Multi-omic prediction of incident type 2 diabetes.

ABSTRACT:

Aims/hypothesis

The identification of people who are at high risk of developing type 2 diabetes is a key part of population-level prevention strategies. Previous studies have evaluated the predictive utility of omics measurements, such as metabolites, proteins or polygenic scores, but have considered these separately. The improvement that combined omics biomarkers can provide over and above current clinical standard models is unclear. The aim of this study was to test the predictive performance of genome, proteome, metabolome and clinical biomarkers when added to established clinical prediction models for type 2 diabetes.

Methods

We developed sparse interpretable prediction models in a prospective, nested type 2 diabetes case-cohort study (N=1105, incident type 2 diabetes ca

SUBMITTER: Carrasco-Zanini J 

PROVIDER: S-EPMC10709231 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature