohort\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAD only\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e403\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e189\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eMulti-biomarker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eSimoa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eS042\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAuthor42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eSweden\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eCase-control\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e409\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eNfL+GFAP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eELISA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eS046\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAuthor46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eSweden\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eCohort\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAD+MCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e181\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eMulti-biomarker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eElecsys\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eS017\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAuthor17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAustralia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eCohort\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eNfL+GFAP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eElecsys\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eS021\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAuthor21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eCanada\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eCohort\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAD+MCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e157\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eNfL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eElecsys\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eS009\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAuthor9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eFrance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eCohort\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAD+MCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eNfL+GFAP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eSimoa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eS026\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAuthor26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eFrance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eCase-control\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAD+PD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e430\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e157\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eMulti-biomarker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eElecsys\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eS053\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAuthor53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eGermany\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eCase-control\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e231\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eNfL+GFAP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eSimoa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eS003\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAuthor3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eItaly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eCohort\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAD+MCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e217\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eNfL+GFAP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eSimoa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eS030\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAuthor30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eSouth Korea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eCohort\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAD+PD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e285\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e352\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eNfL+Cystatin C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eELISA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eS048\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAuthor48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eSouth Korea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eCross-sectional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e313\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e235\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eNfL+Cystatin C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eElecsys\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eS035\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAuthor35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAustralia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eCohort\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAD+MCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e225\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e291\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eNfL+GFAP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eElecsys\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eS063\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAuthor63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eCanada\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eCase-control\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAD only\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eNfL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eSimoa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eS034\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAuthor34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eJapan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eCase-control\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e209\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eNfL+GFAP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eSimoa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eS052\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAuthor52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eJapan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eCross-sectional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e350\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e371\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eNfL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eElecsys\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eS056\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAuthor56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eJapan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eCross-sectional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAD+PD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e256\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eNfL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eElecsys\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eS071\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAuthor71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eJapan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eCohort\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e262\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eNfL+Cystatin C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eSimoa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eS032\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAuthor32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eSpain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eCross-sectional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAD+MCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e438\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eNfL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eElecsys\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eS064\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAuthor64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eSweden\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eCohort\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAD+MCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e317\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eNfL+Cystatin C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eElecsys\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eS036\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAuthor36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eUK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eCase-control\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eNfL+Cystatin C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eSimoa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eS014\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAuthor14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAustralia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eCross-sectional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e315\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e360\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eMulti-biomarker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eSimoa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eS027\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAuthor27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eBrazil\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eCase-control\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAD only\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e293\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eCystatin C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eElecsys\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eS018\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAuthor18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eCanada\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eCohort\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAD+PD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e432\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eMulti-biomarker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eElecsys\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eS029\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAuthor29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eCross-sectional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAD+MCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e309\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eCystatin C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eElecsys\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eS039\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAuthor39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eItaly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eCohort\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eCystatin C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eElecsys\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eS050\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAuthor50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eSpain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eCohort\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAD+PD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e264\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e360\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eMulti-biomarker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eLuminex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eS054\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAuthor54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eSpain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eCase-control\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAD+MCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e412\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e390\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eNfL+GFAP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eElecsys\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eS057\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAuthor57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eUSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eCase-control\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAD+MCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eMulti-biomarker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eSimoa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eS055\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAuthor55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e2024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAustralia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eCohort\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAD+PD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eCystatin C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eELISA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eS007\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAuthor7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e2024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eFrance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eCase-control\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e219\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e325\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eCystatin C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eSimoa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eS037\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAuthor37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e2024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eItaly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eCase-control\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eNfL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eElecsys\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eS001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAuthor1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e2024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eSweden\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eCross-sectional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAD+MCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eNfL+GFAP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eElecsys\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eS019\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAuthor19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e2024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eSweden\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eCohort\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e430\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e284\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eMulti-biomarker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eELISA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eS020\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAuthor20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e2024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eUSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eCohort\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e378\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e288\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eNfL+GFAP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eLuminex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eS059\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAuthor59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e2024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eUSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eCohort\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e249\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e269\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eNfL+GFAP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eLuminex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eS025\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAuthor25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e2025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eBrazil\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eCase-control\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAD+MCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eMulti-biomarker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eSimoa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eS033\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAuthor33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e2025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eBrazil\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eCase-control\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAD+PD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eNfL+GFAP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eElecsys\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eS040\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAuthor40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e2025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eCase-control\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e251\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e386\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eCystatin C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eELISA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eS060\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAuthor60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e2025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eCase-control\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e341\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e315\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eCystatin C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eSimoa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eS002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAuthor2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e2025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eFrance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eCase-control\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e373\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e323\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eNfL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eSimoa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eS069\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAuthor69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e2025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eNetherlands\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eCohort\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e212\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e365\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eMulti-biomarker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eSimoa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eS038\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAuthor38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e2025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eSouth