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Large-scale plasma proteomic profiling unveils diagnostic biomarkers and pathways for Alzheimer’s disease

Nature Aging volume 5, pages 1114–1131 (2025) Cite this article

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Abstract

Proteomic studies have been instrumental in identifying brain, cerebrospinal fluid and plasma proteins associated with Alzheimer’s disease (AD). Here, we comprehensively examined 6,905 aptamers corresponding to 6,106 unique proteins in plasma in more than 3,300 well-characterized individuals to identify new proteins, pathways and predictive models for AD. We identified 416 proteins (294 new) associated with clinical AD status and validated the findings in two external datasets representing more than 7,000 samples. AD-related proteins reflected blood–brain barrier disruption and other processes implicated in AD, such as lipid dysregulation or immune responses. A machine learning model was used to identify a set of seven proteins that were highly predictive of both clinical AD (area under the curve (AUC) of >0.72) and biomarker-defined AD status (AUC of >0.88), which were replicated in multiple external cohorts and orthogonal platforms. These findings underscore the potential of using plasma proteins as biomarkers for the early detection and monitoring of AD and for guiding treatment decisions.

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Fig. 1: Study overview.
The alternative text for this image may have been generated using AI.
Fig. 2: Differential abundance analysis.
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Fig. 3: Replication in external datasets.
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Fig. 4: Pathway and network analyses of AD-associated proteins.
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Fig. 5: Predictive performance of the model with seven plasma proteins.
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Data availability

Plasma proteomic data for the Knight ADRC participants are available from the Knight ADRC at https://live-knightadrc-washu.pantheonsite.io/professionals-clinicians/request-center-resources/. Requests for clinical or proteomic data from individual investigators will be reviewed to ensure compliance with patient confidentiality. For details on accessing available data and study protocols, see https://knightadrc.wustl.edu.

Stanford ADRC data can be requested through the Stanford ADRC data release committee at https://web.stanford.edu/group/adrc/cgi-bin/web-proj/datareq.php.

ROSMAP resources can be requested through https://www.radc.rush.edu and https://www.synapse.org.

GNPC data will be made publicly available after an embargo period at https://www.neuroproteome.org.

ACE cohort data are available upon reasonable request. Additionally, the largest preprocessed SomaScan proteomic dataset from the ACE cohort has been uploaded and is accessible through the Alzheimer’s Disease Data Initiative (ADDI) community. Source data are provided with this paper.

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Acknowledgements

We extend our gratitude to all the participants, their families, as well as the cohorts, institutions and their dedicated staff. This work was supported by grants from the National Institutes of Health (NIH) (R01AG044546 (C.C.), P01AG003991 (C.C., J.C.M.), RF1AG053303 (C.C.), RF1AG058501 (C.C.), U01AG058922 (C.C.), RF1AG074007 (Y.J.S.), P30AG10161 (D.A.B.), P30AG72975 (D.A.B.), R01AG15819 (D.A.B.), R01AG17917 (D.A.B.), U01AG46152 (D.A.B.), U01AG61356 (D.A.B.)), the Chan Zuckerberg Initiative, the Michael J. Fox Foundation (C.C.), the Alzheimer’s Association Zenith Fellows Award (ZEN-22-848604, awarded to C.C.) and an anonymous foundation.

The recruitment and clinical characterization of research participants at Washington University were supported by NIH grants P30AG066444 (J.C.M.), P01AG03991 (J.C.M.) and P01AG026276 (J.C.M.).

The recruitment and clinical characterization of research participants of the ALFA cohort were supported by ‘La Caixa’ Foundation (ID 100010434), under agreement LCF/PR/GN17/50300004, the Health Department of the Catalan Government (Health Research and Innovation Strategic Plan (PERIS) 2016–2020 grant no. SLT002/16/00201) and the Alzheimer’s Association and an international anonymous charity foundation through the TriBEKa Imaging Platform project (TriBEKa-17-519007). Additional support has been received from the Universities and Research Secretariat, Ministry of Business and Knowledge of the Catalan Government under grant nos. 2021 SGR 00913 and 2021 SGR 01137.

F.A. receives funding from the JDC2022-049347-I grant, funded by the MCIU/AEI/10.13039/501100011033 and the European Union NextGenerationEU/PRTR.

N.V.-T. was supported by the Spanish Ministry of Science and Innovation-State Research Agency IJC2020-043216-IMCINAEI101303950110033 and the European Union NextGenerationEU/PRTR and currently receives funding from the Spanish Research Agency MICIUAEI101303950110033 grant RYC2022-038136-I cofunded by the European Union FSE+ and grant PID2022-143106OA-I00 cofunded by the European Union (Fondo Europeo de Desarrollo Regional (FEDER)). Additionally, N.V.-T. is supported, in part, by the William H. Gates Sr. Fellowship from the Alzheimer’s Disease Data Initiative (ADDI). All CRG authors acknowledge the support of the Spanish Ministry of Science Innovation and Universities to the EMBL partnership with the Centro de Excelencia Severo Ochoa and the CERCA Programme/Generalitat de Catalunya.

