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Cerebrospinal fluid proteomic signatures in cognitively normal individuals identify distinct clusters linked to neurodegeneration

Nature Aging volume 5, pages 2125–2141 (2025)Cite this article

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Abstract

Age and APOE ε4 are major risk factors for Alzheimer’s disease (AD), while sex differences exist in disease prevalence and progression. Cerebrospinal fluid (CSF) proteomics can provide additional insights into brain aging and AD. To examine proteomic changes due to age, sex and apolipoprotein E (APOE) ε4 along with amyloid status before clinical AD occurs, we profiled 6,175 proteins in the CSF from 994 cognitively normal individuals aged 43–91 years. We identified and replicated 2,172 age-associated, 711 sex-associated, 193 APOE ε4-associated and 1,807 amyloid-associated proteins, with extensive overlap suggesting their interplay. These CSF-specific signatures were distinct from those in plasma. Network analysis revealed two proteomic modules—M2 (age-associated, sex-associated and amyloid-associated) and M6 (age-associated and sex-associated)—which were linked to neuropsychiatric and aging-related diseases. Together, our study provides proteomic changes during the early phase of AD, which may help identify new therapeutic targets of AD.

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Fig. 1: Study overview.
Fig. 2: Impact of aging, sex, APOE4 and amyloid status on CSF proteomics.
Fig. 3: Network analyses.
Fig. 4: Enrichment analysis for proteins in M2.
Fig. 5: Enrichment analysis for proteins in M6.
Fig. 6: Impact of aging, sex, APOE4 and amyloid status on plasma proteomics.
Fig. 7: Sex-stratified proteomic clocks in the CSF and plasma.

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Data availability

The Knight ADRC CSF and plasma proteomic data can be requested 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. Details on accessing available data and study protocols can be obtained from https://knightadrc.wustl.edu/. ADNI data can be requested through https://adni.loni.usc.edu/. FACE data can be requested through www.fundacioace.com. Emory Diversity data can be requested through www.synapse.org/Synapse:syn44132374 (Project SynID syn44132374). Source data are available.

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Acknowledgements

We thank all the participants and their families, and the involved cohorts, institutions and their staff. This work was supported by grants from the National Institutes of Health (NIH)—R01 AG074007 (Y.J.S.), R01 AG044546 (C.C.), P01 AG003991 (C.C. and J.C.M.), RF1 AG053303 (C.C.), RF1 AG058501 (C.C.), U01 AG058922 (C.C.), P30 AG066444 (J.C.M.) and P01 AG026276 (J.C.M.)—the Chan Zuckerberg Initiative, the Michael J. Fox Foundation (C.C.), the Alzheimer’s Association Zenith Fellows Award (no. ZEN-22-848604, awarded to C.C.) and the Basic Science Research Program through the National Research Foundation of Korea (NRF) and the Ministry of Education (NRF-2022R1A2C1092497, awarded to T.S.P.). Data collection and sharing for this project was funded by ADNI (NIH grant no. U01 AG024904) and Department of Defense ADNI (award no. W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica; Biogen; Bristol Myers Squibb; CereSpir; Cogstate; Eisai; Elan Pharmaceuticals; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche and its affiliated company Genentech; Fujirebio; GE Healthcare; IXICO; Janssen Alzheimer Immunotherapy Research & Development; Johnson & Johnson Pharmaceutical Research & Development; Lumosity; Lundbeck; Merck & Co.; Meso Scale Diagnostics; NeuroRx Research; Neurotrack Technologies; Novartis Corporation; Pfizer; Piramal Imaging; Servier; Takeda Pharmaceutical; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the NIH (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education; 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.

Author information

Author notes

  1. These authors contributed equally: Dahun Seo, Anh N. Do.

  2. These authors jointly supervised this work: Yun Ju Sung, Carlos Cruchaga.

Authors and Affiliations

  1. NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, USA

    Dahun Seo, Anh N. Do, Gyujin Heo, Jiseon Kwon, Soomin Song, Catherine Apio, Cheolmin Matthew Lee, Jigyasha Timsina, Katherine Gong, Yike Chen, Menghan Liu, Pat Kohlfeld, John Budde, Laura Ibanez, Carlos Cruchaga & Yun Ju Sung

  2. Department of Statistics, Seoul National University, Seoul, Korea

    Dahun Seo & Taesung Park

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

    Anh N. Do & Yun Ju Sung

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

    Anh N. Do, Gyujin Heo, Jiseon Kwon, Soomin Song, Jigyasha Timsina, Katherine Gong, Yike Chen, Menghan Liu, Pat Kohlfeld, John Budde, Laura Ibanez, Carlos Cruchaga & Yun Ju Sung

