Plasma multi-omics and machine learning reveal predictive biomarkers for type 2 diabetes and retinopathy in Qatar biobank cohort.

ABSTRACT:

Background

Type 2 diabetes (T2D) and its vascular complications, including diabetic retinopathy (DR), are escalating in prevalence globally, with disproportionately high prevalence in Middle Eastern populations, where genetic predispositions and lifestyle factors intersect. Early detection and precise risk stratification remain critical challenges in this region. We hypothesised that an integrated plasma multi-omics profile; comprising microRNA, mRNA, and protein biomarkers, could accurately distinguish individuals with T2D and its complications in a Middle Eastern cohort.

Methods

A candidate panel of mRNA and protein biomarkers identified from in vitro hyperglycaemia models, along with a vascular microRNA signature previously defined in an Australian cohort, was evaluated.

SUBMITTER: Ahmed I 

PROVIDER: S-EPMC12541984 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature