Application of Plasma Neuron-Derived Exosome-Based Multimodal Biomarkers in the Early Diagnosis of Alzheimer's Disease

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Neuron - derived exosomes (NDEs) carry intraneuronal cargo into peripheral blood, offering a window into early AD pathology. This study aimed to develop a multimodal biomarker model integrating NDE markers, plasma free biomarkers, and imaging indicators for preclinical AD diagnosis using retrospective data. Methods This retrospective study analyzed data from 400 participants: preclinical AD (A + T+N -, n = 120), AD dementia (A + T+N+, n = 80), MCI due to AD (MCI - AD, A + T+N+, n = 80), and cognitively normal controls (A - T - N -, n = 120). Plasma NDEs were isolated using L1CAM immunocapture. NDE levels of Aβ42, p - tau181, p - tau217, GAP43, SNAP25, miR − 132 , and miR − 212 were quantified. Plasma free p - tau181, p - tau217, Aβ42/40 ratio, and NfL were measured, along with Aβ - PET and hippocampal volumetry. LASSO regression selected key biomarkers; logistic regression and random forest constructed multimodal models. Results Preclinical AD showed significant NDE alterations: elevated Aβ42, p - tau181, p - tau217; decreased GAP43 and SNAP25; and downregulated miR − 132/212 . LASSO identified 7 key markers. The multimodal model achieved AUC (Area Under the Curve) 0.94 (95% CI 0.91–0.97) for preclinical AD detection, with 88.3% sensitivity and 86.7% specificity, outperforming single - modality models (p < 0.01). Validation yielded AUC 0.91 (0.87–0.95). NDE - p - tau217 correlated with Aβ - PET (r = 0.72) and predicted 3 - year cognitive decline (r = 0.58) in participants with available longitudinal data. Discussion Plasma NDEs capture intraneuronal AD pathology at the preclinical stage. A multimodal model integrating NDE markers, plasma free biomarkers, and imaging enables high - precision early AD diagnosis. Classification of Evidence: This study provides Class II evidence that a multimodal model incorporating NDE markers, plasma free biomarkers, and imaging accurately distinguishes preclinical AD from cognitively normal individuals. Neurology Alzheimer's disease plasma biomarkers neuron-derived exosomes early diagnosis multimodal biomarkers preclinical AD p-tau217 NDE Introduction Alzheimer disease (AD) is the most common neurodegenerative disorder, accounting for 60–70% of dementia cases worldwide. 1 With global population aging, the number of people with dementia is projected to increase from 55 million to 139 million by 2050, imposing substantial burden on families and healthcare systems. 2 The pathological hallmarks of AD include extracellular β - amyloid (Aβ) plaques and intracellular neurofibrillary tangles composed of hyperphosphorylated tau protein. 3 Over the past 2 decades, more than 200 drug candidates have failed in clinical trials, primarily because intervention is initiated too late—when clinical symptoms appear, irreversible neuronal loss has already occurred. 4 AD pathology begins 15–20 years before symptom onset, 5 opening a critical window for early intervention. The NIA - AA Research Framework defines AD as a biological entity based on A/T/N biomarkers, enabling identification of preclinical AD—cognitively normal individuals with evidence of AD pathology (A + T+N - ). 6,7 Current gold standards for AD diagnosis—Aβ - PET and cerebrospinal fluid (CSF) biomarker analysis—are limited by high cost, radiation exposure, and invasiveness, precluding large - scale screening. 8 Plasma biomarkers (p - tau181, p - tau217, Aβ42/40, neurofilament light (NfL) have emerged as promising non - invasive tools, achieving diagnostic accuracies of 0.85–0.90 for distinguishing AD from non - AD. 9,10 However, these markers reflect global brain changes and lack neuron - specific information about intracellular pathology. 11 Neuron - derived exosomes (NDEs) are nano - sized vesicles (30–150 nm) secreted by neurons that cross the blood - brain barrier into peripheral blood. 12 By carrying cargo reflective of intracellular neuronal state, NDEs offer a unique window into early AD pathology. 13 Previous studies demonstrated elevated NDE Aβ42 and p - tau levels in AD, with changes detectable at the preclinical stage, 14,15 decreased NDE synaptic proteins correlating with cognitive decline, 16 and downregulated microRNAs involved in tau pathology. 17 , 18 AD pathology involves multiple dimensions—Aβ deposition, tau aggregation, synaptic dysfunction, and neurodegeneration—that cannot be fully captured by any single biomarker modality. 19 Multimodal integration combining information from NDEs, plasma free markers, and imaging may provide complementary insights and improve diagnostic accuracy. 20 , 21 This study aimed to develop a multimodal biomarker model integrating NDE markers with plasma free biomarkers and imaging indicators for high - precision diagnosis of preclinical AD using retrospective data. Methods Study Design and Participants This study was a retrospective analysis of de - identified data from a previously collected clinical database. All procedures were performed in accordance with the ethical standards of the institution and with the 1964 Helsinki Declaration and its later amendments. Formal ethical approval was not required under the relevant national regulations, and written informed consent was not obtained because the data were anonymized prior to analysis. Participants were selected from the database based on the following criteria: age 50–85 years; availability of clinical assessment, blood samples, Aβ - PET, and MRI; and absence of exclusion criteria (other neurological disorders, major psychiatric disorders, MRI contraindications, or terminal illness with life expectancy < 2 years). Based on NIA - AA Research Framework criteria, 6 participants were classified into 4 groups: (1) preclinical AD (A + T+N -, Clinical Dementia Rating (CDR) = 0, n = 120); (2) AD dementia (A + T+N+, CDR ≥ 1, n = 80); (3) mild cognitive impairment due to AD (MCI - AD, A + T+N+, CDR = 0.5, n = 80); and (4) cognitively normal controls (A - T - N -, CDR = 0, n = 120). An independent validation cohort (n = 150) was selected from a separate database using identical criteria. Clinical Assessment All participants underwent standardized clinical assessment: cognitive function using Mini - Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), Preclinical Alzheimer Cognitive Composite (PACC), and CDR - Sum of Boxes (CDR - SB); functional status using Alzheimer's Disease Cooperative Study - Activities of Daily Living (ADCS - ADL); and mood assessment using Geriatric Depression Scale (GDS) and Generalized Anxiety Disorder − 7 (GAD − 7). Plasma Collection and Biomarker Measurement Fasting venous blood was collected into EDTA tubes and processed within 2 hours. Plasma was obtained by centrifugation at 2,000g for 10 minutes, followed by 10,000g for 20 minutes, then aliquoted and stored at − 80°C. Plasma free p - tau181, p - tau217, Aβ42, Aβ40, and NfL were quantified using Simoa HD - X Analyzer (Quanterix). The Aβ42/40 ratio was calculated. All measurements were performed in duplicate; inter - assay coefficients of variation were < 10%. NDE Isolation and Characterization Total exosomes were isolated by differential ultracentrifugation: plasma was thawed, centrifuged at 2,000g for 10 minutes, then 10,000g for 20 minutes; supernatant was ultracentrifuged at 100,000g for 70 minutes at 4°C; the pellet was resuspended in PBS and ultracentrifuged again. NDEs were isolated by immunomagnetic capture using anti - L1CAM - conjugated Dynabeads (Invitrogen) incubated overnight at 4°C, followed by magnetic separation and washing. NDE characterization included nanoparticle