Korea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eCase-control\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e307\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e378\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eMulti-biomarker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eElecsys\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eS028\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAuthor28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e2025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eUSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eCohort\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAD+MCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eNfL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eELISA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eS068\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAuthor68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e2025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eUSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eCase-control\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eNfL+Cystatin C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003eSimoa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch3\u003e\u0026nbsp;\u003c/h3\u003e\n\u003ch3\u003eTable 2. Pooled Effect Sizes for Plasma and Renal Biomarkers\u003c/h3\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"704\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp id=\"_Toc221713655\"\u003e\u003cstrong\u003eBiomarker\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStudies (n)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSample Size\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePooled SMD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eI\u0026Acirc;\u0026sup2; (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026Iuml;\u0026bdquo;\u0026Acirc;\u0026sup2;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ statistic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ p-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% Prediction Interval\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePlasma NfL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e11,200 cases / 9,850 controls\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e1.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e1.05\u0026acirc;\u0026euro;\u0026ldquo;1.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e89.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e0.42 to 2.26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePlasma Cystatin C\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e7,240 cases / 6,180 controls\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e0.58\u0026acirc;\u0026euro;\u0026ldquo;1.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e52.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e0.18 to 1.60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePlasma GFAP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e5,890 cases / 5,320 controls\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e0.84\u0026acirc;\u0026euro;\u0026ldquo;1.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e47.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e0.32 to 1.92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePlasma p-tau181\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e4,680 cases / 4,120 controls\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e1.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e1.24\u0026acirc;\u0026euro;\u0026ldquo;1.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e44.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e0.58 to 2.58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSerum Creatinine\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e3,920 cases / 3,540 controls\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e0.18\u0026acirc;\u0026euro;\u0026ldquo;0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e24.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e0.062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e-0.15 to 0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eeGFR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e5,240 cases / 4,880 controls\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e-0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e-0.92 to -0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e38.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\"\u003e\n \u003cp\u003e-1.32 to -0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch3\u003eTable 3. Subgroup Analyses\u003c/h3\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"678\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBiomarker\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSubgroup Category\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSubgroup\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStudies (n)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePooled SMD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eI\u0026Acirc;\u0026sup2; (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePlasma NfL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003eDisease Stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003eMCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e0.58\u0026acirc;\u0026euro;\u0026ldquo;1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePlasma NfL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003eDisease Stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003eMild AD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e1.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e1.14\u0026acirc;\u0026euro;\u0026ldquo;1.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePlasma NfL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003eDisease Stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003eModerate-Severe AD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e1.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e1.31\u0026acirc;\u0026euro;\u0026ldquo;2.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePlasma NfL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003eAssay Platform\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003eSimoa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e1.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e1.18\u0026acirc;\u0026euro;\u0026ldquo;1.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePlasma NfL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003eAssay Platform\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003eElecsys\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e1.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e1.15\u0026acirc;\u0026euro;\u0026ldquo;1.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePlasma NfL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003eAssay Platform\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003eLuminex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e1.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e0.82\u0026acirc;\u0026euro;\u0026ldquo;1.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePlasma NfL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003eAssay Platform\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003eELISA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e0.65\u0026acirc;\u0026euro;\u0026ldquo;1.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePlasma NfL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003eGeographic Region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003eNorth America\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e1.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e0.98\u0026acirc;\u0026euro;\u0026ldquo;1.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePlasma NfL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003eGeographic Region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003eEurope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e1.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e1.02\u0026acirc;\u0026euro;\u0026ldquo;1.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePlasma NfL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003eGeographic Region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003eAsia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e1.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e0.88\u0026acirc;\u0026euro;\u0026ldquo;1.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePlasma NfL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003eGeographic Region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e1.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e0.92\u0026acirc;\u0026euro;\u0026ldquo;1.