Correlations of CSF Biomarkers of Alzheimer’s Disease with Cognitive Measures in MCI and AD Dementia: A Cross-Sectional Analysis

Correlations of CSF Biomarkers of Alzheimer’s Disease with Cognitive Measures in MCI and AD Dementia: A Cross-Sectional Analysis | 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 Research Article Correlations of CSF Biomarkers of Alzheimer’s Disease with Cognitive Measures in MCI and AD Dementia: A Cross-Sectional Analysis Gabriel Bitar, Sierra Alban, Kassu Beyene, Boris Decourt, Shervin Harirchian, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8168314/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 and Objectives: We examined the relationships between cerebrospinal fluid (CSF) amyloid-β, tau, p-tau, and cognition in an Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. Studies have examined the longitudinal relationships between CSF biomarkers for Alzheimer’s disease (AD) and cognition, but there is discordance in the strength of associations between CSF amyloid-β, t-tau, or p-tau with cognition at different disease stages. Methods: The study included 665 patients from the combined ADNI dataset: 128 cognitively normal (CN), 175 with mild cognitive impairment (MCI), and 362 with AD dementia. All patients were amyloid-β–positive according to established CSF amyloid-β cut-off values. Cognitive assessments, diagnoses, and specimen collection/processing were conducted via published standardized protocols. We examined cross-sectional baseline data and performed correlational and regression analyses to evaluate the associations between CSF biomarkers and assessments of learning, memory, executive function, language, attention, visuospatial skills, and activities of daily living. Results: In the MCI cohort, significant negative correlations were observed between CSF amyloid-β and ADAS-Cog11 (r=-0.164, p=0.02, ADAS-Cog13 (r=-0.181, p=0.01), Trails B (r=0.11, p=0.04), and FAQ (r=-0.131, p=0.01). There were positive correlations between amyloid-β and MMSE (r=0.149, p=0.004) and amyloid-β and WMS-delayed recall (r=0.116, p=0.03). Statistically significant correlations were observed between CSF t-tau and p-tau and CDRSB (t-tau: r=0.105, p=0.047), ADAS-Cog11 (t-tau: r=0.114, p=0.03; p-tau-181: r=0.114, p=0.03), ADAS-Cog13 (t-tau: r=0.165, p=0.002; p-tau-181: r=0.162, p=0.002), RAVLT-forgetting (t-tau: r=0.173, p=0.001; p-tau-181: r=0.167, p=0.001), and WMS-delayed recall (t-tau: r=-0.237, p<0.001 and p-tau-181: r=-0.235, p<0.001). In the AD cohort, no statistically significant relationships were observed between CSF amyloid-β₁₋₄₂, t-tau, p-tau-181, and any of the cognitive scores. Discussion: The findings suggest that the relationship between CSF biomarkers and cognitive performance is strongest in MCI. The lack of significant correlations in the AD cohort may indicate other pathophysiological changes dominating cognitive dysfunction at this disease stage. Both tau and amyloid showed similar utility in reflecting cognitive impairment, in contrast to some reports in the literature. Further research is warranted to explore biomarker longitudinal impacts and their predictive values across the spectrum of cognitive impairment. Alzheimer’s Disease Neuroimaging Initiative Amyloid-β Mild cognitive impairment Mini-Mental State Exam Montreal Cognitive Assessment Phosphorylated-tau preclinical disease Total-tau Figures Figure 1 Figure 2 BACKGROUND As our aging population continues to increase, Alzheimer’s disease (AD) prevalence and the associated cost burden rise dramatically. 1 The hallmarks of AD include amyloid-β (Aβ) plaques and neurofibrillary tangles of tau accumulating in the brain. 2 Current clinical guidelines use neuroimaging and cognitive testing, such as the Montreal Cognitive Assessment and Mini Mental State Exam (MMSE), among others, to diagnose patients and determine the level of decline. 3 , 4 Often, by the time cognitive decline has begun, significant brain changes have already occurred during the “preclinical” phase of the disease. 5 , 6 In recent years, researchers have studied the potential of biomarkers to help diagnose AD earlier and initiate interventions as soon as possible, perhaps before clinical manifestations occur. 7 Despite advances in biomarker discovery it remains unclear how traditional CSF biomarkers relate to cognition at different stages of Alzheimer’s Disease within a well-characterized cohort. Cerebrospinal fluid (CSF) biomarkers consisting of Aβ₁₋₄₂, total-tau (t-tau), and phosphorylated-tau (p-tau) have been shown to reflect the disease course and may even indicate preclinical disease. 8 – 11 Typically, decreased CSF Aβ₁₋₄₂ and increased t-tau and p-tau reflect AD plaque accumulation and neurodegeneration, respectively. While these biomarkers are widely used for research purposes, positron emission tomography (PET), CSF, and blood-based tests are being used more commonly in clinical settings for diagnosis. 12 – 15 Previous studies have shown tau, on brain PET and in CSF, to be more closely related to the degree of cognitive decline and likely precedes symptom onset compared to Aβ. 5,16,17 However, some investigators reported conflicting results, suggesting a minimal relationship between CSF biomarkers and cognitive performance or proposing alternate biomarker methods altogether. 18 – 20 Hansson and colleagues proposed that CSF biomarkers agree well with imaging in predicting future decline. 7 Given the discordant results in the literature, there are gaps in our understanding of the role of CSF Aβ and tau in cognitive decline. Our study is one of the first to examine the relationships between CSF biomarkers in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort and all available cognitive tests. The primary aim of this study was to elucidate the cross-sectional relationships between CSF biomarkers and cognitive performance across different stages of cognitive impairment. By leveraging an extensive, well-characterized ADNI dataset, we sought to clarify discordances in prior literature regarding biomarker significance and provide insights into their clinical utility. The objective of this study was to systematically examine cross-sectional relationships between CSF Aβ₁₋₄₂, t-tau, and p-tau-181 and multiple domains of cognition across CN, MCI, and AD groups using the ADNI dataset. In accordance with prior literature, we hypothesize that CSF biomarkers, particularly p-tau and t-tau, will demonstrate stronger correlations with cognitive decline measures than Aβ₁₋₄₂, and that these associations will vary across different cognitive domains and stages of impairment. METHODS The study evaluates the associations between CSF biomarker expressions at baseline within various cognitive domains in a large, robust dataset. We hypothesize that CSF biomarkers, particularly p-tau and t-tau, would demonstrate stronger correlations with cognitive decline measures than Aβ₁₋₄₂, particularly in the cohort with mild cognitive impairment (MCI) and that these associations may vary across the spectrum of impairment. Subjects and Variables Study data were obtained from the combined ADNI 1, 2, and GO database (adni.loni.usc.edu). The ADNI was launched in 2003 as a public-private partnership led by Principal Investigator Michael W. Weiner, MD. The primary goal of the ADNI has been to assess whether serial magnetic resonance imaging (MRI), (PET, other biological markers, and clinical and neuropsychological assessment can be combined to measure the progression of mild cognitive impairment (MCI) and early AD. The study cohorts consisted of people who were classified as cognitively normal (CN), having MCI, or having AD on the basis of standardized diagnostic criteria. For inclusion in the study, participants had to be 55–90 years of age and have a clinical diagnosis of AD, MCI, or CN, with no history of other neurological disorders. Exclusion criteria included conditions that could confound the results, such as major psychiatric disorders or significant systemic illnesses as defined by the ADNI protocol. We additionally excluded those with a history of stroke or a cerebrovascular event. MCI was diagnosed on the basis of evidence of concern for cognitive changes and diminished performance in 1 or more cognitive domains but with generally intact functional ability and lack of significant impairment of social or occupational functioning. 21 AD was diagnosed as cognitive challenges interfering with daily activities, a decline from previous functioning, and cognitive impairments detected on cognitive testing. 22 Participants with normal cognition did not exhibit significant cognitive changes that aligned with MCI or AD diagnoses. Data were retrieved in August 2024 from ADNIMERGE, a curated database that consolidates key clinical, biomarker, and imaging data from the ADNI dataset. The dataset included CSF biomarker levels, cognitive assessments, and demographic variables. A total of 1017 patients were initially queried, with 976 remaining after selection for those with available CSF biomarker data. These 976 patients were further selected on the basis of their amyloid status to 665, which were considered Aβ+, as amyloid positivity is a key biomarker criterion for defining Alzheimer’s disease-specific cohorts. The Aβ + cut-off was determined by methods established and validated, using a cut-off of ≤ 980 pg/mL. 23 The cut-off was determined on the basis of concordance with amyloid PET data in the BioFINDER cohort and then validated in an independent ADNI cohort. 7 Demographic variables such as age and education were matched across diagnostic groups (CN, MCI, AD). CSF data were acquired from the ADNIMERGE. The data were analyzed using the xMAP Luminex immunoassay platform, which was published and validated by the University of Pennsylvania Biomarker Core. Detailed descriptions of the assay and its validation can be found in the literature and ADNI protocol documents. 11 Cognitive function was assessed using 7 standardized instruments including: (1) the Clinical Dementia Rating Scale Sum of Boxes (CDRSB), which measures global cognitive and functional impairment; (2) the Alzheimer’s Disease Assessment Scale-Cognitive Subscale (ADAS-Cog 11, ADAS-Cog 13), which evaluates cognitive performance, including memory, language, and praxis; (3) the MMSE, which screens for global cognitive function; (4) the Rey Auditory Verbal Learning Test (RAVLT), which assesses verbal memory and learning; (5) the Trail Making Test Part B (Trails B), which evaluates executive function and cognitive flexibility; (6) the Logical Memory Delayed Recall (LDELTOTAL), which measures delayed episodic memory recall; and (7) the Functional Activities Questionnaire (FAQ), which assesses instrumental activities of daily living. Statistical Analysis Descriptive summary statistics were calculated, including means and standard deviations for continuous variables and frequencies and percentages for categorical variables. To explore the distribution of both CSF biomarkers and cognitive measures across cognitive status (i.e., CN, MCI, and AD), the violin plots were generated. The relationship between CSF biomarkers, cognitive measures or continuous demographic variables and cognitive status were assessed using one-way analysis of variance (ANOVA), and post hoc pairwise comparisons between cognitive statuses performed using t-test with Bonferroni correction to control for multiple testing. The association between the categorical demographic variable sex and cognitive status was assessed using the Chi-squared tests. The results were presented as frequencies and percentages, with p-value indicating the significance of association. The relationships between continuous variables CSF biomarkers and cognitive measures were visualized using scatter plots (Supplemental Fig. 1) and quantified using Pearson correlation analysis. Multivariable linear regression model was employed to explore the association of CSF biomarkers with cognitive assessments, adjusting for potential confounders such as age, sex, and education. All statistical analyses were performed using R statistical software (version 4.4.1, R Foundation for Statistical Computing, Vienna, Austria), with a 2-sided significance level of 5% for hypothesis tests. Standard Protocol Approvals, Registrations, and Patient Consents Data used in the preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (Adni.loni.usc.edu). The ADNI study was approved by the institutional review boards (IRBs) of all participating institutions, and written informed consent was obtained from all participants or their authorized representatives. This retrospective project used only deidentified, publicly available data. Therefore, our analysis was exempt from additional IRB