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Multiplex cerebrospinal fluid proteomics identifies biomarkers for diagnosis and prediction of Alzheimer’s disease

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Abstract

Recent expansion of proteomic coverage opens unparalleled avenues to unveil new biomarkers of Alzheimer’s disease (AD). Among 6,361 cerebrospinal fluid (CSF) proteins analysed from the ADNI database, YWHAG performed best in diagnosing both biologically (AUC = 0.969) and clinically (AUC = 0.857) defined AD. Four- (YWHAG, SMOC1, PIGR and TMOD2) and five- (ACHE, YWHAG, PCSK1, MMP10 and IRF1) protein panels greatly improved the accuracy to 0.987 and 0.975, respectively. Their superior performance was validated in an independent external cohort and in discriminating autopsy-confirmed AD versus non-AD, rivalling even canonical CSF ATN biomarkers. Moreover, they effectively predicted the clinical progression to AD dementia and were strongly associated with AD core biomarkers and cognitive decline. Synaptic, neurogenic and infectious pathways were enriched in distinct AD stages. Mendelian randomization did not support the significant genetic link between CSF proteins and AD. Our findings revealed promising high-performance biomarkers for AD diagnosis and prediction, with implications for clinical trials targeting different pathomechanisms.

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Fig. 1: Study overview.
Fig. 2: CSF proteomic differences across distinct disease stages.
Fig. 3: Protein importance ranking and performance for distinguishing biologically/clinically defined AD from controls.
Fig. 4: Autopsy neuropathological validation and independent external validation.
Fig. 5: Predictive performance of YWHAG, SMOC1 and TMOD2 and their associations with AD endophenotypes.
Fig. 6: Enrichment, Mendelian randomization and druggability profiling analysis.

Data availability

Data used in the preparation of this Article were obtained on 12 September 2023 from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (https://ida.loni.usc.edu/pages/access/studyData.jsp?project=ADNI) and the Parkinson’s Progression Markers Initiative (PPMI) database (www.ppmi-info.org/access-data-specimens/download-data) (RRID: SCR_006431). For up-to-date information on the study, visit http://adni.loni.usc.edu/ and www.ppmi-info.org. ADNI data are publicly available to bona fide researchers upon application at https://adni.loni.usc.edu/, and PPMI data are publicly available to bona fide researchers upon application at https://www.ppmi-info.org/. Enrichment analysis data can be obtained from the STRING website (https://cn.string-db.org/). Agora Druggability data can be obtained from https://www.synapse.org/. All data supporting the findings described in this paper are available within the paper, in the Supplementary Information and from the corresponding author upon request. Source data are provided with this paper.

Code availability

All software used in this study is publicly available. The code used in this study can be accessed via GitHub at https://github.com/jasonHKU0907/AD_CSF_ADNI ref. 75.

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Acknowledgements

We thank all participants who donated their brains to the ADNI Neuropathology Core Center and PPMI database. We also thank all investigators who collected and processed specimens and performed neuropathological assessments in ADNI and PPMI. As such, the investigators within the ADNI or PPMI contributed to the design and implementation of the database and/or provided data but did not participate in the analysis or in the writing of this paper. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf. We also thank all contributors to the ADNI and PPMI databases. Data collection and sharing for this project was funded by the ADNI (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH12-2-0012). 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 Company; 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. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California. PPMI, a public–private partnership, is funded by the Michael J. Fox Foundation for Parkinson’s Research and funding partners, including 4D Pharma, Abbvie, AcureX, Allergan, Amathus Therapeutics, Aligning Science Across Parkinson’s, AskBio, Avid Radiopharmaceuticals, BIAL, BioArctic, Biogen, Biohaven, BioLegend, BlueRock Therapeutics, Bristol Myers Squibb, Calico Labs, Capsida Biotherapeutics, Celgene, Cerevel Therapeutics, Coave Therapeutics, DaCapo Brainscience, Denali, Edmond J. Safra Foundation, Eli Lilly, Gain Therapeutics, GE HealthCare, Genentech, GSK, Golub Capital, Handl Therapeutics, Insitro, Jazz Pharmaceuticals, Johnson & Johnson Innovative Medicine, Lundbeck, Merck, Meso Scale Discovery, Mission Therapeutics, Neurocrine Biosciences, Neuron23, Neuropore, Pfizer, Piramal, Prevail Therapeutics, Roche, Sanofi, Servier, Sun Pharma Advanced Research Company, Takeda, Teva, UCB, Vanqua Bio, Verily, Voyager Therapeutics, the Weston Family Foundation and Yumanity Therapeutics. We also thank all the participants and researchers from Agora (10.57718/agora-adknowledgeportal), a platform initially developed by the NIA-funded Accelerating Medicines Partnership in AD consortium that shares evidence in support of AD target discovery. J.T.-Y. was funded by grants from the Science and Technology Innovation 2030 Major Projects (2022ZD0211600), the National Natural Science Foundation of China (92249305), the Shanghai Municipal Science and Technology Major Project (2023SHZDZX02) and the Shanghai Municipal Health Commission Emerging Interdisciplinary Research Project (2022JC01). W.C. was funded by the National Natural Science Foundation of China (82071997) and the Shanghai Rising-Star Program (21QA1408700). J.F.-F. was funded by the National Key R&D Program of China (2018YFC1312904, 2019YFA0709502), the Shanghai Municipal Science and Technology Major Project (2018SHZDZX01) and the 111 Project (No. B18015). Y.G. was funded by the National Postdoctoral Program for Innovative Talents (BX20240073). J.Y. was funded by the Shanghai Pujiang Talent Program (23PJD006). S.D.-C. was funded by the National Postdoctoral Program for Innovative Talents (BX20230087). Further, we thank the ZHANGJIANG LAB, Tianqiao and Chrissy Chen Institute, and the State Key Laboratory of Neurobiology and Frontiers Center for Brain Science of the Ministry of Education, Fudan University for support. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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Author notes

  1. These authors contributed equally: Yu Guo, Shi-Dong Chen, Jia You, Shu-Yi Huang, Yi-Lin Chen.

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

    Yu Guo, Shi-Dong Chen, Jia You, Shu-Yi Huang, Yi-Lin Chen, Yi Zhang, **ao-Yu He, Yue-Ting Deng, Ya-Ru Zhang, Yu-Yuan Huang, Qiang Dong, Wei Cheng & **-Tai Yu

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

    Jia You, Lin-Bo Wang, Jian-Feng Feng & Wei Cheng

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

    Jian-Feng Feng & Wei Cheng

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Contributions

J.-T.Y. conceptualized and designed the study, interpreted the data and revised the manuscript. Y.G., S.-D.C., J.Y., S.-Y.H. and Y.-L.C. collected, analysed and interpreted the data, and drafted and revised the manuscript. All authors participated in the revision of the manuscript, had full access to all the study data and accept responsibility for submission of the paper for publication.

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Correspondence to **-Tai Yu.

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Guo, Y., Chen, SD., You, J. et al. Multiplex cerebrospinal fluid proteomics identifies biomarkers for diagnosis and prediction of Alzheimer’s disease. Nat Hum Behav (2024). https://doi.org/10.1038/s41562-024-01924-6

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