tracking analysis (NTA; NanoSight NS300) for size distribution; transmission electron microscopy (TEM; JEOL JEM − 1400) for morphology; and Western blot for exosome markers (CD81, CD9, TSG101) and neuronal marker (L1CAM), with Calnexin as negative control, confirming successful NDE isolation. NDE Marker Detection NDE pellets were lysed using RIPA buffer with protease and phosphatase inhibitors. NDE levels of Aβ42, p - tau181, p - tau217, GAP43, and SNAP25 were measured using Simoa assays with NDE protein normalization. For microRNA analysis, total RNA was extracted using miRNeasy Mini Kit (Qiagen). Reverse transcription was performed using TaqMan MicroRNA Reverse Transcription Kit. Quantitative PCR was conducted using TaqMan MicroRNA Assays for miR − 132 (assay ID: 000457), miR − 212 (assay ID: 000512), and U6 snRNA (assay ID: 001973) as reference. Relative expression was calculated using the 2 − ΔΔCt method. Imaging Aβ - PET was performed using ¹⁸F - florbetaben. Images were acquired 90 minutes post - injection on a Biograph mCT scanner (Siemens). Standardized uptake value ratios (SUVR) were calculated using cerebellar cortex as reference, with positivity defined as SUVR ≥ 1.2 (Centiloid ≥ 20). Structural MRI was performed on a 3.0T scanner (Siemens Prisma) using 3D T1 - weighted MPRAGE sequence (TR/TE/TI = 2300/2.98/900 ms, 1×1×1 mm³). Hippocampal volumes were segmented using FreeSurfer (v7.2) and normalized by intracranial volume. APOE Genotyping APOE ε2/ε3/ε4 genotype was determined using TaqMan SNP genotyping assays (rs429358 and rs7412) on a QuantStudio 12K Flex system. Statistical Analysis Continuous variables are presented as mean ± SD or median (IQR), and categorical variables as n (%). Group comparisons used ANOVA or Kruskal - Wallis test with Bonferroni correction. Correlations were assessed using Pearson or Spearman methods. Biomarker selection used LASSO (Least Absolute Shrinkage and Selection Operator) regression with 10 - fold cross - validation, selecting the optimal λ value minimizing cross - validation error. Logistic regression and random forest (500 trees) were used to construct diagnostic models. Model performance was evaluated by receiver operating characteristic (ROC) curve analysis, with area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Model comparisons used Delong test. All statistical analyses were performed using R (version 4.2.1). Two - sided p < 0.05 was considered statistically significant. Data Availability Anonymized data will be shared by request from qualified investigators. Results Baseline Characteristics The discovery cohort comprised 400 participants with balanced demographics across groups (Table 1 ). No significant differences were observed in age, sex, or education (p > 0.05). Preclinical AD, MCI - AD, and AD dementia groups showed significantly higher Aβ - PET SUVR and APOE ε4 carrier frequency compared to controls (p < 0.001), with progressively lower cognitive scores across the disease continuum. Table 1 Baseline Characteristics of Study Participants Characteristic Control (n = 120) Preclinical AD(n = 120) MCI due to AD (n = 8) ADDementia (n = 80) p -value Age, y 68.4 ± 7.2 69.1 ± 6.8 70.2 ± 7.5 71.3 ± 6.9 0.12 Female, n (%) 70 (58.3) 72 (60.0) 46 (57.5) 44 (55.0) 0.89 Education, y 14.2 ± 3.1 13.9 ± 2.9 13.5 ± 3.0 13.8 ± 3.2 0.34 MMSE 28.5 ± 1.2 28.2 ± 1.3 25.1 ± 1.8 19.3 ± 2.5 < 0.001 MoCA 26.8 ± 1.5 26.3 ± 1.6 22.4 ± 2.0 16.5 ± 2.8 < 0.001 PACC, z - score 0.12 ± 0.85 −0.08 ± 0.92 −1.25 ± 0.98 −2.45 ± 1.12 < 0.001 CDR - SB 0.1 ± 0.2 0.2 ± 0.3 2.1 ± 0.8 6.5 ± 1.9 < 0.001 Aβ - PET SUVR 1.08 ± 0.08 1.35 ± 0.12 1.42 ± 0.15 1.48 ± 0.14 < 0.001 APOE ε4 carrier, n (%) 30 (25.0) 63 (52.5) 39 (48.8) 36 (45.0) < 0.001 Values are mean ± SD unless otherwise indicated. NDE Marker Levels Across Groups NDE protein markers showed distinct patterns across the AD continuum (Table 2 ). Preclinical AD exhibited significantly elevated NDE - Aβ42 (28.6 ± 5.8 vs. 12.4 ± 3.2 pg/mL, p < 0.001), NDE - p - tau181 (18.5 ± 4.2 vs. 8.2 ± 2.1 pg/mL, p < 0.001), and NDE - p - tau217 (14.3 ± 3.6 vs. 5.6 ± 1.5 pg/mL, p < 0.001) compared to controls. Conversely, NDE - GAP43 (112.5 ± 28.4 vs. 156.2 ± 32.5 pg/mL, p < 0.01) and NDE - SNAP25 (72.4 ± 18.6 vs. 98.5 ± 21.3 pg/mL, p < 0.01) were significantly decreased. All markers showed progressive changes with disease severity. Table 2 NDE Protein Marker Levels Across Groups Marker, pg/mL Control (n = 120) Preclinical AD(n = 120) MCI due to AD (n = 80) AD Dementia (n = 80) p - value NDE - Aβ42 12.4 ± 3.2 28.6 ± 5.8 35.2 ± 7.1 38.5 ± 6.9 < 0.001 NDE - p - tau181 8.2 ± 2.1 18.5 ± 4.2 24.6 ± 5.3 28.9 ± 5.8 < 0.001 NDE - p - tau217 5.6 ± 1.5 14.3 ± 3.6 19.8 ± 4.5 23.5 ± 5.1 < 0.001 NDE - GAP43 156.2 ± 32.5 112.5 ± 28.4 98.3 ± 25.6 85.4 ± 22.1 < 0.001 NDE - SNAP25 98.5 ± 21.3 72.4 ± 18.6 62.8 ± 16.5 54.2 ± 14.8 < 0.001 Values are mean ± SD. NDE microRNA levels also showed significant alterations. Preclinical AD demonstrated downregulation of miR − 132 (0.62 ± 0.12 vs. 1.00 ± 0.15, p < 0.001) and miR − 212 (0.68 ± 0.14 vs. 1.00 ± 0.18, p < 0.001), with further reductions in symptomatic stages. Plasma Free Biomarker Levels Plasma free biomarkers showed progressive changes across groups (Table 3 ). Preclinical AD exhibited elevated p - tau181 (2.86 ± 0.78 vs. 1.85 ± 0.52 pg/mL, p < 0.01) and p - tau217 (2.05 ± 0.62 vs. 1.12 ± 0.35 pg/mL, p < 0.001), decreased Aβ42/40 ratio (0.068 ± 0.010 vs. 0.085 ± 0.012, p < 0.001), and no significant change in NfL (14.8 ± 4.1 vs. 12.5 ± 3.2 pg/mL, p = 0.08). Table 3 Plasma Free Biomarker Levels Across Groups Marker Control (n = 120) Preclinical AD(n = 120) MCI due to AD(n = 80) ADDementia (n = 80) p - value p - tau181 (pg/mL) 1.85 ± 0.52 2.86 ± 0.78* 3.42 ± 0.85* 3.95 ± 0.92* < 0.001 p - tau217 (pg/mL) 1.12 ± 0.35 2.05 ± 0.62* 2.68 ± 0.74* 3.15 ± 0.81* < 0.001 Aβ42/40 ratio 0.085 ± 0.012 0.068 ± 0.01* 0.059 ± 0.008* 0.052 ± 0.007* < 0.001 NfL (pg/mL) 12.5 ± 3.2 14.8 ± 4.1 18.2 ± 5.2* 22.5 ± 6.1* < 0.001 Data are mean ± SD. *p < 0.05 vs control. Key Biomarker Selection LASSO regression with 10 - fold cross - validation identified 7 biomarkers with non - zero coefficients. The optimal λ (λ_min = 0.052) selected: NDE - p - tau217 (coefficient 0.85), NDE - Aβ42 (0.72), plasma p - tau217 (0.58), NDE - GAP43 (− 0.45), NDE - p - tau181 (0.42), Aβ42/40 ratio (− 0.38), and hippocampal volume (− 0.35). Diagnostic Model Performance Logistic regression incorporating the 7 selected biomarkers yielded a multimodal model with excellent diagnostic performance (Table 4 ). The model distinguished preclinical AD from controls with AUC of 0.94 (95% CI 0.91–0.97), sensitivity of 88.3%, specificity of 86.7%, PPV of 87.1%, and NPV of 88.0%. Table 4 Diagnostic Performance of Single - Modality and Multimodal Models Model AUC (95% CI) Sensitivity Specificity PPV NPV NDEs alone 0.88 (0.84–0.92) 82.5% 81.7% 82.1% 82.1% Plasma alone 0.82 (0.77–0.87) 76.7% 75.0% 75.4% 76.3% Imaging alone 0.79 (0.74–0.84) 72.5% 70.8% 71.3% 72.0% Multimodal 0.94 (0.91–0.97) 88.3% 86.7% 87.1% 88.0% Delong test demonstrated that the multimodal model AUC was significantly higher than NDEs alone (p = 0.008), plasma alone (p < 0.001), and imaging alone (p < 0.001). Random forest analysis yielded consistent results (AUC = 0.93, 95% CI 0.90–0.96). Independent Validation In the validation cohort (n = 150), the multimodal model maintained robust performance with AUC of 0.91 (95% CI 0.87–0.95), sensitivity of 85.6%, and specificity of 84.3%. Correlations and Predictive Value NDE - Aβ42 correlated strongly with Aβ - PET SUVR (r = 0.72, p < 0.001). NDE - p - tau217 showed significant correlation with Aβ - PET SUVR (r = 0.65, p < 0.001) and moderate correlation with CSF p - tau181 (r = 0.58, p < 0.001) in a subset (n = 80). In preclinical AD participants with available 3 - year longitudinal data, baseline NDE - p - tau217 levels correlated positively with annual PACC score decline (r = 0.58, p < 0.001), indicating prognostic value for future cognitive deterioration. Discussion This study demonstrates that plasma NDEs capture intraneuronal AD pathology at the preclinical stage and, when integrated with plasma free biomarkers and imaging indicators, enable high - precision early diagnosis of AD using retrospective data. Several findings merit discussion. Elevated NDE - Aβ42 and NDE - p - tau in preclinical AD—cognitively normal individuals with positive Aβ - PET—support the amyloid cascade hypothesis and indicate that intraneuronal accumulation of Aβ and tau phosphorylation precede extracellular plaque formation and cognitive decline. 