review due to the extremely low likelihood of patient identification. Data Availability The data that support the study findings are available from the corresponding author upon reasonable request and IRB approval, as applicable. RESULTS A total of 665 individuals from the combined ADNI 1, 2, and GO database were included in this analysis, categorized into 3 groups: CN (n = 128), MCI (n = 175), and AD (n = 362). Pearson correlations were performed to examine the relationship between CSF biomarkers (Aβ₁₋₄₂, t-tau, p-tau-181) and various cognitive assessment scores across the groups. Summary Statistics and Associations Significant differences (p < 0.05) were observed for all variables analyzed (Table 1 ), including CSF biomarkers Aβ₁₋₄₂, t-tau, and p-tau-181, demographic factors, and cognitive measures CDRSB, ADAS-Cog11, ADAS-Cog13, MMSE, RAVLT Immediate, WMS-delayed recall, Trails B, and FAQ. These differences highlight the distinct profiles of cognitive and biomarker characteristics across the spectrum of diagnostic stages. Table 1 Summary statistics for the variables and results of associations between cognitive function and demographic and other variables. Variable* Cognitive function F or Chi-square statistic P-value CN MCI AD Age (years), mean (SD) 74.5 (5.81) 73.4 (7.04) 74.3 (7.77) 1.565 0.210 Education (years), mean (SD) 16.3 (2.61) 16.0 (2.82) 15.5 (2.82) 3.596 0.03 CSF Aβ₁₋₄₂ (pg/mL), mean (SD) 703.7 (188.2) 652.1 (170.3) 577.3 (161.4) 21.34 < 0.001 Total tau (pg/mL), mean (SD) 239.6 (100.5) 315.3 (137.4) 367.1 (134.3) 35.42 < 0.001 P-tau-181 (pg/mL), mean (SD) 23.39 (11.21) 31.71 (15.41) 37.05 (14.62) 32.95 < 0.001 CDRSB 0.059 (0.173) 1.627 (0.971) 4.377 (1.624) 626.5 < 0.001 ADAS-Cog11 6.276 (3.060) 11.22 (4.671) 19.19 (6.063) 286.4 < 0.001 ADAS-Cog13 9.792 (4.431) 18.27 (6.673) 29.49 (7.256) 358.9 < 0.001 MMSE 29.10 (1.169) 27.39 (1.886) 23.42 (1.928) 438.7 < 0.001 RAVLT-immediate 43.56 (9.826) 31.51 (8.989) 23.21 (6.726) 205.8 < 0.001 RAVLT-forgetting 3.797 (2.730) 4.898 (2.240) 4.474 (1.774) 11.71 < 0.001 WMS-delayed recall 12.92 (3.264) 5.243 (3.458) 1.331 (1.773) 537.3 < 0.001 Trails B 95.08 (48.27) 129.3 (70.02) 198.7 (86.83) 88.30 < 0.001 FAQ 0.344 (0.846) 3.754 (4.273) 12.79 (6.614) 320.0 < 0.001 Male, n (%) 59 (15.2) 99 (25.5) 230 (59.3) 12.15 0.002 Female, n (%) 69 (24.9) 76 (27.4) 132 (47.6) *Data are scores unless otherwise noted. Abbreviations: Aβ, amyloid-β; AD, Alzheimer’s disease; ADAS-COG, Alzheimer’s Disease Assessment Scale-Cognitive Subscale; CDRSB, Clinical Dementia Rating Scale Sum of Boxes; CN, cognitively normal; CSF, cerebrospinal fluid; FAQ, Functional Activities Questionnaire; MCI, mild cognitive impairment; MMSE, Mini-Mental State Exam; P-Tau, phosphorylated tau; RAVLT, Rey Auditory Verbal Learning Test; Trails B, Trail Making Test Part B. These group differences are further illustrated in Fig. 2 , which displays the distribution of CSF biomarkers and cognitive scores across CN, MCI, and AD groups, along with ANOVA and post-hoc comparisons. Regression Analysis Multiple linear regression analysis, adjusted for demographic variables (age, sex, education) (Table 2 ), confirmed that CSF Aβ₁₋₄₂ was significantly negatively associated with cognitive decline measures such as CDRSB (estimate=-0.002, p < 0.001), ADAS-Cog11 (Estimate=-0.009, p < 0.001), and ADAS-Cog13 (estimate=-0.014, p < 0.001). Similar trends were observed for t-tau and p-tau-181, which were positively associated with markers of cognitive impairment (e.g., CDRSB, ADAS-Cog13) and negatively associated with measures of preserved function (e.g., MMSE, RAVLT-immediate, WMS-delayed recall). Table 2 Linear regression analysis, adjusted for age, sex, and years of education Outcomes Predictors ABETA TAU PTAU Estimate(p-value) Estimate(p-value) Estimate(p-value) ALL CDRSB -0.00214(< 0.0001) 0.00395(< 0.0001) 0.03245(< 0.0001) ADAS11 -0.00836(< 0.0001) 0.01395(< 0.0001) 0.11320(< 0.0001) ADAS13 -0.01330(< 0.0001) 0.02175(< 0.0001) 0.18007(< 0.0001) MMSE 0.00372(< 0.0001) -0.00561(< 0.0001) -0.04760(< 0.0001) RAVLT_immediate 0.01140(< 0.0001) -0.02334(< 0.0001) -0.19740(< 0.0001) RAVLT_forgetting 0.00009(0.85924) 0.00229(0.00048) 0.02084(0.00041) LDELTOTAL 0.00673(< 0.0001) -0.01180(< 0.0001) -0.10256(< 0.0001) TRABSCOR -0.06912(0.00007) 0.11684(< 0.0001) 0.96313(< 0.0001) FAQ -0.00732(< 0.0001) 0.00985(< 0.0001) 0.08056(< 0.0001) CN CDRSB 0.00002(0.81161) -0.00008(0.63617) -0.00031(0.82998) ADAS11 -0.00394(0.00284) -0.00034(0.89390) -0.00147(0.94857) ADAS13 -0.00598(0.00159) 0.00057(0.87654) 0.00790(0.80899) MMSE -0.00071(0.18174) 0.00078(0.43762) 0.01077(0.23120) RAVLT_immediate 0.00298(0.47950) -0.00013(0.98665) 0.00598(0.93310) RAVLT_forgetting 0.00126(0.33646) 0.00161(0.51785) 0.01474(0.50726) LDELTOTAL 0.00196(0.17836) 0.00130(0.63855) 0.00845(0.73279) TRABSCOR -0.01103(0.62243) 0.04374(0.30035) 0.42602(0.25965) FAQ 0.00047(0.25457) 0.00127(0.10150) 0.00994(0.15210) MCI CDRSB -0.00038(0.20472) 0.00078(0.04252) 0.00617(0.07079) ADAS11 -0.00420(0.00355) 0.00434(0.01766) 0.03810(0.01898) ADAS13 -0.00674(0.00103) 0.00831(0.00143) 0.07228(0.00178) MMSE 0.00153(0.00747) -0.00224(0.00195) -0.02064(0.00132) RAVLT_immediate 0.00338(0.21659) -0.01129(0.00106) -0.09861(0.00128) RAVLT_forgetting -0.00014(0.84492) 0.00273(0.00173) 0.02339(0.00251) LDELTOTAL 0.00251(0.01589) -0.00489(0.00019) -0.04321(0.00020) TRABSCOR -0.03990(0.05990) 0.02857(0.28929) 0.22766(0.34143) FAQ -0.00318(0.01662) 0.00074(0.66173) 0.00534(0.72201) Dementia CDRSB 0.00029(0.71747) 0.00070(0.46936) 0.00123(0.89014) ADAS11 0.00242(0.41298) 0.00656(0.06848) 0.02669(0.41873) ADAS13 0.00104(0.76882) 0.00675(0.11796) 0.02469(0.53215) MMSE 0.00170(0.07012) -0.00057(0.61862) -0.00041(0.96888) RAVLT_immediate 0.00053(0.86846) -0.00323(0.40455) -0.00610(0.86327) RAVLT_forgetting 0.00022(0.79881) -0.00021(0.84391) 0.00142(0.88310) LDELTOTAL 0.00150(0.08020) 0.00064(0.54699) 0.00822(0.39290) TRABSCOR 0.02515(0.54891) 0.04295(0.40232) 0.20139(0.66727) FAQ 0.00063(0.84653) -0.00065(0.87079) -0.01742(0.63031) Dementia & MCI CDRSB -0.00176(0.00009) 0.00227(0.00007) 0.01735(0.00070) ADAS11 -0.00669(0.00003) 0.00921(0.00001) 0.06978(0.00013) ADAS13 -0.01067(< 0.0001) 0.01374(< 0.0001) 0.10771(0.00001) MMSE 0.00387(< 0.0001) -0.00389(< 0.0001) -0.03283(0.00001) RAVLT_immediate 0.00740(0.00116) -0.01331(< 0.0001) -0.10915(0.00002) RAVLT_forgetting 0.00034(0.52916) 0.00145(0.03260) 0.01360(0.02519) LDELTOTAL 0.00441(< 0.0001) -0.00514(< 0.0001) -0.04447(0.00001) TRABSCOR -0.06251(0.00242) 0.07558(0.00397) 0.58157(0.01349) FAQ -0.00707(0.00003) 0.00534(0.01323) 0.04017(0.03768) Adjusted for Age, Sex, and Education Abbreviations: CDRSB, Clinical Dementia Rating Scale Sum of Boxes; CN, cognitively normal; CSF, cerebrospinal fluid; FAQ, Functional Activities Questionnaire; MCI, mild cognitive impairment; MMSE, Mini-Mental State Exam; P-Tau, phosphorylated tau; RAVLT, Rey Auditory Verbal Learning Test; Trails B, Trail Making Test Part B. Correlation Analysis MCI Cohort In the MCI group, significant correlations were observed between CSF biomarkers and cognitive assessments. Negative correlations were noted between CSF Aβ₁₋₄₂ and the following cognitive measures: ADAS-Cog11 (r=-0.164, p = 0.002), ADAS-Cog13 (r=-0.181, p = 0.001), Trails B (r=-0.110, p = 0.04), and FAQ (r=-0.131, p = 0.01). Conversely, positive correlations were identified between CSF Aβ₁₋₄₂ and assessments of memory and global cognitive function, including MMSE (r = 0.149, p = 0.004) and WMS-delayed recall (r = 0.116, p = 0.03). For CSF t-tau and p-tau-181, significant positive correlations were found with several cognitive measures indicative of cognitive decline (Fig. 1 ): CDRSB (t-tau: r = 0.105, p = 0.047, ADAS-Cog11 (t-tau: r = 0.114, p = 0.03; p-tau-181: r = 0.114, p = 0.03), ADAS-Cog13 (t-tau: r = 0.165, p = 0.002; p-tau-181: r = 0.162, p = 0.002), RAVLT Forgetting (t-tau: r = 0.173, p = 0.001; ptau-181: r = 0.167, p = 0.001). Negative correlations were observed between t-tau/p-tau-181 and cognitive scores reflecting preserved function, including MMSE (t-tau: r=-0.175, p = 0.001; p-tau181: r=-0.180, p = 0.001), RAVLT-immediate (t-tau: r=-0.128, p = 0.02; p-tau-181: r=-0.130, p = 0.01), and WMS-delayed recall (t-tau: r=-0.237, p < 0.001; p-tau-181: r=-0.235, p < 0.001). AD Cohort In the AD group, significant correlations were sparse. No statistically significant relationships were observed between CSF Aβ₁₋₄₂, t-tau, p-tau-181, and any of the cognitive assessment scores (Fig. 1 ). Combined MCI and AD Cohort When the MCI and AD cohorts were grouped, significant correlations were observed between CSF biomarkers and cognitive measures. CSF Aβ₁₋₄₂ demonstrated significant negative correlations with measures of cognitive decline, including CDRSB (r=-0.160, p < 0.001), ADASCog11 (r=-0.178, p < 0.001), ADAS-Cog13 (r=-0.206, p < 0.001), Trails B (r=-0.124, p = 0.004), and FAQ (r=-0.175, p < 0.001). Positive correlations were noted with global and memory-specific cognitive scores such as MMSE (r = 0.245, p < 0.001), RAVLT Immediate (r = 0.157, p < 0.001), and WMS-delayed recall (r = 0.199, p < 0.001). CSF t-tau and p-tau-181 exhibited significant correlations with all cognitive measures examined. Both biomarkers were positively correlated with measures of cognitive decline, including CDRSB (t-tau: r = 0.191, p < 0.001; p-tau: r = 0.165, p < 0.001), ADAS-Cog11 (t-tau: r = 0.194, p < 0.001; p-tau-181: r = 0.165, p < 0.001), ADAS-Cog13 (t-tau: r = 0.221, p < 0.001; p-tau181: r = 0.194, p < 0.001), RAVLT Forgetting (t-tau: r = 0.110, p = 0.01; p-tau-181: r = 0.113, p = 0.009), Trails B (t-tau: r = 0.144, p = 0.001; p-tau-181: r = 0.125, p = 0.004), and FAQ (t-tau: r = 0.116, p = 0.007; p-tau-181: r = 0.098, p = 0.02). Conversely, t-tau and p-tau-181 demonstrated negative correlations with scores indicating better cognitive function (Fig. 1 ), including MMSE (t-tau: r=-0.209, p < 0.001; p-tau-181: r = 0.198, p < 0.001), RAVLT Immediate (t-tau: r=-0.157, p < 0.001; p-tau-181: r=-0.145, p = 0.001), and WMS-delayed recall (t-tau: r=-0.244, p < 0.001; p-tau-181: r=-0.235, p < 0.001 CN Cohort Significant but modest correlations were found between CSF biomarkers and cognitive measures in the CN group (Fig. 1 ). CSF Aβ₁₋₄₂ demonstrated negative correlations with ADAS-Cog11 (r=-0.293, p = 0.001) and ADAS-Cog13 (r=-0.304, p < 0.001). Combined CN/MCI/AD Cohort The strongest relationships between biomarkers and cognitive tests were observed in this cohort, in which all 3 diagnostic groups were combined. CSF Aβ₁₋₄₂ demonstrated significant negative correlations with measures of cognitive decline, including CDRSB (r=-0.206, p < 0.001), ADAS-Cog11 (r=-0.238, p < 0.001), ADAS-Cog13 (r=-0.266, p < 0.001), Trails B (r=-0.159, p < 0.001), and FAQ (r=-0.202, p < 0.001). Positive correlations were noted with global and memory-specific cognitive scores such as MMSE (r = 0.256, p < 0.001), RAVLT Immediate (r = 0.217, p < 0.001), and WMS-delayed recall (r = 0.253, p < 0.001). CSF t-tau and p-tau-181 again exhibited significant correlations with all cognitive measures examined. Both biomarkers were positively correlated with measures of cognitive decline, including CDRSB (t-tau: r = 0.286, p < 0.001; p-tau: r = 0.264, p < 0.001), ADAS-Cog11 (t-tau: r = 0.276, p < 0.001; p-tau-181: r = 0.251, p < 0.001), ADAS-Cog13 (t-tau: r = 0.308, p < 0.001; ptau-181: r = 0.285, p < 0.001), RAVLT-forgetting (t-tau: r = 0.143, p < 0.001; p-tau-181: r = 0.144, p < 0.05), Trails B (t-tau: r = 0.208, p < 0.001; p-tau-181: r = 0.192, p < 0.001), and FAQ (t-tau: r = 0.202, p < 0.001; p-tau-181: r = 0.185, p < 0.001). Conversely, t-tau and p-tau-181 demonstrated negative correlations with scores indicating better cognitive function (Fig. 1 ), including MMSE (t-tau: r=-0.282, p < 0.001; p-tau-181: r = 0.268, p < 0.001), RAVLT Immediate (t-tau: r=-0.257, p < 0.001; p-tau-181: r=-0.245, p < 0.001), and WMS-delayed recall (t-tau: r=-0.331, p < 0.001; p-tau-181: r=-0.323, p < 0.001). DISCUSSION This study examined the relationships between CSF biomarkers (Aβ₁₋₄₂, t-tau, p-tau-181) and cognitive performance across CN, MCI, and AD groups, as well as in a combined MCI/AD cohort. Our findings offer a comprehensive view of biomarker-cognition relationships across the AD continuum, demonstrating that these associations are most pronounced at the MCI stage, where therapeutic intervention may hold the greatest potential impact. Among the most significant findings, Aβ₁₋₄₂ demonstrated correlations with cognitive measures in the MCI group, including negative associations with ADAS-Cog13, ADAS-Cog11, Trails B, and FAQ, and positive associations with MMSE and WMS-delayed recall. Similarly, t-tau and p-tau-181 showed significant correlations with both