Plasma multi-omics and machine learning reveal predictive biomarkers for type 2 diabetes and retinopathy in Qatar biobank cohort.
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.
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. These multiomic biomarkers were profiled in 962 individuals (492 controls, 434 T2D and 36 T2D with DR) from the Qatar Biobank (QBB). Random Forest machine learning workflow was used for risk stratification, with model performance assessed by accuracy and area under the receiver operating characteristic curve. SHAP analysis and penalised regression were used to identify key discriminative biomarkers.
The Random Forest classifier achieved robust performance, with an AUC of 0.83, F1 score of 0.78, and overall accuracy of 0.76 in distinguishing T2D cases from controls. A regulatory axis involving miR-29c (protective) and PROM1 (risk-promoting) was identified as a central driver for T2D and DR progression. Protein biomarkers, including ANGPT2 (fold change = 1.64, p-value = 3.8e-03) and PlGF (fold change = 0.66, p-value = 3.7e-02), were significantly associated with vascular complications.
Integrating multi-omics data with machine learning enables accurate risk stratification for T2D and DR in Middle Eastern populations. The miR-29c-PROM1 axis and associated proteins represent promising biomarkers for early detection and targeted intervention. Leveraging QBB resources, this study lays the groundwork for precision health initiatives aimed at mitigating diabetes-related complications in a high-risk Middle Eastern cohort.
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. These multiomic biomarkers were profiled in 962 individuals (492 controls, 434 T2D and 36 T2D with DR) from the Qatar Biobank (QBB). Random Forest machine learning workflow was used for risk stratification, with model performance assessed by accuracy and area under the receiver operating characteristic curve. SHAP analysis and penalised regression were used to identify key discriminative biomarkers.
The Random Forest classifier achieved robust performance, with an AUC of 0.83, F1 score of 0.78, and overall accuracy of 0.76 in distinguishing T2D cases from controls. A regulatory axis involving miR-29c (protective) and PROM1 (risk-promoting) was identified as a central driver for T2D and DR progression. Protein biomarkers, including ANGPT2 (fold change = 1.64, p-value = 3.8e-03) and PlGF (fold change = 0.66, p-value = 3.7e-02), were significantly associated with vascular complications.
Integrating multi-omics data with machine learning enables accurate risk stratification for T2D and DR in Middle Eastern populations. The miR-29c-PROM1 axis and associated proteins represent promising biomarkers for early detection and targeted intervention. Leveraging QBB resources, this study lays the groundwork for precision health initiatives aimed at mitigating diabetes-related complications in a high-risk Middle Eastern cohort.
Authors
Ahmed Ahmed, Bhat Bhat, Jeya Jeya, Wong Wong, Sadida Sadida, Polkamp Polkamp, Hardikar Hardikar, Lakshmi Lakshmi, Hussain Hussain, Chin-Smith Chin-Smith, Ranjan Ranjan, Joglekar Joglekar, Hardikar Hardikar, Fakhro Fakhro, Akil Akil
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