4 , 5 These changes were detectable 15–20 years before expected symptom onset, supporting NDE utility for ultra - early diagnosis. Decreased NDE synaptic proteins GAP43 and SNAP25 in preclinical AD suggest synaptic pathology begins at the preclinical stage, consistent with synaptic dysfunction being the strongest correlate of cognitive impairment. 22 Downregulation of miR − 132 and miR − 212 aligns with previous reports linking these microRNAs to tau pathology and synaptic plasticity. 17 , 18 , 23 The multimodal model achieved AUC of 0.94 (95% CI 0.91–0.97), significantly outperforming single - modality models (all p < 0.01, Delong test). This supports the concept that AD pathology is multidimensional, requiring complementary information from different biomarker sources. 19 NDEs provide insight into intraneuronal pathology, plasma free markers reflect global brain changes, and imaging offers structural and molecular information. LASSO identified NDE - p - tau217 as the most important marker, consistent with recent studies showing p - tau217 outperforms other biomarkers for early detection. 10 , 24 The non - invasive nature of plasma collection makes this multimodal approach suitable for large - scale screening—a critical requirement for population - based early detection. 8 The high diagnostic accuracy and robust validation suggest potential implementation in memory clinics and primary care settings to identify individuals at high risk for AD. The prognostic value of NDE - p - tau217, correlating with future cognitive decline, further supports its utility for risk stratification. In the context of emerging disease - modifying therapies, 25 such prognostic biomarkers could identify individuals most likely to benefit from early intervention, enabling precision prevention approaches. 26 Limitations include: (1) subgroup analyses (e.g., by APOE ε4 status) had limited statistical power; (2) 3 - year follow - up data were available only for a subset, precluding assessment of long - term predictive value for dementia conversion; (3) NDE isolation requires ultracentrifugation and immunocapture, limiting current clinical accessibility; (4) L1CAM is not entirely neuron - specific; future studies may employ multi - marker panels; (5) single - center study; multi - center, multi - ethnic validation is warranted. This study provides Class II evidence that a multimodal model incorporating NDE markers, plasma free biomarkers, and imaging accurately distinguishes preclinical AD from cognitively normal individuals. Declarations Ethics statement This study was reviewed and approved by the Biomedical Ethics Committee of ACH Medical University, Mongolia. Acknowledgments: The author thanks the staff of the Neuroscience Research Center for their support. Funding: This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Disclosures: Min ZHONG reports no disclosures relevant to the manuscript. Author Contributions: Min ZHONG: conceptualization, data analysis, writing – original draft, writing – review & editing. Declaration of Conflicting Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Data Availability: Anonymized data will be shared by request from qualified investigators. Study Registration: Not applicable. References Alzheimer's Association (2023) Alzheimer's disease facts and figures. 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Nat Med 26(3):379–386 van Dyck CH, Swanson CJ, Aisen P et al (2023) Lecanemab in Early Alzheimer's Disease. N Engl J Med 388(1):9–21 Kivipelto M, Mangialasche F, Ngandu T (2018) Lifestyle interventions to prevent cognitive impairment, dementia and Alzheimer disease. Nat Rev Neurol 14(11):653–666 Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9319300","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":617477207,"identity":"a0d2a3e8-fafe-482c-9fc4-01a61be3a0b8","order_by":0,"name":"Min ZHONG","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDElEQVRIiWNgGAWjYHACNoYEIGkAZhswyIGoAw9I0WIM1pJASAsDXAsDQ2IDiMSnRb6999iDBzUM9ubsvYdf3Si4kz4/7PBDoC12croN2LUYnDmXbpBwjIHZsudcmnWOwbPcjbfTDIBako3NDuDQIpFjJpHAxsBmcCPHzDjH4HDuxtkJIC0HErfh0CI/A6TlHwMPTEu64ez0D3i1MABVSiS2MUgAtRg/BmpJkJfOwW8L0C9pEol9EgYGZ86YMQO1GG6Qzik4kGCA2y+gEJP88c3G3uB4j/HnnD+H5eVnp2/+8KHCTg6XFgYGHhAhASLYwKQBWKUBLuVwLWDA/AFsbwM+1aNgFIyCUTASAQBGkGCxJp05ygAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0009-0003-8485-3229","institution":"Ach Medical University Ulaanbaatar, Mongolia","correspondingAuthor":true,"prefix":"","firstName":"Min","middleName":"","lastName":"ZHONG","suffix":""}],"badges":[],"createdAt":"2026-04-04 09:28:42","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-9319300/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9319300/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106725060,"identity":"a4732f07-ebb6-475e-b207-ba5cbc9cb924","added_by":"auto","created_at":"2026-04-12 18:31:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":531832,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9319300/v1/f4e2d99a-838a-4df0-83c0-2c455a641968.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eApplication of Plasma Neuron-Derived Exosome-Based Multimodal Biomarkers in the Early Diagnosis of Alzheimer's Disease\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAlzheimer disease (AD) is the most common neurodegenerative disorder, accounting for 60\u0026ndash;70% of dementia cases worldwide.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e With global population aging, the number of people with dementia is projected to increase from 55\u0026nbsp;million to 139\u0026nbsp;million by 2050, imposing substantial burden on families and healthcare systems.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e The pathological hallmarks of AD include extracellular β - amyloid (Aβ) plaques and intracellular neurofibrillary tangles composed of hyperphosphorylated tau protein.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eOver the past 2 decades, more than 200 drug candidates have failed in clinical trials, primarily because intervention is initiated too late\u0026mdash;when clinical symptoms appear, irreversible neuronal loss has already occurred.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e AD pathology begins 15\u0026ndash;20 years before symptom onset,\u003csup\u003e5\u003c/sup\u003e opening a critical window for early intervention. The NIA - AA Research Framework defines AD as a biological entity based on A/T/N biomarkers, enabling identification of preclinical AD\u0026mdash;cognitively normal individuals with evidence of AD pathology (A\u0026thinsp;+\u0026thinsp;T+N - ).\u003csup\u003e6,7\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eCurrent gold standards for AD diagnosis\u0026mdash;Aβ - PET and cerebrospinal fluid (CSF) biomarker analysis\u0026mdash;are limited by high cost, radiation exposure, and invasiveness, precluding large - scale screening.