markers of cognitive decline (e.g., ADAS-Cog13) and preserved cognitive function (e.g., MMSE and RAVLT-immediate). These findings suggest that biomarker levels, particularly in the MCI stage, are closely linked to cognitive performance and may serve as sensitive indicators of early cognitive decline. Additionally, these results suggest that Aβ₁₋₄₂ showed similar significant associations with cognitive measures as tau and p-tau-18, contradicting previous literature that suggested tau to have stronger relationships with cognition 24 , 25 . These findings partially support our initial hypothesis: while p-tau and t-tau demonstrated robust associations with cognitive decline in the MCI group, Aβ₁₄₂ also showed significant correlations, particularly with cognitive measures reflecting preserved function. This finding suggests that each biomarker may capture different, yet complementary, aspects of cognitive impairment, and that their relevance may vary by disease stage and cognitive domain affected. The combined MCI and AD analysis revealed robust correlations across all cognitive measures and biomarkers, reinforcing the importance of this stage in understanding the progression of AD. These findings further highlight the utility of Aβ₁₋₄₂ as a predictor of preserved cognitive function and t-tau and p-tau-181 as markers of neurodegeneration (Fig. 1 ). Interestingly, in the AD cohort, significant correlations were sparse. Neither Aβ₁₋₄₂ nor t-tau or p-tau-181 had any significant correlations. These results suggest that in advanced stages of AD, other pathological processes may play a more dominant role in cognitive decline, overshadowing the influence of traditional biomarkers. In the CN group, significant correlations were limited to Aβ₁₋₄₂, which was negatively associated with ADAS-Cog13 and ADAS-Cog11. These findings indicate that even in individuals with normal cognition, subtle biomarker-cognition relationships can be detected, potentially offering opportunities for early intervention. This novel study assesses the relationship between well-known cognitive tests and CSF biomarkers of AD in the ADNI dataset. Our findings contribute to the growing knowledge base of the pathophysiology of AD progression. Previous studies have shown tau biomarkers (on PET and in CSF) or a ratio of Aβ/tau to be more closely related to cognition than Aβ alone. 26 , 27 Vemuri and colleagues also illustrated that CSF biomarkers and cognition were not correlated, supporting the use of alternate imaging markers such as MRI of brain atrophy. 19 Our results illustrate a strong association between Aβ and cognition, suggesting that amyloid accumulation may play a significant role in cognitive decline. Although the results of this study are promising, we recognize that it has limitations. One primary limitation is the study’s cross-sectional design due to limited sample size of individuals meeting our inclusion criteria. As a result, we cannot conclude how cognition changes over time relate to CSF biomarkers. Over time, the biomarker profile of a patient may change differentially compared to their performance in various domains of cognition. Since ADNI is a large, multi-site data-collecting initiative, there may have been variations in biospecimen collection and processing protocols from 1 site to the next. With the quantity of data collected and the detailed protocol guidelines provided by ADNI, there might have been errors that could have affected our results. Regarding the ADNI protocol, another potential limitation of the study is the diagnostic categorization of patients as having CN, MCI, or AD, which an on-site physician performs. Although there are specific diagnostic guidelines for physicians, some subjectivity may be inherent in determining this diagnosis. The use of ADNI data also presents a few inherent limitations in its generalizability to the national population and selection biases of subjects involved. Lastly, it would be beneficial to repeat this analysis using PET data since research has shown imaging markers to be increasingly valuable in AD diagnosis, as well as blood-based biomarkers which are less invasive and less costly. 19 , 28 Additionally, our study did not include emerging plasma biomarkers, which are now increasingly validated for diagnostic use. Integrating fluid and imaging markers longitudinally will be essential to establish their combined predictive value. Future research should aim to continue improving our diagnostic accuracy of AD, especially early in the disease course, so adequate measures can be taken to slow progression. It would be beneficial to further research CSF biomarker changes over time and how they relate to the clinical course of AD. Additionally, exploring the role of newer cognitive assessment tools and digital phenotyping may enhance our ability to detect subtle cognitive changes in the preclinical and early MCI stages. CONCLUSION In summary, the findings highlight the nuanced relationships between CSF biomarkers Aβ₁₋₄₂, t-tau, and p-tau-181 and cognitive performance across the spectrum of cognitive impairment. We emphasize the importance of early identification and targeted interventions during the MCI stage, where biomarker-cognition correlations are strongest. Future longitudinal studies should aim to validate these findings and explore the predictive value of the biomarkers in disease progression and therapeutic response. ABBREVIATIONS Aβ, amyloid-β; AD, Alzheimer’s Disease; ADNI, Alzheimer’s Disease Neuroimaging Initiative; CN, cognitively normal; CSF, cerebrospinal fluid; IRB, institutional review board; MCI, mild cognitive impairment; MMSE, Mini-Mental State Exam; MoCA, Montreal Cognitive Assessment; MRI, magnetic resonance imaging; PET, positron emission tomography; p-tau, phosphorylated tau; t-tau, total tau; WMS, Wechsler Memory Scale Declarations Ethics approval and consent to participate Data used in this study were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). The ADNI study was approved by the institutional review boards of all participating institutions, and written informed consent was obtained from all participants or their authorized representatives. Because the present analysis used only deidentified, publicly available data, additional ethical approval and consent were not required. Consent for publication Not applicable Availability of data and materials Data used in the preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). The ADNI data are publicly available to qualified investigators upon registration and compliance with data use policies. Additional data generated or analyzed during the current study are available from the corresponding author upon reasonable request. Competing interests Bitar, Alban, Beyene and DeCourt have no disclosures to report. Dr. Sabbagh discloses that he consults for Alzheon, Athira, Biogen, Roche-Genentech, Synaptogenix, Novo Nordisk, Signant Health, Prothena, Eisai, GSK, Abbvie, Lilly, and Neurotherapia. Funding The authors received no specific funding for this work. Data collection and sharing for this project were funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and the Department of Defense ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from private pharmaceutical companies and nonprofit organizations, as detailed at adni.loni.usc.edu. Authors' contributions Gabriel Bitar: Data analysis, literature review, results interpretation, manuscript writing/editing. Sierra Alban: Data extraction, literature review, interpretation of findings, manuscript writing/editing Kassu Beyene: Data curation, statistical analysis, figure/table preparation. Boris Decourt: Supervision, conceptual guidance, manuscript revisions. Shervin Harirchian: Manuscript writing and editing. Marwan Sabbagh: Critical supervision, conceptual guidance, manuscript revisions. All authors: Reviewed and approved the final manuscript and agree to be accountable for all aspects of the work. Acknowledgements Data collection and sharing for this project were funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and the Department of Defense ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association, Alzheimer’s Drug Discovery Foundation, Araclon Biotech, BioClinica, Inc., Biogen, Bristol Myers Squibb, CereSpir, Inc., Cogstate, Eisai Inc., Elan Pharmaceuticals, Inc., Eli Lilly and Company, EuroImmun, F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc., Fujirebio, GE Healthcare, IXICO Ltd., Janssen Alzheimer Immunotherapy Research & Development, LLC., Johnson & Johnson Pharmaceutical Research & Development LLC., Lumosity, Lundbeck, Merck & Co., Inc., Meso Scale Diagnostics, LLC., NeuroRx Research, Neurotrack Technologies, Novartis Pharmaceuticals Corporation, Pfizer Inc., Piramal Imaging, Servier, Takeda Pharmaceutical Company, and Transition Therapeutics. The Canadian Institutes of Health Research provides funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. 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A., Jacobs, H. I. L., Schultz, A. P., Sepulcre, J., Becker, J. A., Cosio, D. M. O., Farrell, M., Quiroz, Y. T., Mormino, E. C., Buckley, R. F., Papp, K. v, Amariglio, R. A., Dewachter, I., Ivanoiu, A., Huijbers, W., Hedden, T., Marshall, G. A., Chhatwal, J. P., … Johnson, K. (2019). Association of Amyloid and Tau With Cognition in Preclinical Alzheimer Disease: A Longitudinal Study. JAMA Neurology , 76 (8), 915–924. https://doi.org/10.1001/jamaneurol.2019.1424 Xiong, C., Jasielec, M. S., Weng, H., Fagan, A. M., Benzinger, T. L. S., Head, D., Hassenstab, J., Grant, E., Sutphen, C. L., Buckles, V., Moulder, K. L., & Morris, J. C. (2016). Longitudinal relationships among biomarkers for Alzheimer disease in the Adult Children Study. Neurology , 86 (16), 1499–1506. https://doi.org/10.1212/WNL.0000000000002593 Malpas CB, Saling MM, Velakoulis D, Desmond P, O'Brien TJ, Alzheimer's Disease Neuroimaging I. Tau and Amyloid-beta Cerebrospinal Fluid Biomarkers have Differential Relationships with Cognition in Mild Cognitive Impairment. J Alzheimers Dis 2015;47:965-975. Timmers M, Tesseur I, Bogert J, et al. Relevance of the interplay between amyloid and tau for cognitive impairment in early Alzheimer's disease. Neurobiol Aging 2019;79:131-141. Bucci M, Chiotis K, Nordberg A, Alzheimer's Disease Neuroimaging I. Alzheimer's disease profiled by fluid and imaging markers: tau PET best predicts cognitive decline. Mol Psychiatry 2021;26:5888-5898. Additional Declarations Competing interest reported. Dr. Sabbagh discloses that he consults for Alzheon, Athira, Biogen, Roche-Genentech, Synaptogenix, Novo Nordisk, Signant Health, Prothena, Eisai, GSK, Abbvie, Lilly, and Neurotherapia. 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1","display":"","copyAsset":false,"role":"figure","size":161714,"visible":true,"origin":"","legend":"\u003cp\u003ePearson correlation matrices by cohort. Red asterisks indicate statistical significance (p\u0026lt;0.05). \u003cem\u003eUsed with permission from Barrow Neurological Institute, Phoenix, Arizona.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8168314/v1/ec19a380893f3bafc66888c1.png"},{"id":97122346,"identity":"421ec609-0605-44ac-b55f-4d9cb0b2febf","added_by":"auto","created_at":"2025-12-01 07:56:36","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":158047,"visible":true,"origin":"","legend":"\u003cp\u003eGroup differences in CSF biomarkers and cognitive measures across diagnostic categories (CN, MCI, AD)\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8168314/v1/b073a9e07db055f23551b402.png"},{"id":99315014,"identity":"ccb3208f-3f70-4d09-89b0-c3bc5bc76638","added_by":"auto","created_at":"2025-12-31 16:25:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1238814,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8168314/v1/7df41d24-2b86-4a4a-bd05-a29cd93aef0b.pdf"},{"id":97122348,"identity":"a4f4110a-3b62-48a9-981f-afcc27c1591f","added_by":"auto","created_at":"2025-12-01 07:56:36","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":4552318,"visible":true,"origin":"","legend":"","description":"","filename":"SUPPLEMENTALMATERIALS.docx","url":"https://assets-eu.researchsquare.com/files/rs-8168314/v1/990a375c879ae79366ea7569.docx"}],"financialInterests":"Competing interest reported. Dr. Sabbagh discloses that he consults for Alzheon, Athira, Biogen, Roche-Genentech, Synaptogenix, Novo Nordisk, Signant Health, Prothena, Eisai, GSK, Abbvie, Lilly, and Neurotherapia.","formattedTitle":"Correlations of CSF Biomarkers of Alzheimer’s Disease with Cognitive Measures in MCI and AD Dementia: A Cross-Sectional Analysis","fulltext":[{"header":"BACKGROUND","content":"\u003cp\u003eAs our aging population continues to increase, Alzheimer\u0026rsquo;s disease (AD) prevalence and the associated cost burden rise dramatically.