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Peer-reviewedCC BYNature Medicine

Plasma proteomic signatures of cellular aging predict human disease

  • Daisy Yi Ding1,2,3
  • Veronica Augustina Bot4,2,3
  • Kenneth L. Chen5
  • James E. Groves6
  • Róbert Pálovics1
  • Daisuke Masuda7,3
  • Amelia Farinas8,2,3
  • Hamilton Se‐Hwee Oh9,10,11,12
  • Viktoria Wagner1,2,3
  • Nannan Lu1,2,3
  • Carlos Cruchaga13,14
  • Alina Isakova2
2 more authors
  • Jonathan M. Schott6,15
  • Tony Wyss‐Coray16,17,18
18 affiliations
  1. 1Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA
  2. 2The Phil and Penny Knight Initiative for Brain Resilience, Stanford University, Stanford, CA, USA
  3. 3Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
  4. 4Graduate Program in Bioengineering, Stanford University, Stanford, CA, USA
  5. 5Divisions of Hematology and Oncology, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
  6. 6Dementia Research Centre, UCL Queen Square Institute of Neurology, London, UK
  7. 7Department of Management Science and Engineering, Stanford University, Stanford, CA, USA
  8. 8Graduate Program in Neuroscience, Stanford University, Stanford, CA, USA
  9. 9Brain and Body Research Center of the Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
  10. 10Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
  11. 11Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
  12. 12Ronald M. Loeb Center for Alzheimer's Disease, Icahn School of Medicine at Mount Sinai, New York, NY, USA
  13. 13Department of Psychiatry, Washington University, St. Louis, MO, USA
  14. 14NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, USA
  15. 15UK Dementia Research Institute at UCL, London, UK
  16. 16Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA. [email protected]
  17. 17The Phil and Penny Knight Initiative for Brain Resilience, Stanford University, Stanford, CA, USA. [email protected]
  18. 18Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA. [email protected]
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Abstract

Aging is asynchronous across cells and organs, but whether plasma proteins can capture cell type-specific aging and predict disease and mortality remains unknown. We developed machine learning models to estimate the biological age of more than 40 distinct cell types-spanning neuronal, immune, glial, endocrine, epithelial, and musculoskeletal origins-using over 7,000 plasma proteins measured in 60,000 individuals across three cohorts, comprising the largest human plasma proteomics aging study to date. Individuals showed heterogeneous aging profiles, with 20-25% exhibiting accelerated aging in a single cell type and 1-3% across ten or more cell types. APOE genotype showed antagonistic aging effects in different cell types: APOE4 carriers exhibited older astrocytes but younger macrophages, while APOE2 carriers showed the inverse. Cellular aging signatures were uniquely associated with disease status and predicted incident disease and mortality over 15 years of follow-up. Amyotrophic lateral sclerosis (ALS) showed the strongest association with skeletal myocyte aging (hazard ratio = 12.7 for extreme accelerated versus youthful aging). In Alzheimer's disease (AD), prevalent cases showed accelerated aging across multiple neural and peripheral cell types, with extreme astrocyte aging conferring AD risk comparable to APOE4 carrier status. Moreover, extreme astrocyte aging increased AD risk in APOE4/4 carriers threefold, while youthful astrocytes strikingly reduced risk. Beyond neurodegeneration, respiratory cell aging identified smokers at 58% higher lung cancer risk, and myeloid aging identified normoglycemic individuals at higher diabetes risk. Both specific cellular vulnerabilities and cumulative aging burden influenced survival, wherein youthful immune or neuronal profiles were protective. A polycellular aging risk score provided robust mortality risk stratification across platforms and cohorts. These findings establish a framework for quantifying biological aging at the cellular resolution using plasma proteomics, revealing heterogeneity in aging trajectories and their impact on disease susceptibility and resilience.

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Cite

BibTeXRIS
BibTeX
@article{ding2026plasma,
  title = {{Plasma proteomic signatures of cellular aging predict human disease}},
  author = {Daisy Yi Ding and Veronica Augustina Bot and Kenneth L. Chen and James E. Groves and Róbert Pálovics and Daisuke Masuda and Amelia Farinas and Hamilton Se‐Hwee Oh and Viktoria Wagner and Nannan Lu and Carlos Cruchaga and Alina Isakova and Jonathan M. Schott and Tony Wyss‐Coray},
  journal = {Nature Medicine},
  year = {2026},
  month = jun,
  doi = {10.1038/s41591-026-04446-y},
  url = {https://doi.org/10.1038/s41591-026-04446-y},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - Plasma proteomic signatures of cellular aging predict human disease
AU  - Daisy Yi Ding
AU  - Veronica Augustina Bot
AU  - Kenneth L. Chen
AU  - James E. Groves
AU  - Róbert Pálovics
AU  - Daisuke Masuda
AU  - Amelia Farinas
AU  - Hamilton Se‐Hwee Oh
AU  - Viktoria Wagner
AU  - Nannan Lu
AU  - Carlos Cruchaga
AU  - Alina Isakova
AU  - Jonathan M. Schott
AU  - Tony Wyss‐Coray
PY  - 2026
DA  - 2026/06/01
JO  - Nature Medicine
DO  - 10.1038/s41591-026-04446-y
UR  - https://doi.org/10.1038/s41591-026-04446-y
AB  - Aging is asynchronous across cells and organs, but whether plasma proteins can capture cell type-specific aging and predict disease and mortality remains unknown. We developed machine learning models to estimate the biological age of more than 40 distinct cell types-spanning neuronal, immune, glial, endocrine, epithelial, and musculoskeletal origins-using over 7,000 plasma proteins measured in 60,000 individuals across three cohorts, comprising the largest human plasma proteomics aging study to date. Individuals showed heterogeneous aging profiles, with 20-25% exhibiting accelerated aging in a single cell type and 1-3% across ten or more cell types. APOE genotype showed antagonistic aging effects in different cell types: APOE4 carriers exhibited older astrocytes but younger macrophages, while APOE2 carriers showed the inverse. Cellular aging signatures were uniquely associated with disease status and predicted incident disease and mortality over 15 years of follow-up. Amyotrophic lateral sclerosis (ALS) showed the strongest association with skeletal myocyte aging (hazard ratio = 12.7 for extreme accelerated versus youthful aging). In Alzheimer's disease (AD), prevalent cases showed accelerated aging across multiple neural and peripheral cell types, with extreme astrocyte aging conferring AD risk comparable to APOE4 carrier status. Moreover, extreme astrocyte aging increased AD risk in APOE4/4 carriers threefold, while youthful astrocytes strikingly reduced risk. Beyond neurodegeneration, respiratory cell aging identified smokers at 58% higher lung cancer risk, and myeloid aging identified normoglycemic individuals at higher diabetes risk. Both specific cellular vulnerabilities and cumulative aging burden influenced survival, wherein youthful immune or neuronal profiles were protective. A polycellular aging risk score provided robust mortality risk stratification across platforms and cohorts. These findings establish a framework for quantifying biological aging at the cellular resolution using plasma proteomics, revealing heterogeneity in aging trajectories and their impact on disease susceptibility and resilience.
SN  - 1078-8956
SN  - 1546-170X
SN  - 1744-7933
ER  -