  5. Interdisciplinary programs in Bioinformatics, Seoul National University, Seoul, Korea

    Catherine Apio & Taesung Park

  6. Health Sciences Integrated Program, Northwestern University, Chicago, IL, USA

    Cheolmin Matthew Lee

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

    Merce Boada, Adelina Orellana, Maria Victoria Fernandez & Agustin Ruiz

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

    Merce Boada & Agustin Ruiz

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

    Agustin Ruiz

  10. The Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA

    John C. Morris, Suzanne E. Schindler & Carlos Cruchaga

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

    John C. Morris, Suzanne E. Schindler & Laura Ibanez

  12. Hope Center, Washington University School of Medicine, St. Louis, MO, USA

    Laura Ibanez & Carlos Cruchaga

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  1. Dahun Seo
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  2. Anh N. Do
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  3. Gyujin Heo
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  6. Catherine Apio
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  7. Cheolmin Matthew Lee
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  8. Jigyasha Timsina
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  9. Katherine Gong
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  10. Yike Chen
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  12. Pat Kohlfeld
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  13. John Budde
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  14. Merce Boada
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  15. Adelina Orellana
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  16. Maria Victoria Fernandez
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  17. Agustin Ruiz
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  18. John C. Morris
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  19. Suzanne E. Schindler
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  20. Laura Ibanez
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  21. Taesung Park
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  22. Carlos Cruchaga
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  23. Yun Ju Sung
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Contributions

D.S., A.N.D., G.H., C.M.L. and C.A. performed the analysis, interpreted the results and drafted the manuscript. J.K., S.S. and C.A. interpreted the results, created the figures and were involved in manuscript revision. J.T., K.G. and Y.C. performed the proteomic data processing and QC. M.L. performed the phenotype data processing and QC. J.B. and P.K. acquired the Knight ADRC samples and data. M.B., A.O., M.V.F. and A.R. acquired the phenotypes and CSF samples in the FACE cohort. J.C.M. and S.E.S. obtained the funding, recruited the Knight ADRC cohort and curated the phenotype data. L.I. obtained funding to generate the proteomic data. T.S.P. obtained the funding, supervised the work and interpreted the results. Y.J.S. and C.C. obtained the funding to generate the proteomic data, designed the study, supervised the work, interpreted the results and drafted the manuscript. All authors read and approved the manuscript.

Corresponding author

Correspondence to Yun Ju Sung.

Ethics declarations

Competing interests

C.C. has received research support from GSK and Eisai. C.C. is a member of the advisory board of Circular Genomics and owns stocks. S.E.S. has served on scientific advisory boards on biomarker testing and education for Eisai and Novo Nordisk, and has received speaking fees for presentations on biomarker testing from Eisai, Eli Lilly and Company and Novo Nordisk. The other authors declare no competing interests. The study funders had no role in the collection, analysis or interpretation of the data, the writing of the manuscript, or the decision to submit the manuscript for publication.

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Nature Aging thanks Johan Gobom and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Comparison between discovery and replication in CSF.

Scatter plots comparing results between the Knight ADRC cohort (x-axis) and the ADNI & FACE cohorts (y-axis) for age (a), sex (b), APOE4 (c), and amyloid status (d). Each point represents a protein with blue for those replicated; light blue for significant in discovery but not replicated; and gray for not significant in discovery. Pearson correlation coefficients (ρ) and p-values indicate consistency of association results between discovery and replication cohorts. Extremely small P values are reported as P < 2.2*10−308. Abbreviations: ADNI, Alzheimer’s Disease Neuroimaging Initiative; FACE, Fundacio ACE; APOE4, Apolipoprotein E ε4; ADRC, Alzheimer’s Disease Research Center.

Source data

Extended Data Fig. 2 Relationship between proteomic changes due to age and those due to amyloid status in the Knight ADRC cohort.

Scatter plot illustrating the relationship between protein changes due to age (x-axis) and changes due to amyloid status (y-axis). Proteins shown in blue are significant with age and amyloid status at FDR < 0.05, with the top 10 most significant proteins labeled. Abbreviations: ADRC, Alzheimer’s Disease Research Center; FDR, False Discovery Rate.

Source data

Extended Data Fig. 3 Aging trajectories for 11 replicated proteins.

