A proteomic surrogate for cardiovascular outcomes that is sensitive to multiple mechanisms of change in risk
Open accessAbstract
A reliable, individualized, and dynamic surrogate of cardiovascular risk, synoptic for key biologic mechanisms, could shorten the path for drug development, enhance drug cost-effectiveness and improve patient outcomes. We used highly multiplexed proteomics to address these objectives, measuring about 5000 proteins in each of 32,130 archived plasma samples from 22,849 participants in nine clinical studies. We used machine learning to derive a 27-protein model predicting 4-year likelihood of myocardial infarction, stroke, heart failure, or death. The 27 proteins encompassed 10 biologic systems, and 12 were associated with relevant causal genetic traits. We independently validated results in 11,609 participants. Compared to a clinical model, the ratio of observed events in quintile 5 to quintile 1 was 6.7 for proteins versus 2.9 for the clinical model, AUCs (95% CI) were 0.73 (0.72 to 0.74) versus 0.64 (0.62 to 0.65), c -statistics were 0.71 (0.69 to 0.72) versus 0.62 (0.60 to 0.63), and the net reclassification index was +0.43. Adding the clinical model to the proteins only improved discrimination metrics by 0.01 to 0.02. Event rates in four predefined protein risk categories were 5.6, 11.2, 20.0, and 43.4% within 4 years; median time to event was 1.71 years. Protein predictions were directionally concordant with changed outcomes. Adverse risks were predicted for aging, approaching an event, anthracycline chemotherapy, diabetes, smoking, rheumatoid arthritis, cancer history, cardiovascular disease, high systolic blood pressure, and lipids. Reduced risks were predicted for weight loss and exenatide. The 27-protein model has potential as a “universal” surrogate end point for cardiovascular risk.
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The SomaScan assay uses DNA-based slow off-rate modified aptamers (SOMAmer reagents) to quantify the relative abundance of approximately 7000 proteins with high specificity and limits of detection largely comparable to antibody-based assays.
“The assay uses DNA-based slow off-rate modified aptamers (SOMAmerTM reagents) to quantify the relative abundance of approximately 7000 proteins with high specificity and limits of detection largely comparable to antibody-based assays.”
From Cross-ancestry comparison of aptamer and antibody protein measures (2026) · cites this paper as [55]
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Self-citation
- Authors
- Williams, S. A., Ostroff, R., Hinterberg, M. A., Coresh, J., Ballantyne, C. M., Matsushita, K., Mueller, C. E., Walter, J., Jonasson, C., Holman, R. R., Shah, S. H., Sattar, N., Taylor, R., Lean, M. E., Kato, S., Shimokawa, H., Sakata, Y., Nochioka, K., Parikh, C. R., Coca, S. G., Omland, T., Chadwick, J., Astling, D., Hagar, Y., Kureshi, N., Loupy, K., Paterson, C., Primus, J., Simpson, M., Trujillo, N. P., & Ganz, P.
- Affiliations
- SomaLogic (United States) (US)
- Johns Hopkins University (US)
- Baylor College of Medicine (US)
- University of Basel (CH)
- Norwegian University of Science and Technology (NO)
- University of Oxford (GB)
- Duke University (US)
- University of Glasgow (GB)
- Newcastle University (GB)
- NEC Solution Innovators, Ltd. (Japan) (JP)
- Tohoku University (JP)
- Icahn School of Medicine at Mount Sinai (US)
- University of Oslo (NO)
- Boulder Community Hospital (US)
- San Francisco General Hospital (US)
- Corresponding author
- Williams, Stephen A.
- Ganz, Peter
- ORCID
- Williams, Stephen A.
- Ostroff, Rachel
- Hinterberg, Michael A.
- Coresh, Josef
- Ballantyne, Christie M.
- Matsushita, Kunihiro
- Mueller, Christian E.
- Walter, Joan
- Jonasson, Christian
- Holman, Rury R.
- Shah, Svati H.
- Sattar, Naveed
- Taylor, Roy
- Lean, Michael E.
- Kato, Shintaro
- Shimokawa, Hiroaki
- Sakata, Yasuhiko
- Nochioka, Kotaro
- Parikh, Chirag R.
- Coca, Steven G.
- Omland, Torbjørn
- Chadwick, Jessica
- Astling, David
- Hagar, Yolanda
- Kureshi, Natasha
- Loupy, Kelsey
- Paterson, Clare
- Primus, Jeremy
- Simpson, Missy
- Ganz, Peter
- Venue
- Science Translational Medicine
- Published
- 2022-04-06
- Volume
- 14
- Issue
- 639
- Pages
- eabj9625-eabj9625
- ISSN
- 1946-6234 (ISSN-L)
- 1946-6242
- OpenAlex
- W4223581642
- Cited by
- 97
- References
- 19
- Citations by year
- 2026: 18 · 2025: 25 · 2024: 21 · 2023: 27 · 2022: 6
- FWCI
- 18.86
- Type
- journal article
- Language
- en
- Open access
- green
- eprints.gla.ac.uk
- Licence
- cc-by-sa
- Locations
- Science Translational Medicine (publishedVersion)
- PubMed (publishedVersion)
- ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam) (submittedVersion, cc-by-sa)
- Newcastle University ePrints (Newcastle Univesity)
- Funders
- National Institute on Aging (R01AG052964)
- National Heart, Lung, and Blood Institute (HHSN268201700004I, HHSN268201700001I, R01HL085757, HHSN268201700003I, R01HL087641, HHSN268201700005I, R01HL129856, HHSN268201700002I, R01HL086694)
- National Human Genome Research Institute (U01HG004402)
- National Institute of Diabetes and Digestive and Kidney Diseases (U01DK106962, U01DK108809)
- Topics
- Health Sciences › Medicine › Surgery › Lipoproteins and Cardiovascular Health
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiovascular Health and Risk Factors
- Physical Sciences › Chemistry › Spectroscopy › Advanced Proteomics Techniques and Applications
- Keywords
- cardiovascular risk prediction, plasma proteomics, surrogate endpoints, myocardial infarction, stroke, heart failure, cardiovascular mortality, multiplexed proteomics, anthracycline chemotherapy, exenatide, weight loss, rheumatoid arthritis, diabetes mellitus, smoking, drug development
- MeSH terms
- Cardiovascular Diseases, Heart Failure, Humans, Myocardial Infarction, Biomarkers, Stroke, Proteomics
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