Plasma proteomic signatures for type 2 diabetes and related traits in the UK Biobank cohort
Authors' conclusion
Plasma PSs modestly improve type 2 diabetes prediction beyond clinical and genetic factors. Candidate causally associated proteins deserve further study as potential novel therapeutic targets for type 2 diabetes.
Study context
- Population
- UK Biobank participants (three subcohorts) with clinical, genetic and proteomic data
- Exposure
- Proteomic signatures vs clinical and genetic factors for prediction
- Outcome
- Predictive performance for truncal fat, estimated VO2max and T2D
Methodological detailsthree UK Biobank subcohorts, least absolute shrinkage and selection operator (LASSO) regression, two-sample Mendelian randomization
- Three UK Biobank subcohorts
- LASSO regression
- Two-sample Mendelian randomization
Main finding
As reported in the abstract
Prediction of truncal fat, VO2max and T2D
- LASSO-derived PSs improved prediction of truncal fat and VO2max over clinical and genetic factors
- Modest improvement in T2D prediction over QDiabetes score when combining a T2D PS augmented with fat- and fitness-associated PSs
Observational data: associations, not effects.
Further findings
- Two-sample Mendelian randomization (MR) identified a few proteins as potentially causal for each trait
Original publication
Plasma proteomic signatures for type 2 diabetes and related traits in the UK Biobank cohort. Diabetes Res Clin Pract. Published 22 April 2025. doi:10.
Composed by AI from the published abstract and checked against it. How Briefs are made →