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Large-scale proteomic analyses of incident Alzheimer’s disease reveal new pathophysiological insights and potential therapeutic targets

Molecular Psychiatry volume 30, pages 2347–2361 (2025)Cite this article

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Abstract

Pathophysiological evolutions in early-stage Alzheimer’s disease (AD) are not well understood. We used data of 2923 Olink plasma proteins from 51,296 non-demented middle-aged adults. During a follow-up of 15 years, 689 incident AD cases occurred. Cox-proportional hazard models were applied to identify AD-associated proteins in different time intervals. Through linking to protein categories, changing sequences of protein z-scores can reflect pathophysiological evolutions. Mendelian randomization using blood protein quantitative loci data provided causal evidence for potentially druggable proteins. We identified 48 AD-related proteins, with CEND1, GFAP, NEFL, and SYT1 being top hits in both near-term (HR:1.15–1.77; P:9.11 × 10−65–2.78 × 10−6) and long-term AD risk (HR:1.20-1.54; P:2.43 × 10−21–3.95 × 10−6). These four proteins increased 15 years before AD diagnosis and progressively escalated, indicating early and sustained dysfunction in synapse and neurons. Proteins related to extracellular matrix organization, apoptosis, innate immunity, coagulation, and lipid homeostasis showed early disturbances, followed by malfunctions in metabolism, adaptive immunity, and final synaptic and neuronal loss. Combining CEND1, GFAP, NEFL, and SYT1 with demographics generated desirable predictions for 10-year (AUC = 0.901) and over-10-year AD (AUC = 0.864), comparable to full model. Mendelian randomization supports potential genetic link between CEND1, SYT1, and AD as outcome. Our findings highlight the importance of exploring the pathophysiological evolutions in early stages of AD, which is essential for the development of early biomarkers and precision therapeutics.

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Fig. 1: Study overview.
Fig. 2: Proteins associated with incident AD and their functional highlights.
Fig. 3: Temporal evolutions of plasma proteins before diagnosis of AD and their trajectory clustering.
Fig. 4: Associations between AD-related proteins and brain structures.
Fig. 5: ROC curves for the prediction of future AD.

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Data availability

The main data, including the individual-level phenotypic and genetic data used in this study were accessed from the UK Biobank under the application number 19542 and are available through UKB (https://www.ukbiobank.ac.uk/).

Code availability

Analyses were performed using R software (v4.3.1). We considered the two-tailed P < 0.05 significant. Code is available from the corresponding author by request.

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Funding

National Key R&D Program of China 2023YFC3605400. Science and Technology Innovation 2030 Major Projects 2022ZD0211600. National Natural Science Foundation of China 82071201, 82271471, 82472055, 82071997, 92249305, 82402381, and 8247070942. Shanghai Municipal Science and Technology Major Project 2018SHZDZX01 and 2023SHZDZX02. Research Start-up Fund of Huashan Hospital 2022QD002. Excellence 2025 Talent Cultivation Program at Fudan University 3030277001. Program of Shanghai Academic Research Leader 23XD1420400. Shanghai Rising-Star Program 21QA1408700. Shanghai Municipal Science and Technology Major Project 2023SHZDZX02. Shanghai Municipal Health Commission Emerging Interdisciplinary Research Project 2022JC014. National Postdoctoral Program for Innovative Talents (BX20230087 to S.-D.C., BX20230089 to Y.-R.Z., and BX20240073 to Y.G.), Shanghai Pujiang Talent Program (23PJD006 to J.Y.). We want to thank all the participants and researchers from the UK Biobank.

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  1. These authors contributed equally: Yi Zhang, Yu Guo, Yu He, Jia You.

Authors and Affiliations

  1. Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China

    Yi Zhang, Yu Guo, Yu He, Jia You, YaRu Zhang, LinBo Wang, ShiDong Chen, XiaoYu He, Liu Yang, YuYuan Huang, JuJiao Kang, YiJun Ge, Qiang Dong, Wei Cheng & JinTai Yu

  2. Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China

    Jia You, LinBo Wang, JuJiao Kang, JianFeng Feng & Wei Cheng

  3. Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China

    Jia You, JianFeng Feng & Wei Cheng

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Contributions

Conceptualization: JTY, WC. Methodology: JTY, WC, YZ, LBW, JY, YG, YH, XYH, YL. Investigation: JTY, WC, JFF, QD. Visualization: YZ, YG, YH, JY. Supervision: JTY, WC, JFF, QD. Writing—original draft: YZ, YG, YH, JY. Writing—review & editing: All authors

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Correspondence to Wei Cheng or JinTai Yu.

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The study was conducted following the Declaration of Helsinki. The UK Biobank has research tissue bank approval from the North West Multi-Center Research Ethics Committee (11/NW/0382). Written informed consent was obtained from all participants.

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Zhang, Y., Guo, Y., He, Y. et al. Large-scale proteomic analyses of incident Alzheimer’s disease reveal new pathophysiological insights and potential therapeutic targets. Mol Psychiatry 30, 2347–2361 (2025). https://doi.org/10.1038/s41380-024-02840-x

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