Plasma proteomics enhances genetic risk prediction and biological understanding of venous thromboembolism
Open accessAbstract
Background Venous thromboembolism (VTE) is a leading cause of morbidity and the third most common vascular cause of death globally. While clinical and genetic risk factors have improved VTE risk stratification, they do not fully capture underlying disease biology. We hypothesized that integrating large-scale plasma proteomics with a validated polygenic risk score (PRS VTE ) would reveal novel biological understanding and improve risk stratification. Methods In 44,138 UK Biobank participants, we examined associations between PRS VTE and 2,911 plasma proteins using multivariable linear regressions. We evaluated proteins as predictors of incident VTE using Cox regression models. We employed bidirectional Mendelian randomization (MR) using cis-acting protein quantitative trait loci (cis-pQTLs) instruments and genome-wide association summary statistics to infer potential causality. A protein-based risk score (ProteinRS) was developed with Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression and compared against a clinical risk model. Results PRS VTE associated with 265 proteins after false-discovery-rate correction (FDR < 0.01). Pathway enrichment analysis of these proteins highlighted cell adhesion, cell-junction organization, and transmembrane receptor signaling. Time-to-event analyses identified 354 proteins that predicted incident VTE (FDR < 0.01). MR implicated 14 proteins in potential causal pathways to VTE, while genetic liability to VTE affected the levels of 44 proteins. A 60-protein ProteinRS improved discrimination beyond clinical factors (area under the curve (AUC) 0.664 vs 0.647; P=0.024). Participants above the 80th percentile of ProteinRS had a 49% higher hazard of VTE (hazard ratio (HR) = 1.49, 95% confidence interval (CI): 1.17-1.90) and those below the 20th percentile had a 48% lower hazard (HR = 0.52, 95% CI: 0.34-0.79) relative to the 20-80th percentile. Conclusions Integrating genetic and proteomics data uncovered molecular pathways that link genetic susceptibility to VTE, highlighted actionable protein targets, and modestly enhanced risk prediction. Plasma proteomics therefore holds promise for precision prevention of VTE.
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Zahed, M., Skafyan, M., & Thornton, P. K. (2026) · Proteomes · doi:10.3390/proteomes14040055
- Authors
- Mei, Y., Vad, O. B., Beyer, A. F., Ghouse, J., & Lundegaard, P. R.
- Affiliations
- University of Copenhagen (DK)
- Aarhus University Hospital (DK)
- Corresponding author
- Lundegaard, Pia Rengtved
- Venue
- Molecular & Cellular Proteomics
- Publisher
- Elsevier BV
- Published
- 2026-10
- Pages
- 101692
- ISSN
- 1535-9476 (ISSN-L)
- OpenAlex
- W7223926070
- Cited by
- 0
- References
- 79
- FWCI
- 0.00
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Molecular & Cellular Proteomics (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Novo Nordisk Foundation
- Topics
- Health Sciences › Medicine › Hematology › Blood Coagulation and Thrombosis Mechanisms
- Health Sciences › Medicine › Internal Medicine › Venous Thromboembolism Diagnosis and Management
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Keywords
- venous thromboembolism, plasma proteomics, genetic risk prediction, biomarkers, inherited thrombophilia, coagulation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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