Large-Scale Plasma Proteomics Improves Prediction of Peripheral Artery Disease in Individuals With Type 2 Diabetes: A Prospective Cohort Study
Authors' conclusion
This study identified plasma proteins associated with PAD risk in individuals with T2D. Adding proteomic data into the clinical model significantly improved PAD prediction.
Study context
- Population
- Individuals with T2D from the UK Biobank157 incident peripheral artery disease (PAD) cases
- Participants
- 1,859 individuals
- Exposure
- Clinical variables plus proteomics vs clinical variables alone for PAD prediction
- Outcome
- Incident PAD
- Follow-up
- Median 13.2 years
Methodological detailsUK Biobank, multivariable-adjusted Cox regression, least absolute shrinkage and selection operator (LASSO) protein selection
- UK Biobank
- Multivariable-adjusted Cox regression
- LASSO protein selection
Main finding
As reported in the abstract
Associations of 2,920 plasma proteins with incident PAD and PAD prediction
Proteins associated with PAD risk
463
Adding 45 LASSO-selected proteins or a weighted protein risk score significantly enhanced PAD prediction beyond clinical variables alone; maximum C-index
0.835
Observational data: associations, not effects.
Original publication
Large-Scale Plasma Proteomics Improves Prediction of Peripheral Artery Disease in Individuals With Type 2 Diabetes: A Prospective Cohort Study. Diabetes Care. Published 3 January 2025. doi:10.
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