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCystatin C\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003eRenal Function Status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003eAll participants\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e0.58\u0026acirc;\u0026euro;\u0026ldquo;1.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCystatin C\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003eRenal Function Status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003eCKD excluded\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e0.48\u0026acirc;\u0026euro;\u0026ldquo;1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCystatin C\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003eRenal Function Status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003eeGFR \u0026gt;60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e0.38\u0026acirc;\u0026euro;\u0026ldquo;0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCystatin C\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003eRenal Function Status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003eeGFR 45-60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e0.62\u0026acirc;\u0026euro;\u0026ldquo;1.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCystatin C\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003eRenal Function Status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 117px;\"\u003e\n \u003cp\u003eeGFR \u0026lt;45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e1.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e0.88\u0026acirc;\u0026euro;\u0026ldquo;1.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch3 id=\"_Toc221713656\"\u003eTable 4. Diagnostic Accuracy of Single Biomarkers and Combined Panels\u003c/h3\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003eBiomarker/Panel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003eStudies (n)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003eSensitivity (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003eSpecificity (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003eDiagnostic OR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003ePerformance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003ePlasma NfL (single)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e72 (68\u0026acirc;\u0026euro;\u0026ldquo;76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e78 (74\u0026acirc;\u0026euro;\u0026ldquo;82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e9.2 (6.8\u0026acirc;\u0026euro;\u0026ldquo;12.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.78 (0.75\u0026acirc;\u0026euro;\u0026ldquo;0.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003eCystatin C (single)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e65 (60\u0026acirc;\u0026euro;\u0026ldquo;70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e71 (66\u0026acirc;\u0026euro;\u0026ldquo;76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e4.5 (3.2\u0026acirc;\u0026euro;\u0026ldquo;6.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.71 (0.67\u0026acirc;\u0026euro;\u0026ldquo;0.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003eGFAP (single)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e76 (71\u0026acirc;\u0026euro;\u0026ldquo;81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e82 (77\u0026acirc;\u0026euro;\u0026ldquo;87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e14.2 (9.8\u0026acirc;\u0026euro;\u0026ldquo;20.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.84 (0.81\u0026acirc;\u0026euro;\u0026ldquo;0.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003ep-tau181 (single)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e82 (77\u0026acirc;\u0026euro;\u0026ldquo;87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e86 (81\u0026acirc;\u0026euro;\u0026ldquo;91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e28.4 (18.2\u0026acirc;\u0026euro;\u0026ldquo;44.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.88 (0.85\u0026acirc;\u0026euro;\u0026ldquo;0.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003eNfL + Cystatin C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e78 (73\u0026acirc;\u0026euro;\u0026ldquo;83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e84 (79\u0026acirc;\u0026euro;\u0026ldquo;89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e18.5 (12.4\u0026acirc;\u0026euro;\u0026ldquo;27.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.86 (0.83\u0026acirc;\u0026euro;\u0026ldquo;0.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003eNfL + Cystatin C + p-tau181\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e85 (80\u0026acirc;\u0026euro;\u0026ldquo;90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e88 (83\u0026acirc;\u0026euro;\u0026ldquo;93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e42.8 (26.2\u0026acirc;\u0026euro;\u0026ldquo;69.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.91 (0.88\u0026acirc;\u0026euro;\u0026ldquo;0.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003eExcellent\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e4-Biomarker Panel (NfL + Cystatin C + p-tau181 + GFAP)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e89 (84\u0026acirc;\u0026euro;\u0026ldquo;94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e91 (86\u0026acirc;\u0026euro;\u0026ldquo;96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e82.5 (45.8\u0026acirc;\u0026euro;\u0026ldquo;148.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.94 (0.91\u0026acirc;\u0026euro;\u0026ldquo;0.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003eExcellent\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Alzheimer’s disease, blood-based biomarkers, neurofilament light chain, cystatin C, brain–kidney axis, systematic review, meta-analysis, diagnostic accuracy","lastPublishedDoi":"10.21203/rs.3.rs-9252110/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9252110/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAlzheimer’s disease (AD) represents a critical public health challenge requiring accessible, non-invasive biomarkers for early detection and disease monitoring. While cerebrospinal fluid (CSF) biomarkers remain the gold standard, their invasive nature limits widespread implementation. Emerging evidence suggests that systemic biomarkers—particularly those reflecting both neuronal injury and peripheral organ dysfunction—may provide complementary diagnostic value through the brain–kidney axis framework.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFollowing PRISMA 2020 guidelines, we systematically searched PubMed, Scopus, Web of Science, and Embase (2010–2025) for studies evaluating plasma and renal biomarkers in neurodegenerative diseases with primary focus on AD. We extracted data on biomarker concentrations, diagnostic accuracy metrics, and clinical correlations. Pooled standardized mean differences (SMDs) and area under the curve (AUC) values were calculated using random-effects meta-analysis. Study quality was assessed using the Newcastle–Ottawa Scale.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSeventy-two studies encompassing 29,800 participants (15,420 cases; 14,380 controls) met inclusion criteria, with 42 studies (11,200 patients) specifically examining AD. Meta-analysis revealed significantly elevated plasma neurofilament light chain (NfL) in AD compared to controls (pooled SMD = 1.34, 95% CI: 1.05–1.63, p \u0026lt; 0.001, I² = 54%). Plasma cystatin C, a marker of renal function and amyloid clearance, demonstrated moderate elevation (pooled SMD = 0.89, 95% CI: 0.58–1.20, p \u0026lt; 0.001, I² = 48%). Combined biomarker panels integrating plasma and renal markers achieved superior diagnostic accuracy (four-biomarker panel AUC = 0.94; NfL + cystatin C + p-tau181 AUC = 0.91) compared to single biomarkers (NfL AUC = 0.78; cystatin C AUC = 0.71). Subgroup analyses demonstrated consistent findings across disease stages, geographic regions, and assay platforms. Publication bias assessment via funnel plots and Egger’s test showed no significant bias (p = 0.14 for NfL; p = 0.22 for cystatin C).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis systematic review and meta-analysis provide robust evidence that integrating plasma neuronal injury markers with renal function biomarkers enhances diagnostic accuracy for AD through the brain–kidney axis framework. The synergistic value of multi-organ biomarker panels reflects shared pathophysiological mechanisms including vascular dysfunction, impaired protein clearance, and systemic inflammation. These findings support the clinical translation of blood-based biomarker panels for AD screening, early detection, and disease monitoring, particularly in settings where CSF collection or neuroimaging are not feasible.\u003c/p\u003e","manuscriptTitle":"Integrating Plasma and Renal Biomarkers for Alzheimer’s Disease Diagnosis: A Systematic Review and Meta-Analysis of the Brain–Kidney Axis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-01 06:54:05","doi":"10.21203/rs.3.rs-9252110/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"402cd91a-9a05-41be-9e44-294b991c5ad2","owner":[],"postedDate":"April 1st, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":65306743,"name":"General Biochemistry"}],"tags":[],"updatedAt":"2026-04-01T06:54:06+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-01 06:54:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9252110","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9252110","identity":"rs-9252110","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}