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e Plasma biomarkers (p - tau181, p - tau217, Aβ42/40, neurofilament light (NfL) have emerged as promising non - invasive tools, achieving diagnostic accuracies of 0.85\u0026ndash;0.90 for distinguishing AD from non - AD.\u003csup\u003e9,10\u003c/sup\u003e However, these markers reflect global brain changes and lack neuron - specific information about intracellular pathology.\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eNeuron - derived exosomes (NDEs) are nano - sized vesicles (30\u0026ndash;150 nm) secreted by neurons that cross the blood - brain barrier into peripheral blood.\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e By carrying cargo reflective of intracellular neuronal state, NDEs offer a unique window into early AD pathology.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e Previous studies demonstrated elevated NDE Aβ42 and p - tau levels in AD, with changes detectable at the preclinical stage,\u003csup\u003e14,15\u003c/sup\u003e decreased NDE synaptic proteins correlating with cognitive decline,\u003csup\u003e16\u003c/sup\u003e and downregulated microRNAs involved in tau pathology.\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eAD pathology involves multiple dimensions\u0026mdash;Aβ deposition, tau aggregation, synaptic dysfunction, and neurodegeneration\u0026mdash;that cannot be fully captured by any single biomarker modality.\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e Multimodal integration combining information from NDEs, plasma free markers, and imaging may provide complementary insights and improve diagnostic accuracy.\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e This study aimed to develop a multimodal biomarker model integrating NDE markers with plasma free biomarkers and imaging indicators for high - precision diagnosis of preclinical AD using retrospective data.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eStudy Design and Participants\u003c/p\u003e \u003cp\u003eThis study was a retrospective analysis of de - identified data from a previously collected clinical database. All procedures were performed in accordance with the ethical standards of the institution and with the 1964 Helsinki Declaration and its later amendments. Formal ethical approval was not required under the relevant national regulations, and written informed consent was not obtained because the data were anonymized prior to analysis.\u003c/p\u003e \u003cp\u003eParticipants were selected from the database based on the following criteria: age 50\u0026ndash;85 years; availability of clinical assessment, blood samples, Aβ - PET, and MRI; and absence of exclusion criteria (other neurological disorders, major psychiatric disorders, MRI contraindications, or terminal illness with life expectancy\u0026thinsp;\u0026lt;\u0026thinsp;2 years).\u003c/p\u003e \u003cp\u003eBased on NIA - AA Research Framework criteria,\u003csup\u003e6\u003c/sup\u003e participants were classified into 4 groups: (1) preclinical AD (A\u0026thinsp;+\u0026thinsp;T+N -, Clinical Dementia Rating (CDR)\u0026thinsp;=\u0026thinsp;0, n\u0026thinsp;=\u0026thinsp;120); (2) AD dementia (A\u0026thinsp;+\u0026thinsp;T+N+, CDR\u0026thinsp;\u0026ge;\u0026thinsp;1, n\u0026thinsp;=\u0026thinsp;80); (3) mild cognitive impairment due to AD (MCI - AD, A\u0026thinsp;+\u0026thinsp;T+N+, CDR\u0026thinsp;=\u0026thinsp;0.5, n\u0026thinsp;=\u0026thinsp;80); and (4) cognitively normal controls (A - T - N -, CDR\u0026thinsp;=\u0026thinsp;0, n\u0026thinsp;=\u0026thinsp;120). An independent validation cohort (n\u0026thinsp;=\u0026thinsp;150) was selected from a separate database using identical criteria.\u003c/p\u003e \u003cp\u003eClinical Assessment\u003c/p\u003e \u003cp\u003eAll participants underwent standardized clinical assessment: cognitive function using Mini - Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), Preclinical Alzheimer Cognitive Composite (PACC), and CDR - Sum of Boxes (CDR - SB); functional status using Alzheimer's Disease Cooperative Study - Activities of Daily Living (ADCS - ADL); and mood assessment using Geriatric Depression Scale (GDS) and Generalized Anxiety Disorder\u0026thinsp;\u0026minus;\u0026thinsp;7 (GAD\u0026thinsp;\u0026minus;\u0026thinsp;7).\u003c/p\u003e \u003cp\u003ePlasma Collection and Biomarker Measurement\u003c/p\u003e \u003cp\u003eFasting venous blood was collected into EDTA tubes and processed within 2 hours. Plasma was obtained by centrifugation at 2,000g for 10 minutes, followed by 10,000g for 20 minutes, then aliquoted and stored at \u0026minus;\u0026thinsp;80\u0026deg;C.\u003c/p\u003e \u003cp\u003ePlasma free p - tau181, p - tau217, Aβ42, Aβ40, and NfL were quantified using Simoa HD - X Analyzer (Quanterix). The Aβ42/40 ratio was calculated. All measurements were performed in duplicate; inter - assay coefficients of variation were \u0026lt;\u0026thinsp;10%.\u003c/p\u003e \u003cp\u003eNDE Isolation and Characterization\u003c/p\u003e \u003cp\u003eTotal exosomes were isolated by differential ultracentrifugation: plasma was thawed, centrifuged at 2,000g for 10 minutes, then 10,000g for 20 minutes; supernatant was ultracentrifuged at 100,000g for 70 minutes at 4\u0026deg;C; the pellet was resuspended in PBS and ultracentrifuged again. NDEs were isolated by immunomagnetic capture using anti - L1CAM - conjugated Dynabeads (Invitrogen) incubated overnight at 4\u0026deg;C, followed by magnetic separation and washing.\u003c/p\u003e \u003cp\u003eNDE characterization included nanoparticle tracking analysis (NTA; NanoSight NS300) for size distribution; transmission electron microscopy (TEM; JEOL JEM\u0026thinsp;\u0026minus;\u0026thinsp;1400) for morphology; and Western blot for exosome markers (CD81, CD9, TSG101) and neuronal marker (L1CAM), with Calnexin as negative control, confirming successful NDE isolation.\u003c/p\u003e \u003cp\u003eNDE Marker Detection\u003c/p\u003e \u003cp\u003eNDE pellets were lysed using RIPA buffer with protease and phosphatase inhibitors. NDE levels of Aβ42, p - tau181, p - tau217, GAP43, and SNAP25 were measured using Simoa assays with NDE protein normalization.\u003c/p\u003e \u003cp\u003eFor microRNA analysis, total RNA was extracted using miRNeasy Mini Kit (Qiagen). Reverse transcription was performed using TaqMan MicroRNA Reverse Transcription Kit. Quantitative PCR was conducted using TaqMan MicroRNA Assays for miR\u0026thinsp;\u0026minus;\u0026thinsp;132 (assay ID: 000457), miR\u0026thinsp;\u0026minus;\u0026thinsp;212 (assay ID: 000512), and U6 snRNA (assay ID: 001973) as reference. Relative expression was calculated using the 2 \u003csup\u003e\u0026minus; ΔΔCt\u003c/sup\u003e method.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eImaging\u003c/h2\u003e \u003cp\u003eAβ - PET was performed using \u0026sup1;⁸F - florbetaben. Images were acquired 90 minutes post - injection on a Biograph mCT scanner (Siemens). Standardized uptake value ratios (SUVR) were calculated using cerebellar cortex as reference, with positivity defined as SUVR\u0026thinsp;\u0026ge;\u0026thinsp;1.2 (Centiloid\u0026thinsp;\u0026ge;\u0026thinsp;20).\u003c/p\u003e \u003cp\u003eStructural MRI was performed on a 3.0T scanner (Siemens Prisma) using 3D T1 - weighted MPRAGE sequence (TR/TE/TI\u0026thinsp;=\u0026thinsp;2300/2.98/900 ms, 1\u0026times;1\u0026times;1 mm\u0026sup3;). Hippocampal volumes were segmented using FreeSurfer (v7.2) and normalized by intracranial volume.\u003c/p\u003e \u003cp\u003eAPOE Genotyping\u003c/p\u003e \u003cp\u003eAPOE ε2/ε3/ε4 genotype was determined using TaqMan SNP genotyping assays (rs429358 and rs7412) on a QuantStudio 12K Flex system.