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e The hallmarks of AD include amyloid-β (Aβ) plaques and neurofibrillary tangles of tau accumulating in the brain.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e Current clinical guidelines use neuroimaging and cognitive testing, such as the Montreal Cognitive Assessment and Mini Mental State Exam (MMSE), among others, to diagnose patients and determine the level of decline.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e Often, by the time cognitive decline has begun, significant brain changes have already occurred during the \u0026ldquo;preclinical\u0026rdquo; phase of the disease.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e In recent years, researchers have studied the potential of biomarkers to help diagnose AD earlier and initiate interventions as soon as possible, perhaps before clinical manifestations occur.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e Despite advances in biomarker discovery it remains unclear how traditional CSF biomarkers relate to cognition at different stages of Alzheimer\u0026rsquo;s Disease within a well-characterized cohort.\u003c/p\u003e\u003cp\u003eCerebrospinal fluid (CSF) biomarkers consisting of Aβ₁₋₄₂, total-tau (t-tau), and phosphorylated-tau (p-tau) have been shown to reflect the disease course and may even indicate preclinical disease.\u003csup\u003e\u003cspan additionalcitationids=\"CR9 CR10\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e Typically, decreased CSF Aβ₁₋₄₂ and increased t-tau and p-tau reflect AD plaque accumulation and neurodegeneration, respectively. While these biomarkers are widely used for research purposes, positron emission tomography (PET), CSF, and blood-based tests are being used more commonly in clinical settings for diagnosis.\u003csup\u003e\u003cspan additionalcitationids=\"CR13 CR14\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003ePrevious studies have shown tau, on brain PET and in CSF, to be more closely related to\u003c/p\u003e\u003cp\u003ethe degree of cognitive decline and likely precedes symptom onset compared to Aβ.\u003csup\u003e5,16,17\u003c/sup\u003e However, some investigators reported conflicting results, suggesting a minimal relationship between CSF biomarkers and cognitive performance or proposing alternate biomarker methods altogether.\u003csup\u003e\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e Hansson and colleagues proposed that CSF biomarkers agree well with imaging in predicting future decline.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e Given the discordant results in the literature, there are gaps in our understanding of the role of CSF Aβ and tau in cognitive decline.\u003c/p\u003e\u003cp\u003eOur study is one of the first to examine the relationships between CSF biomarkers in the\u003c/p\u003e\u003cp\u003eAlzheimer\u0026rsquo;s Disease Neuroimaging Initiative (ADNI) cohort and all available cognitive tests. The primary aim of this study was to elucidate the cross-sectional relationships between CSF biomarkers and cognitive performance across different stages of cognitive impairment. By leveraging an extensive, well-characterized ADNI dataset, we sought to clarify discordances in prior literature regarding biomarker significance and provide insights into their clinical utility. The objective of this study was to systematically examine cross-sectional relationships between CSF Aβ₁₋₄₂, t-tau, and p-tau-181 and multiple domains of cognition across CN, MCI, and AD groups using the ADNI dataset. In accordance with prior literature, we hypothesize that CSF biomarkers, particularly p-tau and t-tau, will demonstrate stronger correlations with cognitive decline measures than Aβ₁₋₄₂, and that these associations will vary across different cognitive domains and stages of impairment.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003eThe study evaluates the associations between CSF biomarker expressions at baseline within various cognitive domains in a large, robust dataset. We hypothesize that CSF biomarkers, particularly p-tau and t-tau, would demonstrate stronger correlations with cognitive decline measures than Aβ₁₋₄₂, particularly in the cohort with mild cognitive impairment (MCI) and that these associations may vary across the spectrum of impairment.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eSubjects and Variables\u003c/h2\u003e\u003cp\u003eStudy data were obtained from the combined ADNI 1, 2, and GO database (adni.loni.usc.edu). The ADNI was launched in 2003 as a public-private partnership led by Principal Investigator Michael W. Weiner, MD. The primary goal of the ADNI has been to assess whether serial magnetic resonance imaging (MRI), (PET, other biological markers, and clinical and neuropsychological assessment can be combined to measure the progression of mild cognitive impairment (MCI) and early AD.\u003c/p\u003e\u003cp\u003eThe study cohorts consisted of people who were classified as cognitively normal (CN), having MCI, or having AD on the basis of standardized diagnostic criteria. For inclusion in the study, participants had to be 55\u0026ndash;90 years of age and have a clinical diagnosis of AD, MCI, or CN, with no history of other neurological disorders. Exclusion criteria included conditions that could confound the results, such as major psychiatric disorders or significant systemic illnesses as defined by the ADNI protocol. We additionally excluded those with a history of stroke or a cerebrovascular event. MCI was diagnosed on the basis of evidence of concern for cognitive changes and diminished performance in 1 or more cognitive domains but with generally intact functional ability and lack of significant impairment of social or occupational functioning.\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e AD was diagnosed as cognitive challenges interfering with daily activities, a decline from previous functioning, and cognitive impairments detected on cognitive testing.\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e Participants with normal cognition did not exhibit significant cognitive changes that aligned with MCI or AD diagnoses.\u003c/p\u003e\u003cp\u003eData were retrieved in August 2024 from ADNIMERGE, a curated database that consolidates key clinical, biomarker, and imaging data from the ADNI dataset. The dataset included CSF biomarker levels, cognitive assessments, and demographic variables. A total of\u003c/p\u003e\u003cp\u003e1017 patients were initially queried, with 976 remaining after selection for those with available CSF biomarker data. These 976 patients were further selected on the basis of their amyloid status to 665, which were considered Aβ+, as amyloid positivity is a key biomarker criterion for defining Alzheimer\u0026rsquo;s disease-specific cohorts. The Aβ\u0026thinsp;+\u0026thinsp;cut-off was determined by methods established and validated, using a cut-off of \u0026le;\u0026thinsp;980 pg/mL.\u003csup\u003e23\u003c/sup\u003e The cut-off was determined on the basis of concordance with amyloid PET data in the BioFINDER cohort and then validated in an independent ADNI cohort.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e Demographic variables such as age and education were matched across diagnostic groups (CN, MCI, AD).\u003c/p\u003e\u003cp\u003eCSF data were acquired from the ADNIMERGE. The data were analyzed using the xMAP Luminex immunoassay platform, which was published and validated by the University of Pennsylvania Biomarker Core. Detailed descriptions of the assay and its validation can be found in the literature and ADNI protocol documents.\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eCognitive function was assessed using 7 standardized instruments including: (1) the Clinical Dementia Rating Scale Sum of Boxes (CDRSB), which measures global cognitive and functional impairment; (2) the Alzheimer\u0026rsquo;s Disease Assessment Scale-Cognitive Subscale (ADAS-Cog 11, ADAS-Cog 13), which evaluates cognitive performance, including memory, language, and praxis; (3) the MMSE, which screens for global cognitive function; (4) the Rey\u003c/p\u003e\u003cp\u003e Auditory Verbal Learning Test (RAVLT), which assesses verbal memory and learning; (5) the\u003c/p\u003e\u003cp\u003eTrail Making Test Part B (Trails B), which evaluates executive function and cognitive flexibility; (6) the Logical Memory Delayed Recall (LDELTOTAL), which measures delayed episodic memory recall; and (7) the Functional Activities Questionnaire (FAQ), which assesses instrumental activities of daily living.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eDescriptive summary statistics were calculated, including means and standard deviations for continuous variables and frequencies and percentages for categorical variables. To explore the distribution of both CSF biomarkers and cognitive measures across cognitive status (i.e., CN, MCI, and AD), the violin plots were generated. The relationship between CSF biomarkers, cognitive measures or continuous demographic variables and cognitive status were assessed using one-way analysis of variance (ANOVA), and post hoc pairwise comparisons between cognitive statuses performed using t-test with Bonferroni correction to control for multiple testing. The association between the categorical demographic variable sex and cognitive status was assessed using the Chi-squared tests. The results were presented as frequencies and percentages, with p-value indicating the significance of association. The relationships between continuous variables CSF biomarkers and cognitive measures were visualized using scatter plots (Supplemental Fig.\u0026nbsp;1) and quantified using Pearson correlation analysis. Multivariable linear regression model was employed to explore the association of CSF biomarkers with cognitive assessments, adjusting for potential confounders such as age, sex, and education. All statistical analyses were performed using R statistical software (version 4.4.1, R Foundation for Statistical Computing, Vienna, Austria), with a 2-sided significance level of 5% for hypothesis tests.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eStandard Protocol Approvals, Registrations, and Patient Consents\u003c/h3\u003e\n\u003cp\u003eData used in the preparation of this article were obtained from the Alzheimer\u0026rsquo;s Disease Neuroimaging Initiative (ADNI) database (Adni.loni.usc.edu). The ADNI study was approved by the institutional review boards (IRBs) of all participating institutions, and written informed consent was obtained from all participants or their authorized representatives. This retrospective project used only deidentified, publicly available data. Therefore, our analysis was exempt from additional IRB review due to the extremely low likelihood of patient identification.\u003c/p\u003e\n\u003ch3\u003eData Availability\u003c/h3\u003e\n\u003cp\u003eThe data that support the study findings are available from the corresponding author upon reasonable request and IRB approval, as applicable.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003eA total of 665 individuals from the combined ADNI 1, 2, and GO database were included in this analysis, categorized into 3 groups: CN (n\u0026thinsp;=\u0026thinsp;128), MCI (n\u0026thinsp;=\u0026thinsp;175), and AD (n\u0026thinsp;=\u0026thinsp;362). Pearson correlations were performed to examine the relationship between CSF biomarkers (A\u0026beta;₁₋₄₂, t-tau, p-tau-181) and various cognitive assessment scores across the groups.\u003c/p\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eSummary Statistics and Associations\u003c/h2\u003e\n \u003cp\u003eSignificant differences (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were observed for all variables analyzed (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e), including CSF biomarkers A\u0026beta;₁₋₄₂, t-tau, and p-tau-181, demographic factors, and cognitive measures CDRSB, ADAS-Cog11, ADAS-Cog13, MMSE, RAVLT Immediate, WMS-delayed recall, Trails B, and FAQ. These differences highlight the distinct profiles of cognitive and biomarker characteristics across the spectrum of diagnostic stages.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"char\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSummary statistics for the variables and results of associations between cognitive function and demographic and other variables.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eVariable*\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eCognitive function\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eF or Chi-square statistic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCN\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMCI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAD\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge (years), mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e74.5 (5.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e73.4 (7.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e74.3 (7.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.565\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.210\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEducation (years), mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.3 (2.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.0 (2.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.5 (2.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.596\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCSF A\u0026beta;₁₋₄₂ (pg/mL), mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e703.7 (188.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e652.1 (170.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e577.3 (161.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal tau (pg/mL), mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e239.6 (100.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e315.3 (137.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e367.1 (134.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP-tau-181 (pg/mL), mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23.39 (11.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31.71 (15.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37.05 (14.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCDRSB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.059 (0.173)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.627 (0.971)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.377 (1.624)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e626.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eADAS-Cog11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.276 (3.060)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.22 (4.671)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.19 (6.063)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e286.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eADAS-Cog13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.792 (4.431)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18.27 (6.673)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29.49 (7.256)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e358.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMMSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29.10 (1.169)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27.39 (1.886)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23.42 (1.928)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e438.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRAVLT-immediate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43.56 (9.826)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31.51 (8.989)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23.21 (6.726)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e205.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRAVLT-forgetting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.797 (2.730)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.898 (2.240)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.474 (1.774)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWMS-delayed recall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.92 (3.264)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.243 (3.458)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.331 (1.773)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e537.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTrails B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e95.08 (48.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e129.3 (70.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e198.7 (86.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e88.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFAQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.344 (0.846)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.754 (4.273)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.79 (6.614)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e320.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e59 (15.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e99 (25.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e230 (59.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e12.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e69 (24.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e76 (27.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e132 (47.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003e*Data are scores unless otherwise noted.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cstrong\u003eAbbreviations:\u0026nbsp;\u003c/strong\u003eA\u0026beta;, amyloid-\u0026beta;; AD, Alzheimer\u0026rsquo;s disease; ADAS-COG, Alzheimer\u0026rsquo;s Disease Assessment Scale-Cognitive Subscale; CDRSB, Clinical Dementia Rating Scale Sum of Boxes; CN, cognitively normal; CSF, cerebrospinal fluid; FAQ, Functional Activities Questionnaire; MCI, mild cognitive impairment; MMSE, Mini-Mental State Exam; P-Tau, phosphorylated tau; RAVLT, Rey Auditory Verbal Learning Test; Trails B, Trail Making Test Part B.\u003c/p\u003e\n \u003cp\u003eThese group differences are further illustrated in Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, which displays the distribution of CSF biomarkers and cognitive scores across CN, MCI, and AD groups, along with ANOVA and post-hoc comparisons.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eRegression Analysis\u003c/h3\u003e\n\u003cp\u003eMultiple linear regression analysis, adjusted for demographic variables (age, sex, education) (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e), confirmed that CSF A\u0026beta;₁₋₄₂ was significantly negatively associated with cognitive decline measures such as CDRSB (estimate=-0.002, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), ADAS-Cog11 (Estimate=-0.009, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and ADAS-Cog13 (estimate=-0.014, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Similar trends were observed for t-tau and p-tau-181, which were positively associated with markers of cognitive impairment (e.g., CDRSB, ADAS-Cog13) and negatively associated with measures of preserved function (e.g., MMSE, RAVLT-immediate, WMS-delayed recall).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eLinear regression analysis, adjusted for age, sex, and years of education\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eOutcomes\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003ePredictors\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eABETA\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTAU\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePTAU\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEstimate(p-value)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEstimate(p-value)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEstimate(p-value)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eALL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCDRSB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.00214(\u0026lt;\u0026thinsp;0.0001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00395(\u0026lt;\u0026thinsp;0.0001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03245(\u0026lt;\u0026thinsp;0.0001)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eADAS11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.00836(\u0026lt;\u0026thinsp;0.0001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01395(\u0026lt;\u0026thinsp;0.0001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.11320(\u0026lt;\u0026thinsp;0.0001)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eADAS13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.01330(\u0026lt;\u0026thinsp;0.0001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02175(\u0026lt;\u0026thinsp;0.0001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.18007(\u0026lt;\u0026thinsp;0.0001)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMMSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00372(\u0026lt;\u0026thinsp;0.0001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.00561(\u0026lt;\u0026thinsp;0.0001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.04760(\u0026lt;\u0026thinsp;0.0001)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRAVLT_immediate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01140(\u0026lt;\u0026thinsp;0.0001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.02334(\u0026lt;\u0026thinsp;0.0001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.19740(\u0026lt;\u0026thinsp;0.0001)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRAVLT_forgetting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00009(0.85924)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00229(0.00048)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02084(0.00041)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLDELTOTAL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00673(\u0026lt;\u0026thinsp;0.0001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.01180(\u0026lt;\u0026thinsp;0.0001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.10256(\u0026lt;\u0026thinsp;0.0001)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTRABSCOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.06912(0.00007)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.11684(\u0026lt;\u0026thinsp;0.0001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.96313(\u0026lt;\u0026thinsp;0.0001)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFAQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.00732(\u0026lt;\u0026thinsp;0.0001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00985(\u0026lt;\u0026thinsp;0.0001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.08056(\u0026lt;\u0026thinsp;0.0001)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eCN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCDRSB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00002(0.81161)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.00008(0.63617)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.00031(0.82998)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eADAS11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.00394(0.00284)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.00034(0.89390)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.00147(0.94857)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eADAS13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.00598(0.00159)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00057(0.87654)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00790(0.80899)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMMSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.00071(0.18174)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00078(0.43762)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01077(0.23120)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRAVLT_immediate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00298(0.47950)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.00013(0.98665)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00598(0.93310)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRAVLT_forgetting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00126(0.33646)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00161(0.51785)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01474(0.50726)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLDELTOTAL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00196(0.17836)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00130(0.63855)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00845(0.73279)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTRABSCOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.01103(0.62243)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.04374(0.30035)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.42602(0.25965)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFAQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00047(0.25457)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00127(0.10150)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00994(0.15210)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eMCI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCDRSB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.00038(0.20472)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00078(0.04252)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00617(0.07079)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eADAS11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.00420(0.00355)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00434(0.01766)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03810(0.01898)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eADAS13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.00674(0.00103)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00831(0.00143)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.07228(0.00178)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMMSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00153(0.00747)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.00224(0.00195)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.02064(0.00132)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRAVLT_immediate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00338(0.21659)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.01129(0.00106)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.09861(0.00128)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRAVLT_forgetting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.00014(0.84492)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00273(0.00173)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02339(0.00251)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLDELTOTAL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00251(0.01589)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.00489(0.00019)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.04321(0.00020)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTRABSCOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.03990(0.05990)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02857(0.28929)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.22766(0.34143)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFAQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.00318(0.01662)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00074(0.66173)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00534(0.72201)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eDementia\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCDRSB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00029(0.71747)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00070(0.46936)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00123(0.89014)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eADAS11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00242(0.41298)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00656(0.06848)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02669(0.41873)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eADAS13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00104(0.76882)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00675(0.11796)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02469(0.53215)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMMSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00170(0.07012)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.00057(0.61862)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.00041(0.96888)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRAVLT_immediate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00053(0.86846)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.00323(0.40455)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.00610(0.86327)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRAVLT_forgetting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00022(0.79881)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.00021(0.84391)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00142(0.88310)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLDELTOTAL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00150(0.08020)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00064(0.54699)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00822(0.39290)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTRABSCOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02515(0.54891)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.04295(0.40232)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.20139(0.66727)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFAQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00063(0.84653)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.00065(0.87079)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.01742(0.63031)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eDementia \u0026amp; MCI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCDRSB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.00176(0.00009)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00227(0.00007)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01735(0.00070)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eADAS11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.00669(0.00003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00921(0.00001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.06978(0.00013)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eADAS13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.01067(\u0026lt;\u0026thinsp;0.0001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01374(\u0026lt;\u0026thinsp;0.0001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.10771(0.00001)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMMSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00387(\u0026lt;\u0026thinsp;0.0001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.00389(\u0026lt;\u0026thinsp;0.0001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.03283(0.00001)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRAVLT_immediate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00740(0.00116)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.01331(\u0026lt;\u0026thinsp;0.0001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.10915(0.00002)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRAVLT_forgetting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00034(0.52916)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00145(0.03260)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01360(0.02519)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLDELTOTAL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00441(\u0026lt;\u0026thinsp;0.0001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.00514(\u0026lt;\u0026thinsp;0.0001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.04447(0.00001)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTRABSCOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.06251(0.00242)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.07558(0.00397)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.58157(0.01349)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFAQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.00707(0.00003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00534(0.01323)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.04017(0.03768)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003eAdjusted for Age, Sex, and Education\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations:\u003c/strong\u003e CDRSB, Clinical Dementia Rating Scale Sum of Boxes; CN, cognitively normal; CSF, cerebrospinal fluid; FAQ, Functional Activities Questionnaire; MCI, mild cognitive impairment; MMSE, Mini-Mental State Exam; P-Tau, phosphorylated tau; RAVLT, Rey Auditory Verbal Learning Test; Trails B, Trail Making Test Part B.\u003c/p\u003e\n\u003ch3\u003eCorrelation Analysis\u003c/h3\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eMCI Cohort\u003c/h2\u003e\n \u003cp\u003eIn the MCI group, significant correlations were observed between CSF biomarkers and cognitive assessments. Negative correlations were noted between CSF A\u0026beta;₁₋₄₂ and the following cognitive measures: ADAS-Cog11 (r=-0.164, p\u0026thinsp;=\u0026thinsp;0.002), ADAS-Cog13 (r=-0.181, p\u0026thinsp;=\u0026thinsp;0.001), Trails B (r=-0.110, p\u0026thinsp;=\u0026thinsp;0.04), and FAQ (r=-0.131, p\u0026thinsp;=\u0026thinsp;0.01). Conversely, positive correlations were identified between CSF A\u0026beta;₁₋₄₂ and assessments of memory and global cognitive function, including MMSE (r\u0026thinsp;=\u0026thinsp;0.149, p\u0026thinsp;=\u0026thinsp;0.004) and WMS-delayed recall (r\u0026thinsp;=\u0026thinsp;0.116, p\u0026thinsp;=\u0026thinsp;0.03).\u003c/p\u003e\n \u003cp\u003eFor CSF t-tau and p-tau-181, significant positive correlations were found with several cognitive measures indicative of cognitive decline (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e): CDRSB (t-tau: r\u0026thinsp;=\u0026thinsp;0.105, p\u0026thinsp;=\u0026thinsp;0.047, ADAS-Cog11 (t-tau: r\u0026thinsp;=\u0026thinsp;0.114, p\u0026thinsp;=\u0026thinsp;0.03; p-tau-181: r\u0026thinsp;=\u0026thinsp;0.114, p\u0026thinsp;=\u0026thinsp;0.03), ADAS-Cog13 (t-tau: r\u0026thinsp;=\u0026thinsp;0.165, p\u0026thinsp;=\u0026thinsp;0.002; p-tau-181: r\u0026thinsp;=\u0026thinsp;0.162, p\u0026thinsp;=\u0026thinsp;0.002), RAVLT Forgetting (t-tau: r\u0026thinsp;=\u0026thinsp;0.173, p\u0026thinsp;=\u0026thinsp;0.001; ptau-181: r\u0026thinsp;=\u0026thinsp;0.167, p\u0026thinsp;=\u0026thinsp;0.001). Negative correlations were observed between t-tau/p-tau-181 and cognitive scores reflecting preserved function, including MMSE (t-tau: r=-0.175, p\u0026thinsp;=\u0026thinsp;0.001; p-tau181: r=-0.180, p\u0026thinsp;=\u0026thinsp;0.001), RAVLT-immediate (t-tau: r=-0.128, p\u0026thinsp;=\u0026thinsp;0.02; p-tau-181: r=-0.130, p\u0026thinsp;=\u0026thinsp;0.01), and WMS-delayed recall (t-tau: r=-0.237, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; p-tau-181: r=-0.235, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eAD Cohort\u003c/h2\u003e\n \u003cp\u003eIn the AD group, significant correlations were sparse. No statistically significant relationships were observed between CSF A\u0026beta;₁₋₄₂, t-tau, p-tau-181, and any of the cognitive assessment scores (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eCombined MCI and AD Cohort\u003c/h2\u003e\n \u003cp\u003eWhen the MCI and AD cohorts were grouped, significant correlations were observed between CSF biomarkers and cognitive measures. CSF A\u0026beta;₁₋₄₂ demonstrated significant negative correlations with measures of cognitive decline, including CDRSB (r=-0.160, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), ADASCog11 (r=-0.178, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), ADAS-Cog13 (r=-0.206, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), Trails B (r=-0.124, p\u0026thinsp;=\u0026thinsp;0.004), and FAQ (r=-0.175, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Positive correlations were noted with global and memory-specific cognitive scores such as MMSE (r\u0026thinsp;=\u0026thinsp;0.245, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), RAVLT Immediate (r\u0026thinsp;=\u0026thinsp;0.157, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and WMS-delayed recall (r\u0026thinsp;=\u0026thinsp;0.199, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\n \u003cp\u003eCSF t-tau and p-tau-181 exhibited significant correlations with all cognitive measures examined. Both biomarkers were positively correlated with measures of cognitive decline, including CDRSB (t-tau: r\u0026thinsp;=\u0026thinsp;0.191, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; p-tau: r\u0026thinsp;=\u0026thinsp;0.165, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), ADAS-Cog11 (t-tau: r\u0026thinsp;=\u0026thinsp;0.194, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; p-tau-181: r\u0026thinsp;=\u0026thinsp;0.165, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), ADAS-Cog13 (t-tau: r\u0026thinsp;=\u0026thinsp;0.221, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; p-tau181: r\u0026thinsp;=\u0026thinsp;0.194, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), RAVLT Forgetting (t-tau: r\u0026thinsp;=\u0026thinsp;0.110, p\u0026thinsp;=\u0026thinsp;0.01; p-tau-181: r\u0026thinsp;=\u0026thinsp;0.113, p\u0026thinsp;=\u0026thinsp;0.009), Trails B (t-tau: r\u0026thinsp;=\u0026thinsp;0.144, p\u0026thinsp;=\u0026thinsp;0.001; p-tau-181: r\u0026thinsp;=\u0026thinsp;0.125, p\u0026thinsp;=\u0026thinsp;0.004), and FAQ (t-tau: r\u0026thinsp;=\u0026thinsp;0.116, p\u0026thinsp;=\u0026thinsp;0.007; p-tau-181: r\u0026thinsp;=\u0026thinsp;0.098, p\u0026thinsp;=\u0026thinsp;0.02).