Identifying and ranking non-traditional risk factors for cardiovascular disease prediction in people with type 2 diabetes.

ABSTRACT:

Background

Cardiovascular disease (CVD) prediction models perform poorly in people with type 2 diabetes (T2DM). We aimed to identify potentially non-traditional CVD predictors for six facets of CVD (including coronary heart disease, ischemic stroke, heart failure, and atrial fibrillation) in people with T2DM.

Methods

We analysed data on 600+ features from the UK Biobank, stratified by history of CVD and T2DM: 459,142 participants without diabetes or CVD, 14,610 with diabetes but without CVD, and 4432 with diabetes and CVD. A penalised generalized linear model with a binomial distribution was used to identify CVD-related features. Subsequently, a 20% hold-out set was used to replicate identified features and provide an importance based ranking.

Results

Here we show tha

SUBMITTER: Dziopa K 

PROVIDER: S-EPMC11906859 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature