Improved sex-specific cardiovascular risk prediction with multi-omics data in people with type 2 diabetes.

ABSTRACT:

Background

To evaluate whether integrating proteomics, metabolomics, and a cardiovascular disease specific polygenic risk score (CVD-PRS) in the SCORE2-Diabetes model improves sex-specific 10-year prediction of major adverse cardiovascular events (MACE) in individuals with type 2 diabetes (T2D).

Methods

Genome-wide association study (GWAS), plasma proteomics (with the Olink Explore 3072 platform), and metabolomics (with nuclear magnetic resonance spectroscopy by Nightingale Health) data were measured in the UK Biobank. A novel sex-specific protein algorithm was developed using bootstrap-LASSO (Least absolute shrinkage and selection operator) regression. The CVD-PRS and sex-specific metabolite algorithms were used from previous UK Biobank projects. In a subset of 990 particip

SUBMITTER: Xie R 

PROVIDER: S-EPMC12837037 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature