Evidence mapPaperPMID 41399804Full record

ArticleThe Lancet regional health. Europe2026

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.

Laura M Güdemann, Katherine G Young, Pedro Cardoso, Bilal A Mateen, Rury R Holman, Naveed Sattar, Ewan R Pearson, Andrew T Hattersley, Angus G Jones, Beverley M Shields and 2 more

Abstract read
In one paragraph

Article in The Lancet regional health. Europe, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Laura M GüdemannClinical and Biomedical Sciences, University of Exeter Medical School, Exeter, UK.
Katherine G YoungClinical and Biomedical Sciences, University of Exeter Medical School, Exeter, UK.
Pedro CardosoClinical and Biomedical Sciences, University of Exeter Medical School, Exeter, UK.
Bilal A MateenSchool of Life Sciences, University of Birmingham, Birmingham, UK.
Rury R HolmanDiabetes Trials Unit, Radcliffe Department of Medicine, University of Oxford, Oxford, UK.
Naveed SattarSchool of Cardiovascular and Metabolic Health, University of Glasgow, Glasgow, UK.
Ewan R PearsonDivision of Diabetes, Endocrinology and Reproductive Medicine, Ninewells Hospital and Medical School, University of Dundee, Dundee, UK.
Andrew T HattersleyClinical and Biomedical Sciences, University of Exeter Medical School, Exeter, UK.
Angus G JonesClinical and Biomedical Sciences, University of Exeter Medical School, Exeter, UK.
Beverley M ShieldsClinical and Biomedical Sciences, University of Exeter Medical School, Exeter, UK.
John M DennisClinical and Biomedical Sciences, University of Exeter Medical School, Exeter, UK.
MASTERMIND Consortium

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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 = 6663; 4.6%), South Asian (n = 20,969; 14.4%) and Mixed/Other (n = 3637; 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. Funding: UK Medical Research Council, National Institute for Health and Care Research Exeter Biomedical Research Centre, and EFSD/Novo Nordisk.

Indexed as

DPP4-inhibitorsEthnicityHeterogeneous treatment effectsPersonalised medicinePrecision medicineSGLT2-inhibitorsType 2 diabetes

Identifiers

PMID41399804
PMCPMC12702077

What Socratic holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.