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eContinuous variables are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD or median (IQR), and categorical variables as n (%). Group comparisons used ANOVA or Kruskal - Wallis test with Bonferroni correction. Correlations were assessed using Pearson or Spearman methods.\u003c/p\u003e \u003cp\u003eBiomarker selection used LASSO (Least Absolute Shrinkage and Selection Operator) regression with 10 - fold cross - validation, selecting the optimal λ value minimizing cross - validation error. Logistic regression and random forest (500 trees) were used to construct diagnostic models. Model performance was evaluated by receiver operating characteristic (ROC) curve analysis, with area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Model comparisons used Delong test.\u003c/p\u003e \u003cp\u003eAll statistical analyses were performed using R (version 4.2.1). Two - sided p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e\u003cp\u003eData Availability\u003c/p\u003e\n\u003cp\u003eAnonymized data will be shared by request from qualified investigators.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eBaseline Characteristics\u003c/p\u003e \u003cp\u003eThe discovery cohort comprised 400 participants with balanced demographics across groups (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). No significant differences were observed in age, sex, or education (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Preclinical AD, MCI - AD, and AD dementia groups showed significantly higher Aβ - PET SUVR and APOE ε4 carrier frequency compared to controls (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with progressively lower cognitive scores across the disease continuum.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline Characteristics of Study Participants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;120)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePreclinical\u003c/p\u003e \u003cp\u003eAD(n\u0026thinsp;=\u0026thinsp;120)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMCI due to\u003c/p\u003e \u003cp\u003eAD (n\u0026thinsp;=\u0026thinsp;8)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eADDementia\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;80)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep -value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, y\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68.4\u0026thinsp;\u0026plusmn;\u0026thinsp;7.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69.1\u0026thinsp;\u0026plusmn;\u0026thinsp;6.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e70.2\u0026thinsp;\u0026plusmn;\u0026thinsp;7.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e71.3\u0026thinsp;\u0026plusmn;\u0026thinsp;6.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale,\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70 (58.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72 (60.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46 (57.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e44 (55.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation, y\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.2\u0026thinsp;\u0026plusmn;\u0026thinsp;3.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.9\u0026thinsp;\u0026plusmn;\u0026thinsp;2.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.8\u0026thinsp;\u0026plusmn;\u0026thinsp;3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMMSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19.3\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMoCA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.4\u0026thinsp;\u0026plusmn;\u0026thinsp;2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePACC, z - score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;1.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;2.45\u0026thinsp;\u0026plusmn;\u0026thinsp;1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCDR - SB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAβ - PET SUVR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.35\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.42\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.48\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAPOE ε4 carrier,\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30 (25.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63 (52.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39 (48.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36 (45.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eValues are mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD unless otherwise indicated.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eNDE Marker Levels Across Groups\u003c/p\u003e \u003cp\u003eNDE protein markers showed distinct patterns across the AD continuum (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Preclinical AD exhibited significantly elevated NDE - Aβ42 (28.6\u0026thinsp;\u0026plusmn;\u0026thinsp;5.8 vs. 12.4\u0026thinsp;\u0026plusmn;\u0026thinsp;3.2 pg/mL, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), NDE - p - tau181 (18.5\u0026thinsp;\u0026plusmn;\u0026thinsp;4.2 vs. 8.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1 pg/mL, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and NDE - p - tau217 (14.3\u0026thinsp;\u0026plusmn;\u0026thinsp;3.6 vs. 5.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5 pg/mL, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) compared to controls. Conversely, NDE - GAP43 (112.5\u0026thinsp;\u0026plusmn;\u0026thinsp;28.4 vs. 156.2\u0026thinsp;\u0026plusmn;\u0026thinsp;32.5 pg/mL, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and NDE - SNAP25 (72.4\u0026thinsp;\u0026plusmn;\u0026thinsp;18.6 vs. 98.5\u0026thinsp;\u0026plusmn;\u0026thinsp;21.3 pg/mL, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) were significantly decreased. All markers showed progressive changes with disease severity.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNDE Protein Marker Levels Across Groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarker,\u003c/p\u003e \u003cp\u003epg/mL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;120)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePreclinical\u003c/p\u003e \u003cp\u003eAD(n\u0026thinsp;=\u0026thinsp;120)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMCI due to\u003c/p\u003e \u003cp\u003eAD (n\u0026thinsp;=\u0026thinsp;80)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAD Dementia\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;80)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep - value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNDE - Aβ42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e12.4\u0026thinsp;\u0026plusmn;\u0026thinsp;3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e28.6\u0026thinsp;\u0026plusmn;\u0026thinsp;5.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e35.2\u0026thinsp;\u0026plusmn;\u0026thinsp;7.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e38.5\u0026thinsp;\u0026plusmn;\u0026thinsp;6.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNDE - p - tau181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e8.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e18.5\u0026thinsp;\u0026plusmn;\u0026thinsp;4.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e24.6\u0026thinsp;\u0026plusmn;\u0026thinsp;5.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e28.9\u0026thinsp;\u0026plusmn;\u0026thinsp;5.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNDE - p - tau217\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e5.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e14.3\u0026thinsp;\u0026plusmn;\u0026thinsp;3.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e19.8\u0026thinsp;\u0026plusmn;\u0026thinsp;4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e23.5\u0026thinsp;\u0026plusmn;\u0026thinsp;5.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNDE - GAP43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e156.2\u0026thinsp;\u0026plusmn;\u0026thinsp;32.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e112.5\u0026thinsp;\u0026plusmn;\u0026thinsp;28.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e98.3\u0026thinsp;\u0026plusmn;\u0026thinsp;25.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e85.4\u0026thinsp;\u0026plusmn;\u0026thinsp;22.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNDE - SNAP25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e98.5\u0026thinsp;\u0026plusmn;\u0026thinsp;21.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e72.4\u0026thinsp;\u0026plusmn;\u0026thinsp;18.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e62.8\u0026thinsp;\u0026plusmn;\u0026thinsp;16.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e54.2\u0026thinsp;\u0026plusmn;\u0026thinsp;14.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eValues are mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eNDE microRNA levels also showed significant alterations. Preclinical AD demonstrated downregulation of miR\u0026thinsp;\u0026minus;\u0026thinsp;132 (0.62\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12 vs. 1.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and miR\u0026thinsp;\u0026minus;\u0026thinsp;212 (0.68\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14 vs. 1.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with further reductions in symptomatic stages.\u003c/p\u003e \u003cp\u003ePlasma Free Biomarker Levels\u003c/p\u003e \u003cp\u003ePlasma free biomarkers showed progressive changes across groups (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Preclinical AD exhibited elevated p - tau181 (2.86\u0026thinsp;\u0026plusmn;\u0026thinsp;0.78 vs. 1.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.52 pg/mL, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and p - tau217 (2.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.62 vs. 1.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.35 pg/mL, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), decreased Aβ42/40 ratio (0.068\u0026thinsp;\u0026plusmn;\u0026thinsp;0.010 vs. 0.085\u0026thinsp;\u0026plusmn;\u0026thinsp;0.012, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and no significant change in NfL (14.8\u0026thinsp;\u0026plusmn;\u0026thinsp;4.1 vs. 12.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3.2 pg/mL, p\u0026thinsp;=\u0026thinsp;0.08).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePlasma Free Biomarker Levels Across Groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarker\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;120)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePreclinical AD(n\u0026thinsp;=\u0026thinsp;120)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMCI due to AD(n\u0026thinsp;=\u0026thinsp;80)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eADDementia\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;80)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep - value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep - tau181\u003c/p\u003e \u003cp\u003e(pg/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e2.86\u0026thinsp;\u0026plusmn;\u0026thinsp;0.78*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e3.42\u0026thinsp;\u0026plusmn;\u0026thinsp;0.85*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e3.95\u0026thinsp;\u0026plusmn;\u0026thinsp;0.92*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep - tau217\u003c/p\u003e \u003cp\u003e(pg/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e2.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.62*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e2.68\u0026thinsp;\u0026plusmn;\u0026thinsp;0.74*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e3.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.81*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAβ42/40\u003c/p\u003e \u003cp\u003eratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.085\u0026thinsp;\u0026plusmn;\u0026thinsp;0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.068\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e0.059\u0026thinsp;\u0026plusmn;\u0026thinsp;0.008*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e0.052\u0026thinsp;\u0026plusmn;\u0026thinsp;0.007*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNfL (pg/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e12.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e14.8\u0026thinsp;\u0026plusmn;\u0026thinsp;4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e18.2\u0026thinsp;\u0026plusmn;\u0026thinsp;5.2*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e22.5\u0026thinsp;\u0026plusmn;\u0026thinsp;6.1*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eData are mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD. *p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 vs control.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eKey Biomarker Selection\u003c/p\u003e \u003cp\u003eLASSO regression with 10 - fold cross - validation identified 7 biomarkers with non - zero coefficients. The optimal λ (λ_min\u0026thinsp;=\u0026thinsp;0.052) selected: NDE - p - tau217 (coefficient 0.85), NDE - Aβ42 (0.72), plasma p - tau217 (0.58), NDE - GAP43 (\u0026minus;\u0026thinsp;0.45), NDE - p - tau181 (0.42), Aβ42/40 ratio (\u0026minus;\u0026thinsp;0.38), and hippocampal volume (\u0026minus;\u0026thinsp;0.35).\u003c/p\u003e \u003cp\u003eDiagnostic Model Performance\u003c/p\u003e \u003cp\u003eLogistic regression incorporating the 7 selected biomarkers yielded a multimodal model with excellent diagnostic performance (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The model distinguished preclinical AD from controls with AUC of 0.94 (95% CI 0.91\u0026ndash;0.97), sensitivity of 88.3%, specificity of 86.7%, PPV of 87.1%, and NPV of 88.0%.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDiagnostic Performance of Single - Modality and Multimodal Models\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAUC (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSensitivity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSpecificity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePPV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNPV\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNDEs alone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.88 (0.84\u0026ndash;0.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e82.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e81.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e82.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e82.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlasma alone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.82 (0.77\u0026ndash;0.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e76.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e75.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e75.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e76.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImaging alone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.79 (0.74\u0026ndash;0.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e72.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e70.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e71.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e72.