M.S.-C. receives funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant agreement no. 948677); ERA PerMed (ERAPERMED2021-184); Project ‘PI19/00155’ and ‘PI22/00456’, funded by Instituto de Salud Carlos III (ISCIII) and cofunded by the European Union; and a fellowship from ‘La Caixa’ Foundation (ID 100010434) and the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement no. 847648 (LCF/BQ/PR21/11840004).

R.P., P.G.-G., M.M., M.V.F., M.B., A.C. and A.R. acknowledge the support of the Agency for Innovation and Entrepreneurship (VLAIO) grant no. PR067/21 for the HARPONE project; the support of the Spanish Ministry of Science and Innovation, Proyectos de Generación de Conocimiento grant PID2021-122473OA-I00, ISCIII, Acción Estratégica en Salud integrated in the Spanish National R + D + I Plan and financed by ISCIII Subdirección General de Evaluación and FEDER (‘Una manera de hacer Europa’) grants PI19/00335 and PI22/01403; the support of CIBERNED (ISCIII) under grant CB18/05/00010; the support from PREADAPT project, Joint Program for Neurodegenerative Diseases (JPND) grant no. AC19/00097; and the support of Fundación bancaria ‘La Caixa’, Fundación ADEY, Fundación Echevarne and Grífols SA (GR@ACE project). A.C. received support from the ISCIII under the grant Sara Borrell (CD22/00125). P.G.G. is supported by a CIBERNED employment plan (CNV-304-PRF-866).

This work was supported by access to equipment made possible by the Hope Center for Neurological Disorders, the Neurogenomics and Informatics Center (NGI; https://neurogenomics.wustl.edu/) and the Departments of Neurology and Psychiatry at Washington University School of Medicine.

ROSMAP resources can be requested at https://www.radc.rush.edu and https://www.synapse.org.

Data provided by the ACE cohort are publicly available from the responsible authors upon reasonable request. Additionally, the largest preprocessed SomaScan proteomic dataset from the ACE cohort has been uploaded and is publicly accessible through the ADDI community.

Author information

Author notes

  1. These authors contributed equally: Gyujin Heo, Ying Xu.

Authors and Affiliations

  1. Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA

    Gyujin Heo, Ying Xu, Muhammad Ali, Soomin Song, Jigyasha Timsina, Menghan Liu, Daniel Western, Katherine Gong, Yike Chen, Pat Kohlfeld, Allison Flynn, Alvin G. Thomas, Joseph Lowery, Laura Ibanez, Yun Ju Sung & Carlos Cruchaga

  2. NeuroGenomics and Informatics Center, Washington University, St. Louis, MO, USA

    Gyujin Heo, Ying Xu, Muhammad Ali, Soomin Song, Jigyasha Timsina, Menghan Liu, Daniel Western, Katherine Gong, Yike Chen, Pat Kohlfeld, Allison Flynn, Alvin G. Thomas, Joseph Lowery, Laura Ibanez, Yun Ju Sung & Carlos Cruchaga

  3. Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Erming Wang & Bin Zhang

  4. Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Erming Wang & Bin Zhang

  5. Graduate Program in Stem Cell and Regenerative Medicine, Stanford University, Stanford, CA, USA

    Hamilton Se-Hwee Oh

  6. The Phil and Penny Knight Initiative for Brain Resilience, Stanford University, Stanford, CA, USA

    Hamilton Se-Hwee Oh & Patricia Moran-Losada

  7. Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA

    Hamilton Se-Hwee Oh, Patricia Moran-Losada & Tony Wyss-Coray

  8. Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA

    Patricia Moran-Losada

  9. Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain

    Federica Anastasi, Armand González Escalante, Natalia Vilor-Tejedor & Marc Suárez-Calvet

  10. Hospital del Mar Research Institute, Barcelona, Spain

    Federica Anastasi, Armand González Escalante, Natalia Vilor-Tejedor & Marc Suárez-Calvet

  11. Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Barcelona, Spain

    Federica Anastasi & Natalia Vilor-Tejedor

  12. Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Barcelona, Spain

    Armand González Escalante

  13. Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya, Barcelona, Spain

    Raquel Puerta, Pablo García-González, Marta Marquié, Maria Victoria Fernández, Mercè Boada, Amanda Cano & Agustín Ruiz

  14. PhD Program in Biotechnology, Faculty of Pharmacy and Food Sciences, University of Barcelona, Barcelona, Spain

    Raquel Puerta

  15. Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA

    John C. Morris, David M. Holtzman, Suzanne E. Schindler, Laura Ibanez & Carlos Cruchaga

  16. Hope Center for Neurologic Diseases, Washington University, St. Louis, MO, USA

    David M. Holtzman & Carlos Cruchaga

  17. Knight Alzheimer’s Disease Research Center, Washington University, St. Louis, MO, USA

    David M. Holtzman & Carlos Cruchaga

  18. Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA

    Joel S. Perlmutter & Tammie Benzinger

  19. Department of Genetics, Radboud UMC, Nijmegen, Netherlands

    Natalia Vilor-Tejedor

  20. Servei de Neurologia, Hospital del Mar, Barcelona, Spain

    Marc Suárez-Calvet

  21. Biomedical Research Networking Centre in Neurodegenerative Diseases (CIBERNED), National Institute of Health Carlos III, Madrid, Spain