Aging trajectory of 11 proteins that were influenced by all four factors (age, sex, APOE status, and amyloid levels). These 11 proteins were newly identified in our study. Each trajectory illustrates how protein abundance changes over time.

Source data

Extended Data Fig. 4 Comparison between Knight ADRC and two mass-spectrometry data.

Scatter plots comparing association estimates across different cohorts. The left column compares results between Knight ADRC (x-axis) and Emory diversity cohort (y-axis), while the right column compares results between Knight ADRC (x-axis) and Wesenhagen et al. study (y-axis). Each point represents a protein with blue for those validated; light blue for significant in discovery but not validated; and gray for not significant in discovery. Pearson correlation test is used to evaluate association in (a-h).

Source data

Extended Data Fig. 5 Aging trajectories for 11 protein modules.

The LOESS plot displaying aging trajectories of each protein for 11 modules. Each panel shows aging trajectories separated by amyloid status (Amyloid- and Amyloid + ). Individual protein trajectories are shown as thin lines (blue for males, orange for females), while the average trajectories are represented by thicker lines.

Source data

Extended Data Fig. 6 Comparison between plasma Knight ADRC and UK biobank pharma proteomics project (UKB-PPP) data.

Scatter plots comparing results between the Knight ADRC cohort and the UKB-PPP (a) age effects and (b) sex effects. Each point represents a protein with blue for those significant in both cohorts; light blue for significant in Knight ADRC but not significant in UKB-PPP; and gray for not significant in Knight ADRC. Pearson correlation test was used to evaluate association.

Source data

Extended Data Fig. 7 Validation of sex-specific proteomic clocks in CSF and plasma.

(a) Performance of CSF proteomic clock in the independent ADNI (left) and FACE (right) for males (top) and females (bottom), separately (b) Performance of sex-specific plasma proteomic clock in the additional Knight ADRC samples that were excluded for creating proteomic clock. Pearson correlation test is used to evaluate association in (a-b).

Source data

Supplementary information

Supplementary Information

Supplementary Figs. 1–5 with table of contents and Tables 1–13 with table of contents.

Reporting Summary

Supplementary Tables 1–13

Supplementary Tables with table of contents.

Source data

Source Data Fig. 2

Input data for Fig. 2. Statistical source data from differential abundance analysis, UpSet plot and correlation matrix.

Source Data Fig. 3

Input data for Fig. 3. Statistical source data from the correlation matrix of WGCNA modules and aging trajectories of proteins in M2 and M6.

Source Data Fig. 4

Input data for Fig. 4. Statistical source data from enrichment analysis for M2 with Disease Ontology, Gene Ontology, cell type and survival analysis with CARTPT.

Source Data Fig. 5

Input data for Fig. 5. Statistical source data from the enrichment analysis for M6 with Disease Ontology, Gene Ontology, cell type and survival analysis with CFD.

Source Data Fig. 6

Input data for Fig. 6. Statistical source data from the differential abundance analysis in plasma data and effect size between CSF and plasma results.

Source Data Fig. 7

Input data for Fig. 7. Statistical source data from the proteomic clock analysis.

Source Data Extended Data Fig. 1

Input data for Extended Data Fig. 1. Statistical source data from the differential abundance analysis of Discovery and Replication datasets.

Source Data Extended Data Fig. 2

Input data for Extended Data Fig. 2. Statistical source data from differential abundance analysis for age and amyloid positivity.

Source Data Extended Data Fig. 3

Input data for Extended Data Fig. 3. Statistical source data from the aging trajectories of 11 replicated proteins.

Source Data Extended Data Fig. 4

Input data for Extended Data Fig. 4. Statistical source data from the differential abundance analysis from the Knight ADRC and two mass-spectrometry datasets.

Source Data Extended Data Fig. 5

Input data for Extended Data Fig. 5. Statistical source data from the aging trajectories of 11 protein modules.

Source Data Extended Data Fig. 6

Input data for Extended Data Fig. 6. Statistical source data from the differential abundance analysis of the Knight ADRC and UKB-PPP datasets.

Source Data Extended Data Fig. 7

Input data for Extended Data Fig. 7. Statistical source data from the proteomic clock analysis of CSF and plasma results.

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Seo, D., Do, A.N., Heo, G. et al. Cerebrospinal fluid proteomic signatures in cognitively normal individuals identify distinct clusters linked to neurodegeneration. Nat Aging 5, 2125–2141 (2025). https://doi.org/10.1038/s43587-025-00971-6

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