\u003c/p\u003e\n \u003cp\u003eConversely, t-tau and p-tau-181 demonstrated negative correlations with scores indicating better cognitive function (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e), including MMSE (t-tau: r=-0.209, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; p-tau-181: r\u0026thinsp;=\u0026thinsp;0.198, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), RAVLT Immediate (t-tau: r=-0.157, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; p-tau-181: r=-0.145, p\u0026thinsp;=\u0026thinsp;0.001), and WMS-delayed recall (t-tau: r=-0.244, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; p-tau-181: r=-0.235, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003eCN Cohort\u003c/h2\u003e\n \u003cp\u003eSignificant but modest correlations were found between CSF biomarkers and cognitive measures in the CN group (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). CSF A\u0026beta;₁₋₄₂ demonstrated negative correlations with ADAS-Cog11 (r=-0.293, p\u0026thinsp;=\u0026thinsp;0.001) and ADAS-Cog13 (r=-0.304, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003eCombined CN/MCI/AD Cohort\u003c/h2\u003e\n \u003cp\u003eThe strongest relationships between biomarkers and cognitive tests were observed in this cohort, in which all 3 diagnostic groups were combined. CSF A\u0026beta;₁₋₄₂ demonstrated significant negative correlations with measures of cognitive decline, including CDRSB (r=-0.206, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), ADAS-Cog11 (r=-0.238, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), ADAS-Cog13 (r=-0.266, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), Trails B (r=-0.159, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and FAQ (r=-0.202, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Positive correlations were noted with global and memory-specific cognitive scores such as MMSE (r\u0026thinsp;=\u0026thinsp;0.256, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), RAVLT Immediate (r\u0026thinsp;=\u0026thinsp;0.217, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and WMS-delayed recall (r\u0026thinsp;=\u0026thinsp;0.253, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\n \u003cp\u003eCSF t-tau and p-tau-181 again exhibited significant correlations with all cognitive measures examined. Both biomarkers were positively correlated with measures of cognitive decline, including CDRSB (t-tau: r\u0026thinsp;=\u0026thinsp;0.286, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; p-tau: r\u0026thinsp;=\u0026thinsp;0.264, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), ADAS-Cog11 (t-tau: r\u0026thinsp;=\u0026thinsp;0.276, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; p-tau-181: r\u0026thinsp;=\u0026thinsp;0.251, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), ADAS-Cog13 (t-tau: r\u0026thinsp;=\u0026thinsp;0.308, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; ptau-181: r\u0026thinsp;=\u0026thinsp;0.285, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), RAVLT-forgetting (t-tau: r\u0026thinsp;=\u0026thinsp;0.143, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; p-tau-181: r\u0026thinsp;=\u0026thinsp;0.144, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), Trails B (t-tau: r\u0026thinsp;=\u0026thinsp;0.208, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; p-tau-181: r\u0026thinsp;=\u0026thinsp;0.192, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and FAQ (t-tau: r\u0026thinsp;=\u0026thinsp;0.202, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; p-tau-181: r\u0026thinsp;=\u0026thinsp;0.185, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\n \u003cp\u003eConversely, t-tau and p-tau-181 demonstrated negative correlations with scores indicating better cognitive function (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e), including MMSE (t-tau: r=-0.282, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; p-tau-181: r\u0026thinsp;=\u0026thinsp;0.268, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), RAVLT Immediate (t-tau: r=-0.257, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; p-tau-181: r=-0.245, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and WMS-delayed recall (t-tau: r=-0.331, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; p-tau-181: r=-0.323, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis study examined the relationships between CSF biomarkers (Aβ₁₋₄₂, t-tau, p-tau-181) and cognitive performance across CN, MCI, and AD groups, as well as in a combined MCI/AD cohort. Our findings offer a comprehensive view of biomarker-cognition relationships across the AD continuum, demonstrating that these associations are most pronounced at the MCI stage, where therapeutic intervention may hold the greatest potential impact. Among the most significant findings, Aβ₁₋₄₂ demonstrated correlations with cognitive measures in the MCI group, including negative associations with ADAS-Cog13, ADAS-Cog11, Trails B, and FAQ, and positive associations with MMSE and WMS-delayed recall. Similarly, t-tau and p-tau-181 showed significant correlations with both markers of cognitive decline (e.g., ADAS-Cog13) and preserved cognitive function (e.g., MMSE and RAVLT-immediate). These findings suggest that biomarker levels, particularly in the MCI stage, are closely linked to cognitive performance and may serve as sensitive indicators of early cognitive decline. Additionally, these results suggest that Aβ₁₋₄₂ showed similar significant associations with cognitive measures as tau and p-tau-18, contradicting previous literature that suggested tau to have stronger relationships with cognition\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. These findings partially support our initial hypothesis: while p-tau and t-tau demonstrated robust associations with cognitive decline in the MCI group, Aβ₁₄₂ also showed significant correlations, particularly with cognitive measures reflecting preserved function. This finding suggests that each biomarker may capture different, yet complementary, aspects of cognitive impairment, and that their relevance may vary by disease stage and cognitive domain affected.\u003c/p\u003e\u003cp\u003eThe combined MCI and AD analysis revealed robust correlations across all cognitive measures and biomarkers, reinforcing the importance of this stage in understanding the progression of AD. These findings further highlight the utility of Aβ₁₋₄₂ as a predictor of preserved cognitive function and t-tau and p-tau-181 as markers of neurodegeneration (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eInterestingly, in the AD cohort, significant correlations were sparse. Neither Aβ₁₋₄₂ nor t-tau or p-tau-181 had any significant correlations. These results suggest that in advanced stages of AD, other pathological processes may play a more dominant role in cognitive decline, overshadowing the influence of traditional biomarkers.\u003c/p\u003e\u003cp\u003eIn the CN group, significant correlations were limited to Aβ₁₋₄₂, which was negatively associated with ADAS-Cog13 and ADAS-Cog11. These findings indicate that even in individuals with normal cognition, subtle biomarker-cognition relationships can be detected, potentially offering opportunities for early intervention.\u003c/p\u003e\u003cp\u003eThis novel study assesses the relationship between well-known cognitive tests and CSF biomarkers of AD in the ADNI dataset. Our findings contribute to the growing knowledge base of the pathophysiology of AD progression. Previous studies have shown tau biomarkers (on PET and in CSF) or a ratio of Aβ/tau to be more closely related to cognition than Aβ alone.\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e Vemuri and colleagues also illustrated that CSF biomarkers and cognition were not correlated, supporting the use of alternate imaging markers such as MRI of brain atrophy.\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e Our results illustrate a strong association between Aβ and cognition, suggesting that amyloid accumulation may play a significant role in cognitive decline.\u003c/p\u003e\u003cp\u003eAlthough the results of this study are promising, we recognize that it has limitations. One primary limitation is the study\u0026rsquo;s cross-sectional design due to limited sample size of individuals meeting our inclusion criteria. As a result, we cannot conclude how cognition changes over time relate to CSF biomarkers. Over time, the biomarker profile of a patient may change differentially compared to their performance in various domains of cognition. Since ADNI is a large, multi-site data-collecting initiative, there may have been variations in biospecimen collection and processing protocols from 1 site to the next. With the quantity of data collected and the detailed protocol guidelines provided by ADNI, there might have been errors that could have affected our results.\u003c/p\u003e\u003cp\u003eRegarding the ADNI protocol, another potential limitation of the study is the diagnostic categorization of patients as having CN, MCI, or AD, which an on-site physician performs. Although there are specific diagnostic guidelines for physicians, some subjectivity may be inherent in determining this diagnosis. The use of ADNI data also presents a few inherent limitations in its generalizability to the national population and selection biases of subjects involved. Lastly, it would be beneficial to repeat this analysis using PET data since research has shown imaging markers to be increasingly valuable in AD diagnosis, as well as blood-based biomarkers which are less invasive and less costly.\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eAdditionally, our study did not include emerging plasma biomarkers, which are now increasingly validated for diagnostic use. Integrating fluid and imaging markers longitudinally will be essential to establish their combined predictive value. Future research should aim to continue improving our diagnostic accuracy of AD, especially early in the disease course, so adequate measures can be taken to slow progression. It would be beneficial to further research CSF biomarker changes over time and how they relate to the clinical course of AD. Additionally, exploring the role of newer cognitive assessment tools and digital phenotyping may enhance our ability to detect subtle cognitive changes in the preclinical and early MCI stages.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eIn summary, the findings highlight the nuanced relationships between CSF biomarkers Aβ₁₋₄₂, t-tau, and p-tau-181 and cognitive performance across the spectrum of cognitive impairment. We emphasize the importance of early identification and targeted interventions during the MCI stage, where biomarker-cognition correlations are strongest. Future longitudinal studies should aim to validate these findings and explore the predictive value of the biomarkers in disease progression and therapeutic response.\u003c/p\u003e"},{"header":"ABBREVIATIONS","content":"\u003cp\u003eAβ, amyloid-β; AD, Alzheimer’s Disease; ADNI, Alzheimer’s Disease Neuroimaging Initiative; CN, cognitively normal; CSF, cerebrospinal fluid; IRB, institutional review board; MCI, mild cognitive impairment; MMSE, Mini-Mental State Exam; MoCA, Montreal Cognitive Assessment; MRI, magnetic resonance imaging; PET, positron emission tomography; p-tau, phosphorylated tau; t-tau, total tau; WMS, Wechsler Memory Scale\u003c/p\u003e"},{"header":"Declarations","content":"\u003cul\u003e\n \u003cli\u003eEthics approval and consent to participate\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eData used in this study were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). The ADNI study was approved by the institutional review boards of all participating institutions, and written informed consent was obtained from all participants or their authorized representatives. Because the present analysis used only deidentified, publicly available data, additional ethical approval and consent were not required.\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eConsent for publication\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eAvailability of data and materials\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eData used in the preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). The ADNI data are publicly available to qualified investigators upon registration and compliance with data use policies. Additional data generated or analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eCompeting interests\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eBitar, Alban, Beyene and DeCourt\u0026nbsp;have no disclosures to report.\u0026nbsp;Dr. Sabbagh discloses that he consults for Alzheon, Athira, Biogen, Roche-Genentech, Synaptogenix, Novo Nordisk, Signant Health, Prothena, Eisai, GSK, Abbvie, Lilly, and Neurotherapia.