Validation of an algorithm for selection of SGLT2 and DPP4 inhibitor therapies in people with type 2 diabetes across major UK ethnicity groups: a retrospective cohort study

TY - JOUR

T1 - Validation of an algorithm for selection of SGLT2 and DPP4 inhibitor therapies in people with type 2 diabetes across major UK ethnicity groups

T2 - a retrospective cohort study

AU - Güdemann, Laura M.

AU - Young, Katherine G.

AU - Cardoso, Pedro

AU - Mateen, Bilal A.

AU - Holman, Rury R.

AU - Sattar, Naveed

AU - Pearson, Ewan R

AU - Hattersley, Andrew T.

AU - Jones, Angus G.

AU - Shields, Beverley M.

AU - Dennis, John M.

AU - MASTERMIND consortium

N1 - Copyright © 2025 The Authors.

PY - 2025/11/27

Y1 - 2025/11/27

N2 - Background Routine clinical features of individual patients can potentially be used to guide selection of type 2 diabetes treatments. We aimed to evaluate a recently proposed treatment selection model predicting differences in glycaemic responses to SGLT2-inhibitors and DPP4-inhibitors across major UK ethnicity groups. Methods We externally validated the SGLT2i-DPP4i model in UK primary care cohort (CPRD Aurum, 2013-2020) independent of the original model development cohort. Non-insulin treated individuals with type 2 diabetes were identified and categorised by major UK self-reported ethnicity groups: White, Black, South Asian and Mixed/Other. For each ethnicity group, we applied a closed testing procedure to assess whether model recalibration was required. After model updates, we assessed the calibration accuracy of predicted differences in glycaemic response (6-month change in HbA1c) between SGLT2i and DPP4i for each ethnicity group. Findings SGLT2i (n=57,749) and DPP4i (n=87,807) initiations were identified amongst people of White (n=114,287; 78.5%), Black (n=6,663; 4.6%), South Asian (n=20,969; 14.4%) and Mixed/Other (n=3,637; 2.5%) ethnicities. Minor model adjustment was required to adjust for greater observed than predicted glycaemic responses to DPP4i (White -1.6 mmol/mol; Black -3.0 mmol/mol; South Asian -2.6 mmol/mol; Mixed/Other -2.6 mmol/mol). SGLT2i predictions did not require adjustment for non-White ethnicity groups. After model updates, average predicted HbA1c reduction was 3.7 mmol/mol (95%CI 3.5-3.9) greater with SGLT2i than DPP4i for those of White ethnicity; this was greater than for those of South Asian (2.1 mmol/mol (95%CI 1.6-2.6)), Black (0.6 mmol/mol (95%CI 0.5-1.7)) and Mixed/Other (2.6 mmol/mol (95%CI 1.4-3.8)) ethnicity groups. For all ethnicity groups, predicted differential glycaemic treatment effects were well-calibrated. Interpretation Our model for selection of SGLT2-inhibitor and DPP4-inhibitor therapies was accurate for all major self-reported ethnicity groups in a UK primary care cohort. Simple recalibration is beneficial to optimise performance and this is recommended prior to deployment of the model in new populations and settings.

AB - Background Routine clinical features of individual patients can potentially be used to guide selection of type 2 diabetes treatments. We aimed to evaluate a recently proposed treatment selection model predicting differences in glycaemic responses to SGLT2-inhibitors and DPP4-inhibitors across major UK ethnicity groups. Methods We externally validated the SGLT2i-DPP4i model in UK primary care cohort (CPRD Aurum, 2013-2020) independent of the original model development cohort. Non-insulin treated individuals with type 2 diabetes were identified and categorised by major UK self-reported ethnicity groups: White, Black, South Asian and Mixed/Other. For each ethnicity group, we applied a closed testing procedure to assess whether model recalibration was required. After model updates, we assessed the calibration accuracy of predicted differences in glycaemic response (6-month change in HbA1c) between SGLT2i and DPP4i for each ethnicity group. Findings SGLT2i (n=57,749) and DPP4i (n=87,807) initiations were identified amongst people of White (n=114,287; 78.5%), Black (n=6,663; 4.6%), South Asian (n=20,969; 14.4%) and Mixed/Other (n=3,637; 2.5%) ethnicities. Minor model adjustment was required to adjust for greater observed than predicted glycaemic responses to DPP4i (White -1.6 mmol/mol; Black -3.0 mmol/mol; South Asian -2.6 mmol/mol; Mixed/Other -2.6 mmol/mol). SGLT2i predictions did not require adjustment for non-White ethnicity groups. After model updates, average predicted HbA1c reduction was 3.7 mmol/mol (95%CI 3.5-3.9) greater with SGLT2i than DPP4i for those of White ethnicity; this was greater than for those of South Asian (2.1 mmol/mol (95%CI 1.6-2.6)), Black (0.6 mmol/mol (95%CI 0.5-1.7)) and Mixed/Other (2.6 mmol/mol (95%CI 1.4-3.8)) ethnicity groups. For all ethnicity groups, predicted differential glycaemic treatment effects were well-calibrated. Interpretation Our model for selection of SGLT2-inhibitor and DPP4-inhibitor therapies was accurate for all major self-reported ethnicity groups in a UK primary care cohort. Simple recalibration is beneficial to optimise performance and this is recommended prior to deployment of the model in new populations and settings.

KW - Type 2 diabetes

KW - precision medicine

KW - personalised medicine

KW - heterogeneous treatment effects

KW - ethnicity

KW - SGLT2-inhibitors

KW - DPP4-inhibitors

KW - Ethnicity

KW - Precision medicine

KW - Heterogeneous treatment effects

KW - Personalised medicine

UR - https://www.scopus.com/pages/publications/105024247523

U2 - 10.1016/j.lanepe.2025.101547

DO - 10.1016/j.lanepe.2025.101547

M3 - Article

C2 - 41399804

SN - 2666-7762

VL - 61

JO - The Lancet Regional Health - Europe

JF - The Lancet Regional Health - Europe

M1 - 101547

ER -