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMultimodal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.94 (0.91\u0026ndash;0.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e88.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e86.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e87.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e88.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eDelong test demonstrated that the multimodal model AUC was significantly higher than NDEs alone (p\u0026thinsp;=\u0026thinsp;0.008), plasma alone (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and imaging alone (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Random forest analysis yielded consistent results (AUC\u0026thinsp;=\u0026thinsp;0.93, 95% CI 0.90\u0026ndash;0.96).\u003c/p\u003e \u003cp\u003eIndependent Validation\u003c/p\u003e \u003cp\u003eIn the validation cohort (n\u0026thinsp;=\u0026thinsp;150), the multimodal model maintained robust performance with AUC of 0.91 (95% CI 0.87\u0026ndash;0.95), sensitivity of 85.6%, and specificity of 84.3%.\u003c/p\u003e \u003cp\u003eCorrelations and Predictive Value\u003c/p\u003e \u003cp\u003eNDE - Aβ42 correlated strongly with Aβ - PET SUVR (r\u0026thinsp;=\u0026thinsp;0.72, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). NDE - p - tau217 showed significant correlation with Aβ - PET SUVR (r\u0026thinsp;=\u0026thinsp;0.65, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and moderate correlation with CSF p - tau181 (r\u0026thinsp;=\u0026thinsp;0.58, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) in a subset (n\u0026thinsp;=\u0026thinsp;80). In preclinical AD participants with available 3 - year longitudinal data, baseline NDE - p - tau217 levels correlated positively with annual PACC score decline (r\u0026thinsp;=\u0026thinsp;0.58, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating prognostic value for future cognitive deterioration.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study demonstrates that plasma NDEs capture intraneuronal AD pathology at the preclinical stage and, when integrated with plasma free biomarkers and imaging indicators, enable high - precision early diagnosis of AD using retrospective data. Several findings merit discussion.\u003c/p\u003e \u003cp\u003eElevated NDE - Aβ42 and NDE - p - tau in preclinical AD\u0026mdash;cognitively normal individuals with positive Aβ - PET\u0026mdash;support the amyloid cascade hypothesis and indicate that intraneuronal accumulation of Aβ and tau phosphorylation precede extracellular plaque formation and cognitive decline.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e These changes were detectable 15\u0026ndash;20 years before expected symptom onset, supporting NDE utility for ultra - early diagnosis. Decreased NDE synaptic proteins GAP43 and SNAP25 in preclinical AD suggest synaptic pathology begins at the preclinical stage, consistent with synaptic dysfunction being the strongest correlate of cognitive impairment.\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e Downregulation of miR\u0026thinsp;\u0026minus;\u0026thinsp;132 and miR\u0026thinsp;\u0026minus;\u0026thinsp;212 aligns with previous reports linking these microRNAs to tau pathology and synaptic plasticity.\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe multimodal model achieved AUC of 0.94 (95% CI 0.91\u0026ndash;0.97), significantly outperforming single - modality models (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, Delong test). This supports the concept that AD pathology is multidimensional, requiring complementary information from different biomarker sources.\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e NDEs provide insight into intraneuronal pathology, plasma free markers reflect global brain changes, and imaging offers structural and molecular information. LASSO identified NDE - p - tau217 as the most important marker, consistent with recent studies showing p - tau217 outperforms other biomarkers for early detection.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe non - invasive nature of plasma collection makes this multimodal approach suitable for large - scale screening\u0026mdash;a critical requirement for population - based early detection.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e The high diagnostic accuracy and robust validation suggest potential implementation in memory clinics and primary care settings to identify individuals at high risk for AD. The prognostic value of NDE - p - tau217, correlating with future cognitive decline, further supports its utility for risk stratification. In the context of emerging disease - modifying therapies,\u003csup\u003e25\u003c/sup\u003e such prognostic biomarkers could identify individuals most likely to benefit from early intervention, enabling precision prevention approaches.\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eLimitations include: (1) subgroup analyses (e.g., by APOE ε4 status) had limited statistical power; (2) 3 - year follow - up data were available only for a subset, precluding assessment of long - term predictive value for dementia conversion; (3) NDE isolation requires ultracentrifugation and immunocapture, limiting current clinical accessibility; (4) L1CAM is not entirely neuron - specific; future studies may employ multi - marker panels; (5) single - center study; multi - center, multi - ethnic validation is warranted.\u003c/p\u003e \u003cp\u003eThis study provides Class II evidence that a multimodal model incorporating NDE markers, plasma free biomarkers, and imaging accurately distinguishes preclinical AD from cognitively normal individuals.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics statement This study was reviewed and approved by the Biomedical Ethics Committee of ACH Medical University, Mongolia.\u003c/p\u003e\u003cp\u003eAcknowledgments:\u003c/p\u003e\n\u003cp\u003eThe author thanks the staff of the Neuroscience Research Center for their support.\u003c/p\u003e\n\u003cp\u003eFunding:\u003c/p\u003e\n\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003eDisclosures:\u003c/p\u003e\n\u003cp\u003eMin ZHONG reports no disclosures relevant to the manuscript.\u003c/p\u003e\n\u003cp\u003eAuthor Contributions:\u003c/p\u003e\n\u003cp\u003eMin ZHONG: conceptualization, data analysis, writing\u0026nbsp;–\u0026nbsp;original draft, writing\u0026nbsp;–\u0026nbsp;review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003eDeclaration of Conflicting Interests:\u003c/p\u003e\n\u003cp\u003eThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.\u003c/p\u003e\n\u003cp\u003eData Availability:\u003c/p\u003e\n\u003cp\u003eAnonymized data will be shared by request from qualified investigators.