Plasma protein patterns as comprehensive indicators of health
Open accessAbstract
Proteins are effector molecules that mediate the functions of genes 1,2 and modulate comorbidities 3-10, behaviors and drug treatments 11. They represent an enormous potential resource for personalized, systemic and data-driven diagnosis, prevention, monitoring and treatment. However, the concept of using plasma proteins for individualized health assessment across many health conditions simultaneously has not been tested. Here, we show that plasma protein expression patterns strongly encode for multiple different health states, future disease risks and lifestyle behaviors. We developed and validated protein-phenotype models for 11 different health indicators: liver fat, kidney filtration, percentage body fat, visceral fat mass, lean body mass, cardiopulmonary fitness, physical activity, alcohol consumption, cigarette smoking, diabetes risk and primary cardiovascular event risk. The analyses were prospectively planned, documented and executed at scale on archived samples and clinical data, with a total of ~85 million protein measurements in 16,894 participants. Our proof-of-concept study demonstrates that protein expression patterns reliably encode for many different health issues, and that large-scale protein scanning 12-16 coupled with machine learning is viable for the development and future simultaneous delivery of multiple measures of health. We anticipate that, with further validation and the addition of more protein-phenotype models, this approach could enable a single-source, individualized so-called liquid health check.
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Kidney function has an effect on plasma protein concentrations that is not fully understood but can confound ageing analysis.
“Kidney function has an effect on plasma protein concentrations that is not fully understood but can confound ageing analysis101,102.”
From Measuring biological age using omics data (2022) · cites this paper as [101]
Proteins have shown great clinical utility and promise as prognostic biomarkers in many settings relevant to ageing, prominently in heart, kidney, liver, metabolic, inflammatory and neurodegenerative disease risk.
“Additionally, they have shown great clinical utility and promise as prognostic biomarkers in many settings relevant to ageing, prominently in heart, kidney, liver, metabolic, inflammatory and neurodegenerative disease risk101,161,162.”
From Measuring biological age using omics data (2022) · cites this paper as [101]
The coverage of proteins on the platform has taken into account proteins known to be relevant to human disease, including neurodegenerative diseases and cardiovascular diseases.
“The coverage of proteins on the platform has taken into account proteins known to be relevant to human disease, including neurodegenerative diseases55 and cardiovascular diseases56; thus it has been widely used for biomarker discovery.”
From Genomic atlas of the proteome from brain, CSF and plasma prioritizes proteins implicated in neurological disorders (2021) · cites this paper as [56]
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- Authors
- Williams, S. A., Kivimaki, M., Langenberg, C., Hingorani, A. D., Casas, J. P., Bouchard, C., Jonasson, C., Sarzynski, M. A., Shipley, M. J., Alexander, L., Ash, J., Bauer, T., Chadwick, J., Datta, G., DeLisle, R. K., Hagar, Y., Hinterberg, M., Ostroff, R., Weiss, S., Ganz, P., & Wareham, N. J.
- Affiliations
- SomaLogic (United States) (US)
- University of Cambridge (GB)
- British Heart Foundation (GB)
- VA Boston Healthcare System (US)
- Pennington Biomedical Research Center (US)
- Norwegian University of Science and Technology (NO)
- University of South Carolina (US)
- University College London (GB)
- San Francisco General Hospital (US)
- Corresponding author
- Williams, Stephen A.
- ORCID
- Venue
- Nature Medicine
- Published
- 2019-12
- Volume
- 25
- Issue
- 12
- Pages
- 1851-1857
- ISSN
- 1078-8956 (ISSN-L)
- 1546-170X
- PMC
- PMC6922049
- OpenAlex
- W2990851413
- Cited by
- 506
- References
- 44
- Citations by year
- 2026: 60 · 2025: 96 · 2024: 95 · 2023: 75 · 2022: 88 · 2021: 57 · 2020: 34 · 2019: 1
- FWCI
- 14.88
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Nature Medicine (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- UCL Discovery (University College London) (submittedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- NORA - Norwegian Open Research Archives (submittedVersion, other-oa)
- Funders
- National Institute for Health and Care Research (NF-SI-0515-10091, NF-SI-0617-10149)
- British Heart Foundation (RG/16/11/32334, AA/18/6/34223, RG/10/12/28456, SP/13/6/30554)
- Rosetrees Trust (M202-F2)
- Medical Research Council (MR/S011676/1, MR/R024227/1, MC_UU_12015/1)
- National Institute on Aging (RF1AG062553, R01AG056477)
- National Heart, Lung, and Blood Institute (R01HL146462, R01HL045670)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Advanced Biosensing Techniques and Applications
- Physical Sciences › Environmental Science › Health, Toxicology and Mutagenesis › Health, Environment, Cognitive Aging
- Physical Sciences › Chemistry › Spectroscopy › Advanced Proteomics Techniques and Applications
- Keywords
- plasma proteins, protein expression patterns, liver fat, kidney filtration, fat mass, visceral fat, lean body mass, cardiorespiratory fitness, physical activity, alcohol consumption, cigarette smoking, diabetes risk, cardiovascular event risk, large-scale proteomics, machine learning
- MeSH terms
- Adipose Tissue, Blood Proteins, Body Composition, Female, Humans, Life Style, Liver, Male, Risk Factors, Exercise, Intra-Abdominal Fat, Precision Medicine
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