\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eFunding\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe authors received no specific funding for this work. Data collection and sharing for this project were funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and the Department of Defense ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from private pharmaceutical companies and nonprofit organizations, as detailed at adni.loni.usc.edu.\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eAuthors' contributions\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eGabriel Bitar:\u003c/strong\u003e Data analysis, literature review, results interpretation, manuscript writing/editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSierra Alban:\u003c/strong\u003e Data extraction, literature review, interpretation of findings, manuscript writing/editing\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eKassu Beyene:\u003c/strong\u003e Data curation, statistical analysis, figure/table preparation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBoris Decourt: Supervision, conceptual guidance, manuscript revisions.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eShervin Harirchian: Manuscript writing and editing.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMarwan Sabbagh:\u003c/strong\u003e Critical supervision, conceptual guidance, manuscript revisions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAll authors:\u003c/strong\u003e Reviewed and approved the final manuscript and agree to be accountable for all aspects of the work.\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eAcknowledgements\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eData collection and sharing for this project were funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and the Department of Defense ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association, Alzheimer’s Drug Discovery Foundation, Araclon Biotech, BioClinica, Inc., Biogen, Bristol Myers Squibb, CereSpir, Inc., Cogstate, Eisai Inc., Elan Pharmaceuticals, Inc., Eli Lilly and Company, EuroImmun, F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc., Fujirebio, GE Healthcare, IXICO Ltd., Janssen Alzheimer Immunotherapy Research \u0026amp; Development, LLC., Johnson \u0026amp; Johnson Pharmaceutical Research \u0026amp; Development LLC., Lumosity, Lundbeck, Merck \u0026amp; Co., Inc., Meso Scale Diagnostics, LLC., NeuroRx Research, Neurotrack Technologies, Novartis Pharmaceuticals Corporation, Pfizer Inc., Piramal Imaging, Servier, Takeda Pharmaceutical Company, and Transition Therapeutics. The Canadian Institutes of Health Research provides funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eNandi A, Counts N, Broker J, et al. Cost of care for Alzheimer\u0026apos;s disease and related dementias in the United States: 2016 to 2060. NPJ Aging 2024;10:13.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eImbimbo BP, Lombard J, Pomara N. Pathophysiology of Alzheimer\u0026apos;s disease. 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An Algorithm for Preclinical Diagnosis of Alzheimer\u0026apos;s Disease. Front Neurosci 2018;12:275.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eShaw LM, Vanderstichele H, Knapik-Czajka M, et al. Cerebrospinal fluid biomarker signature in Alzheimer\u0026apos;s disease neuroimaging initiative subjects. Ann Neurol 2009;65:403-413.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eOssenkoppele R, Hansson O. Towards clinical application of tau PET tracers for diagnosing dementia due to Alzheimer\u0026apos;s disease. Alzheimers Dement 2021;17:1998-2008.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eU. S. Food and Drug Administration. FDA Approves First Drug to Image Tau Pathology in Patients Being Evaluated for Alzheimer\u0026rsquo;s Disease [online]. Available.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eLeuzy, A., Mattsson‐Carlgren, N., Palmqvist, S., Janelidze, S., Dage, J. L., \u0026amp; Hansson, O. (2022). Blood\u0026amp;#x2010;based biomarkers for Alzheimer\u0026rsquo;s disease. \u003cem\u003eEMBO Molecular Medicine\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e(1), e14408. https://doi.org/https://doi.org/10.15252/emmm.202114408\u003c/li\u003e\n \u003cli\u003ePalmqvist, S., Whitson, H. E., Allen, L. A., Suarez-Calvet, M., Galasko, D., Karikari, T. K., Okrahvi, H. R., Paczynski, M., Schindler, S. E., Teunissen, C. E., Zetterberg, H., Carrillo, M. C., Edelmayer, R. M., Mahinrad, S., McAteer, M. B., Kahale, L. A., Pahlke, S., \u0026amp; Tampi, M. P. (2025). Alzheimer\u0026rsquo;s Association Clinical Practice Guideline on the use of blood-based biomarkers in the diagnostic workup of suspected Alzheimer\u0026rsquo;s disease within specialized care settings.\u0026nbsp;\u003cem\u003eAlzheimer\u0026rsquo;s \u0026amp; Dementia\u003c/em\u003e, \u003cem\u003e21\u003c/em\u003e(7), e70535. https://doi.org/https://doi.org/10.1002/alz.70535\u003c/li\u003e\n \u003cli\u003eBrier MR, Gordon B, Friedrichsen K, et al. Tau and Abeta imaging, CSF measures, and cognition in Alzheimer\u0026apos;s disease. Sci Transl Med 2016;8:338ra366.\u003c/li\u003e\n \u003cli\u003eWolters EE, Ossenkoppele R, Verfaillie SCJ, et al. Regional [(18)F]flortaucipir PET is more closely associated with disease severity than CSF p-tau in Alzheimer\u0026apos;s disease. Eur J Nucl\u0026nbsp;Med Mol Imaging 2020;47:2866-2878.\u003c/li\u003e\n \u003cli\u003eGuo T, Korman D, La Joie R, et al. Normalization of CSF pTau measurement by Abeta(40) improves its performance as a biomarker of Alzheimer\u0026apos;s disease. Alzheimers Res Ther 2020;12:97.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eVemuri P, Wiste HJ, Weigand SD, et al. MRI and CSF biomarkers in normal, MCI, and AD subjects: diagnostic discrimination and cognitive correlations. Neurology 2009;73:287-293.\u003c/li\u003e\n \u003cli\u003eRadanovic M, Oshiro CA, Freitas TQ, Talib LL, Forlenza OV. Correlation between CSF biomarkers of Alzheimer\u0026apos;s disease and global cognition in a psychogeriatric clinic cohort. Braz J Psychiatry 2019;41:479-484.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eAlbert MS, DeKosky ST, Dickson D, et al. The diagnosis of mild cognitive impairment due to Alzheimer\u0026apos;s disease: recommendations from the National Institute on Aging-Alzheimer\u0026apos;s Association workgroups on diagnostic guidelines for Alzheimer\u0026apos;s disease. Alzheimers Dement 2011;7:270-279.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eMcKhann GM, Knopman DS, Chertkow H, et al. The diagnosis of dementia due to Alzheimer\u0026apos;s disease: recommendations from the National Institute on Aging-Alzheimer\u0026apos;s Association workgroups on diagnostic guidelines for Alzheimer\u0026apos;s disease. Alzheimers Dement 2011;7:263-269.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eBlennow K, Shaw LM, Stomrud E, et al. Predicting clinical decline and conversion to Alzheimer\u0026apos;s disease or dementia using novel Elecsys Abeta(1-42), pTau and tTau CSF immunoassays. Sci Rep 2019;9:19024.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eHanseeuw, B. J., Betensky, R. A., Jacobs, H. I. L., Schultz, A. P., Sepulcre, J., Becker, J. A., Cosio, D. M. O., Farrell, M., Quiroz, Y. T., Mormino, E. C., Buckley, R. F., Papp, K. v, Amariglio, R. A., Dewachter, I., Ivanoiu, A., Huijbers, W., Hedden, T., Marshall, G. A., Chhatwal, J. P., \u0026hellip; Johnson, K. (2019). Association of Amyloid and Tau With Cognition in Preclinical Alzheimer Disease: A Longitudinal Study. \u003cem\u003eJAMA Neurology\u003c/em\u003e, \u003cem\u003e76\u003c/em\u003e(8), 915\u0026ndash;924. https://doi.org/10.1001/jamaneurol.2019.1424\u003c/li\u003e\n \u003cli\u003eXiong, C., Jasielec, M. S., Weng, H., Fagan, A. M., Benzinger, T. L. S., Head, D., Hassenstab, J., Grant, E., Sutphen, C. L., Buckles, V., Moulder, K. L., \u0026amp; Morris, J. C. (2016). Longitudinal relationships among biomarkers for Alzheimer disease in the Adult Children Study. \u003cem\u003eNeurology\u003c/em\u003e, \u003cem\u003e86\u003c/em\u003e(16), 1499\u0026ndash;1506. https://doi.org/10.1212/WNL.0000000000002593\u003c/li\u003e\n \u003cli\u003eMalpas CB, Saling MM, Velakoulis D, Desmond P, O\u0026apos;Brien TJ, Alzheimer\u0026apos;s Disease Neuroimaging I. Tau and Amyloid-beta Cerebrospinal Fluid Biomarkers have Differential Relationships with Cognition in Mild Cognitive Impairment. J Alzheimers Dis 2015;47:965-975.\u003c/li\u003e\n \u003cli\u003eTimmers M, Tesseur I, Bogert J, et al. Relevance of the interplay between amyloid and tau for cognitive impairment in early Alzheimer\u0026apos;s disease. Neurobiol Aging 2019;79:131-141.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eBucci M, Chiotis K, Nordberg A, Alzheimer\u0026apos;s Disease Neuroimaging I. Alzheimer\u0026apos;s disease profiled by fluid and imaging markers: tau PET best predicts cognitive decline. Mol Psychiatry 2021;26:5888-5898.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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 Neuroimaging Initiative, Amyloid-β, Mild cognitive impairment, Mini-Mental State Exam, Montreal Cognitive Assessment, Phosphorylated-tau, preclinical disease, Total-tau ","lastPublishedDoi":"10.21203/rs.3.rs-8168314/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8168314/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground and Objectives:\u003c/strong\u003e We examined the relationships between cerebrospinal fluid (CSF) amyloid-β, tau, p-tau, and cognition in an Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. Studies have examined the longitudinal relationships between CSF biomarkers for Alzheimer’s disease (AD) and cognition, but there is discordance in the strength of associations between CSF amyloid-β, t-tau, or p-tau with cognition at different disease stages.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e The study included 665 patients from the combined ADNI dataset: 128 cognitively normal (CN), 175 with mild cognitive impairment (MCI), and 362 with AD dementia. All patients were amyloid-β–positive according to established CSF amyloid-β cut-off values. Cognitive assessments, diagnoses, and specimen collection/processing were conducted via published standardized protocols. We examined cross-sectional baseline data and performed correlational and regression analyses to evaluate the associations between CSF biomarkers and assessments of learning, memory, executive function, language, attention, visuospatial skills, and activities of daily living.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e In the MCI cohort, significant negative correlations were observed between CSF amyloid-β and ADAS-Cog11 (r=-0.164, p=0.02, ADAS-Cog13 (r=-0.181, p=0.01), Trails B (r=0.11, p=0.04), and FAQ (r=-0.131, p=0.01). There were positive correlations between amyloid-β and MMSE (r=0.149, p=0.004) and amyloid-β and WMS-delayed recall (r=0.116, p=0.03). Statistically significant correlations were observed between CSF t-tau and p-tau and CDRSB (t-tau: r=0.105, p=0.047), ADAS-Cog11 (t-tau: r=0.114, p=0.03; p-tau-181: r=0.114, p=0.03), ADAS-Cog13 (t-tau: r=0.165, p=0.002; p-tau-181: r=0.162, p=0.002), RAVLT-forgetting (t-tau:\u003c/p\u003e\n\u003cp\u003er=0.173, p=0.001; p-tau-181: r=0.167, p=0.001), and WMS-delayed recall (t-tau: r=-0.237, p\u0026lt;0.001 and p-tau-181: r=-0.235, p\u0026lt;0.001). In the AD cohort, no statistically significant relationships were observed between CSF amyloid-β₁₋₄₂, t-tau, p-tau-181, and any of the cognitive scores.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDiscussion:\u003c/strong\u003e The findings suggest that the relationship between CSF biomarkers and cognitive performance is strongest in MCI. The lack of significant correlations in the AD cohort may indicate other pathophysiological changes dominating cognitive dysfunction at this disease stage. Both tau and amyloid showed similar utility in reflecting cognitive impairment, in contrast to some reports in the literature. Further research is warranted to explore biomarker longitudinal impacts and their predictive values across the spectrum of cognitive impairment.\u003c/p\u003e","manuscriptTitle":"Correlations of CSF Biomarkers of Alzheimer’s Disease with Cognitive Measures in MCI and AD Dementia: A Cross-Sectional Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-01 07:56:31","doi":"10.21203/rs.3.rs-8168314/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":"0d794fbe-5579-42fd-a980-134b5fe0e245","owner":[],"postedDate":"December 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-27T08:39:00+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-01 07:56:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8168314","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8168314","identity":"rs-8168314","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}