Genetic and Clinical Determinants of Variation in Drug Response in Type 2 Diabetes: Insights From the Scottish and UK Biobank Cohorts

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ABSTRACT Objective Treatment response in type 2 diabetes (T2D) varies widely among individuals. This study aimed to quantify the contributions of clinical characteristics and genetic predisposition as measured through partitioned polygenic risk scores (pPRS) to variation in glycemic response to glucose‐lowering therapies. Research Design and Method We analysed data from two population‐based cohorts: the Genetics of Diabetes Audit and Research in Tayside Scotland (GoDARTS) and the UK Biobank (UKBB). GoDARTS included 41 802 patients who initiated one of six major drug classes, of whom 11 615 had available genotype data. UKBB contributed 9371 individuals, including 8293 with genetic data. The primary outcome was glycaemic response, defined as the change in HbA1c 12 months after treatment initiation. Variables included demographic and clinical factors (age, sex, BMI, baseline HbA1c, kidney and liver function markers) and 14 pPRS representing T2D‐related biological pathways. Linear regression models were fitted within each cohort and drug class (metformin, sulfonylureas, TZDs, DPP4i, SGLT2i, GLP‐1RA), and effect estimates were combined using fixed‐effect meta‐analysis. Result Baseline HbA1c was most strongly associated with glycemic response ( p < 0.001). Older age was consistently associated with greater HbA1c reduction, while BMI and total cholesterol demonstrated drug‐class‐specific associations, with higher BMI associated with improved response to TZDs and higher total cholesterol generally associated with poorer glycaemic outcomes. Meta‐analysis across GoDARTS and UK Biobank showed that higher overall T2D genetic risk was associated with greater HbA1c reduction with sulfonylureas ( β = −0.46 mmol/mol, p = 0.013). Specific genetic profiles were also associated with drug responses, including β‐cell function clusters with sulfonylureas ( β = −0.57, p = 0.002), obesity‐related variants with GLP‐1RA ( β = −1.49, p = 0.04), liver‐lipid variants with SGLT2 inhibitors ( β = −0.84, p = 0.05), and bilirubin pPRS with DPP‐4 inhibitors ( β = −0.69, p = 0.006). Conclusion Both clinical and genetic factors significantly contribute to inter‐individual variability in T2D drug response. Partitioned PRSs provide mechanistic insights into drug‐specific pathways and have the potential to inform precision prescribing and optimise therapeutic outcomes in routine diabetes care.