    Pablo García-González, Marta Marquié, Maria Victoria Fernández, Mercè Boada, Amanda Cano & Agustín Ruiz

  22. Glenn Biggs Institute for Alzheimer’s & Neurodegenerative Diseases, University of Texas Health Science Center, San Antonio, TX, USA

    Agustín Ruiz

  23. Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Bin Zhang

  24. Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Bin Zhang

  25. Icahn Genomics Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Bin Zhang

  26. Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL, USA

    David A. Bennett

  27. Division of Biostatistics, Washington University School of Medicine, St. Louis, MO, USA

    Yun Ju Sung

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  1. Gyujin Heo
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  2. Ying Xu
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  3. Erming Wang
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  4. Muhammad Ali
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  8. Armand González Escalante
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  10. Soomin Song
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  11. Jigyasha Timsina
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Contributions

G.H., Y.J.S. and C.C. conceptualized the study. G.H. led and performed the analyses. J.T. processed and performed quality control on the proteomic data. M.L., D.W., P.K., A.F., J.L., J.S.P., S.E.S., T.B. and L.I. contributed to sample processing, data processing and curation. S.S., J.T., Y.X. and K.G. assisted with figure preparation. Y.X., Y.C., K.G. and M.A. assisted with performing analyses. E.W. and B.Z. conducted the MEGENA analysis. A.G.T. conducted the IPA analysis. T.W.-C., H.S.-H.O. and P.M.L. generated the Stanford ADRC data. D.A.B. generated and provided the ROSMAP data. H.S.-H.O. analyzed the ROSMAP data. F.A., A.G.E., N.V.-T. and M.S.-C. provided and analyzed the ALFA data. R.P., P.G.-G., M.M., M.V.F., M.B., A.C. and A.R. provided and analyzed the ACE data. J.C.M. and D.M.H. acquired funding and managed the project. G.H. and C.C. led manuscript writing and figure generation. C.C. supervised the study. All authors critically revised, read and approved the final version of the manuscript.

Corresponding author

Correspondence to Carlos Cruchaga.

Ethics declarations

Competing interests

C.C. has received research support from GSK and Eisai. C.C. is a member of the scientific advisory board of Circular Genomics and owns stocks. C.C. is a member of the scientific advisory board of ADmit. There is an invention disclosure for the prediction models, including protein IDs, alternative proteins and weights, cutoff and algorithms. C.C. has served on the scientific advisory boards of GSK and Novo Nordisk. D.M.H. is a cofounder with equity in C2N Diagnostics. D.M.H. is on the scientific advisory boards of Genentech, Denali, C2N Diagnostics and Cajal Neurosciences. D.M.H. consults for Asteroid, Acta Pharmaceuticals, Alnylam, Pfizer and Switch. T.W.-C. and H.S.-H.O. are cofounders and scientific advisors of Teal Omics and have received equity stakes. T.W.-C. is a cofounder and scientific advisor of Alkahest and Qinotto and has received equity stakes in these companies. S.E.S. has served on scientific advisory boards on biomarker testing and clinical care pathways for Eisai and Novo Nordisk and has received speaking fees for presentations on biomarker testing from Eisai, Eli Lilly and Novo Nordisk. M.S.-C. has received consultancy/speaker fees (paid to the institution) from Almirall, Eli Lilly, Novo Nordisk and Roche Diagnostics in the past 36 months. M.S.-C. has received consultancy fees or served on the advisory boards (paid to the institution) of Eli Lilly, Grifols, Novo Nordisk and Roche Diagnostics. M.S.-C. was granted a project and is a site investigator of a clinical trial (funding granted to the institution) by Roche Diagnostics. M.S.-C. did not receive any personal compensation from these organizations or any other for-profit organization. The other authors declare no competing interests.

Peer review

Peer review information

Nature Aging thanks Marcus Bantscheff, Yong Shen and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary information

Supplementary Information (download PDF )

Supplementary Figs. 1–14 with table of contents.

Reporting Summary (download PDF )

Supplementary Tables (download XLSX )

Supplementary Tables 1–33 with table of contents.

Source data

Source Data Fig. 2 (download XLSX )

Input data for Fig. 2b–h. Statistical source data from differential abundance analysis, progression analysis and correlation matrix.

Source Data Fig. 3 (download XLSX )

Input data for Fig. 3a–c. Statistical source data from meta-analysis and differential abundance analysis in external datasets.

Source Data Fig. 5 (download XLSX )

Input data for Fig. 5b–e. Statistical source data for ROC and Kaplan–Meier plots.

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Heo, G., Xu, Y., Wang, E. et al. Large-scale plasma proteomic profiling unveils diagnostic biomarkers and pathways for Alzheimer’s disease. Nat Aging 5, 1114–1131 (2025). https://doi.org/10.1038/s43587-025-00872-8

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