\u003c/p\u003e\n\u003cp\u003eStudy Registration:\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAlzheimer's Association (2023) Alzheimer's disease facts and figures. \u003cem\u003eAlzheimers Dement\u003c/em\u003e. 2023;19(4):1598\u0026ndash;1695\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLivingston G, Huntley J, Sommerlad A et al (2020) Dementia prevention, intervention, and care: 2020 report of the Lancet Commission. Lancet 396(10248):413\u0026ndash;446\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLong JM, Holtzman DM (2019) Alzheimer Disease: An Update on Pathobiology and Treatment Strategies. Cell 179(2):312\u0026ndash;339\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHardy J, Selkoe DJ (2002) The amyloid hypothesis of Alzheimer's disease: progress and problems on the road to therapeutics. Science 297(5580):353\u0026ndash;356\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBateman RJ, Xiong C, Benzinger TL et al (2012) Clinical and biomarker changes in dominantly inherited Alzheimer's disease. N Engl J Med 367(9):795\u0026ndash;804\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJack CR Jr, Bennett DA, Blennow K et al (2018) NIA - AA Research Framework: Toward a biological definition of Alzheimer's disease. Alzheimers Dement 14(4):535\u0026ndash;562\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSperling RA, Aisen PS, Beckett LA et al (2011) Toward defining the preclinical stages of Alzheimer's disease. Alzheimers Dement 7(3):280\u0026ndash;292\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHansson O (2021) Biomarkers for neurodegenerative diseases. Nat Med 27(6):954\u0026ndash;963\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTeunissen CE, Verberk IMW, Thijssen EH et al (2022) Blood - based biomarkers for Alzheimer's disease: towards clinical implementation. Lancet Neurol 21(1):66\u0026ndash;77\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePalmqvist S, Janelidze S, Quiroz YT et al (2020) Discriminative Accuracy of Plasma Phospho - tau217 for Alzheimer Disease vs. Other Neurodegenerative Disorders. JAMA 324(8):772\u0026ndash;781\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZetterberg H, Bendlin BB (2021) Biomarkers for Alzheimer's disease preparing for a new era of disease - modifying therapies. Mol Psychiatry 26(1):296\u0026ndash;308\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKalluri R, LeBleu VS (2020) The biology, function, and biomedical applications of exosomes. Science 367(6478):eaau6977\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMustapic M, Eitan E, Werner JK Jr et al (2017) Plasma Extracellular Vesicles Enriched for Neuronal Origin: A Potential Window into Brain Pathologic Processes. Front Neurosci 11:278\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFiandaca MS, Kapogiannis D, Mapstone M et al (2015) Identification of preclinical Alzheimer's disease by a profile of pathogenic proteins in neurally derived blood exosomes: A case - control study. Alzheimers Dement 11(6):600\u0026ndash;607e1\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGoetzl EJ, Mustapic M, Kapogiannis D et al (2016) Cargo proteins of plasma astrocyte - derived exosomes in Alzheimer's disease. FASEB J 30(11):3853\u0026ndash;3859\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGoetzl EJ, Abner EL, Jicha GA et al (2018) Declining levels of functionally specialized synaptic proteins in plasma neuronal exosomes with progression of Alzheimer's disease. FASEB J 32(2):888\u0026ndash;893\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu W, Zhang L, Xuan Z et al (2020) Altered microRNA expression profile in plasma exosomes from patients with Alzheimer's disease. 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Nat Med 27(6):1034\u0026ndash;1042\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHampel H, O'Bryant SE, Molinuevo JL et al (2018) Blood - based biomarkers for Alzheimer disease: mapping the road to the clinic. Nat Rev Neurol 14(11):639\u0026ndash;652\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKnopman DS, Amieva H, Petersen RC et al (2021) Alzheimer disease. Nat Rev Dis Primers 7(1):33\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSalta E, De Strooper B (2017) Non - coding RNAs with essential roles in neurodegenerative disorders. Lancet Neurol 16(8):652\u0026ndash;664\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJanelidze S, Mattsson N, Palmqvist S et al (2020) Plasma P - tau181 in Alzheimer's disease: relationship to other biomarkers, differential diagnosis, neuropathology and longitudinal progression to Alzheimer's dementia. Nat Med 26(3):379\u0026ndash;386\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evan Dyck CH, Swanson CJ, Aisen P et al (2023) Lecanemab in Early Alzheimer's Disease. N Engl J Med 388(1):9\u0026ndash;21\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKivipelto M, Mangialasche F, Ngandu T (2018) Lifestyle interventions to prevent cognitive impairment, dementia and Alzheimer disease. Nat Rev Neurol 14(11):653\u0026ndash;666\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Ach Medical University Ulaanbaatar, Mongolia","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, plasma biomarkers, neuron-derived exosomes, early diagnosis, multimodal biomarkers, preclinical AD, p-tau217, NDE","lastPublishedDoi":"10.21203/rs.3.rs-9319300/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9319300/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjectives\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAlzheimer disease (AD) pathology begins 15–20 years before clinical symptoms, necessitating non - invasive biomarkers for early diagnosis. Neuron - derived exosomes (NDEs) carry intraneuronal cargo into peripheral blood, offering a window into early AD pathology. This study aimed to develop a multimodal biomarker model integrating NDE markers, plasma free biomarkers, and imaging indicators for preclinical AD diagnosis using retrospective data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis retrospective study analyzed data from 400 participants: preclinical AD (A + T+N -, n = 120), AD dementia (A + T+N+, n = 80), MCI due to AD (MCI - AD, A + T+N+, n = 80), and cognitively normal controls (A - T - N -, n = 120). Plasma NDEs were isolated using L1CAM immunocapture. NDE levels of Aβ42, p - tau181, p - tau217, GAP43, SNAP25, \u003cem\u003emiR − 132\u003c/em\u003e, and \u003cem\u003emiR − 212\u003c/em\u003e were quantified. Plasma free p - tau181, p - tau217, Aβ42/40 ratio, and NfL were measured, along with Aβ - PET and hippocampal volumetry. LASSO regression selected key biomarkers; logistic regression and random forest constructed multimodal models.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePreclinical AD showed significant NDE alterations: elevated Aβ42, p - tau181, p - tau217; decreased GAP43 and SNAP25; and downregulated \u003cem\u003emiR − 132/212\u003c/em\u003e. LASSO identified 7 key markers. The multimodal model achieved AUC (Area Under the Curve) 0.94 (95% CI 0.91–0.97) for preclinical AD detection, with 88.3% sensitivity and 86.7% specificity, outperforming single - modality models (p \u0026lt; 0.01). Validation yielded AUC 0.91 (0.87–0.95). NDE - p - tau217 correlated with Aβ - PET (r = 0.72) and predicted 3 - year cognitive decline (r = 0.58) in participants with available longitudinal data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDiscussion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePlasma NDEs capture intraneuronal AD pathology at the preclinical stage. A multimodal model integrating NDE markers, plasma free biomarkers, and imaging enables high - precision early AD diagnosis.\u003c/p\u003e\n\u003cp\u003eClassification of Evidence: This study provides Class II evidence that a multimodal model incorporating NDE markers, plasma free biomarkers, and imaging accurately distinguishes preclinical AD from cognitively normal individuals.\u003c/p\u003e","manuscriptTitle":"Application of Plasma Neuron-Derived Exosome-Based Multimodal Biomarkers in the Early Diagnosis of Alzheimer's Disease","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-09 18:01:36","doi":"10.21203/rs.3.rs-9319300/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":"7d6880af-36f4-438f-8e54-1929c9029036","owner":[],"postedDate":"April 9th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":65717312,"name":"Neurology"}],"tags":[],"updatedAt":"2026-04-09T18:01:36+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-09 18:01:36","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9319300","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9319300","identity":"rs-9319300","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}