{
  "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.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA",
    "The Phil and Penny Knight Initiative for Brain Resilience, Stanford University, Stanford, CA, USA",
    "Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA",
    "Graduate Program in Bioengineering, Stanford University, Stanford, CA, USA",
    "Divisions of Hematology and Oncology, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA",
    "Dementia Research Centre, UCL Queen Square Institute of Neurology, London, UK",
    "Department of Management Science and Engineering, Stanford University, Stanford, CA, USA",
    "Graduate Program in Neuroscience, Stanford University, Stanford, CA, USA",
    "Brain and Body Research Center of the Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA",
    "Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA",
    "Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA",
    "Ronald M. Loeb Center for Alzheimer's Disease, Icahn School of Medicine at Mount Sinai, New York, NY, USA",
    "Department of Psychiatry, Washington University, St. Louis, MO, USA",
    "NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, USA",
    "UK Dementia Research Institute at UCL, London, UK",
    "Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA. twc@stanford.edu",
    "The Phil and Penny Knight Initiative for Brain Resilience, Stanford University, Stanford, CA, USA. twc@stanford.edu",
    "Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA. twc@stanford.edu"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 14,
  "authors": [
    {
      "affs": [
        0,
        1,
        2
      ],
      "name": "Daisy Yi Ding",
      "orcid": "0000-0003-2914-9557"
    },
    {
      "affs": [
        3,
        1,
        2
      ],
      "name": "Veronica Augustina Bot",
      "orcid": "0009-0008-5060-1142"
    },
    {
      "affs": [
        4
      ],
      "name": "Kenneth L. Chen"
    },
    {
      "affs": [
        5
      ],
      "name": "James E. Groves",
      "orcid": "0000-0002-7803-8696"
    },
    {
      "affs": [
        0
      ],
      "name": "Róbert Pálovics",
      "orcid": "0000-0003-0202-7760"
    },
    {
      "affs": [
        6,
        2
      ],
      "name": "Daisuke Masuda",
      "orcid": "0009-0001-1354-3245"
    },
    {
      "affs": [
        7,
        1,
        2
      ],
      "name": "Amelia Farinas",
      "orcid": "0000-0002-6697-8883"
    },
    {
      "affs": [
        8,
        9,
        10,
        11
      ],
      "name": "Hamilton Se‐Hwee Oh",
      "orcid": "0000-0001-8192-7593"
    },
    {
      "affs": [
        0,
        1,
        2
      ],
      "name": "Viktoria Wagner",
      "orcid": "0000-0002-1957-0658"
    },
    {
      "affs": [
        0,
        1,
        2
      ],
      "name": "Nannan Lu",
      "orcid": "0000-0003-0905-8199"
    },
    {
      "affs": [
        12,
        13
      ],
      "name": "Carlos Cruchaga",
      "orcid": "0000-0002-0276-2899"
    },
    {
      "affs": [
        1
      ],
      "name": "Alina Isakova",
      "orcid": "0000-0003-1113-6889"
    },
    {
      "affs": [
        5,
        14
      ],
      "name": "Jonathan M. Schott",
      "orcid": "0000-0003-2059-024X"
    },
    {
      "affs": [
        15,
        16,
        17
      ],
      "name": "Tony Wyss‐Coray",
      "orcid": "0000-0001-5893-0831"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/553akw37gyhcw.bib",
  "cited_by": 23,
  "doi": "10.1038/s41591-026-04446-y",
  "fulltext_url": null,
  "id": "553akw37gyhcw",
  "issns": [
    "1078-8956",
    "1546-170X",
    "1744-7933"
  ],
  "landing_url": "https://doi.org/10.1038/s41591-026-04446-y",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
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      "openalex",
      "1302|Genetics, Aging, and Longevity in Model Organisms"
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  "openalex": "W7164805684",
  "pdf_url": null,
  "pmcid": "PMC13279268",
  "pmid": "42297981",
  "published": "2026-06-01",
  "publisher": "Nature Portfolio",
  "ris": "https://research.draconitic.com/paper/553akw37gyhcw.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:26:46Z",
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      "url": "https://doi.org/10.1038/s41591-026-04446-y"
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  "subjects": [
    {
      "field": "biology",
      "origin": "openalex:1302|Genetics, Aging, and Longevity in Model Organisms",
      "subfield": "cell-biology"
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  ],
  "title": "Plasma proteomic signatures of cellular aging predict human disease",
  "title_text": "Plasma proteomic signatures of cellular aging predict human disease",
  "type": "peer-reviewed",
  "updated_at": "2026-11-09T13:21:05Z",
  "url": "https://research.draconitic.com/paper/553akw37gyhcw",
  "venue": "Nature Medicine",
  "versions": []
}