Development of a treatment selection algorithm for SGLT2 and DPP-4 inhibitor therapies in people with type 2 diabetes: a retrospective cohort study

Dennis, J. M. et al. (2022) Development of a treatment selection algorithm for SGLT2 and DPP-4 inhibitor therapies in people with type 2 diabetes: a retrospective cohort study. Lancet Digital Health, 4(12), e873-e883. (doi: 10.1016/S2589-7500(22)00174-1) (PMID:36427949)

Abstract

Background: Current treatment guidelines do not provide recommendations to support the selection of treatment for most people with type 2 diabetes. We aimed to develop and validate an algorithm to allow selection of optimal treatment based on glycaemic response, weight change, and tolerability outcomes when choosing between SGLT2 inhibitor or DPP-4 inhibitor therapies. Methods: In this retrospective cohort study, we identified patients initiating SGLT2 and DPP-4 inhibitor therapies after Jan 1, 2013, from the UK Clinical Practice Research Datalink (CPRD). We excluded those who received SGLT2 or DPP-4 inhibitors as first-line treatment or insulin at the same time, had estimated glomerular filtration rate (eGFR) of less than 45 mL/min per 1·73 m2, or did not have a valid baseline glycated haemoglobin (HbA1c) measure (<53 or ≥120 mmol/mol). The primary efficacy outcome was the HbA1c value reached 6 months after drug initiation, adjusted for baseline HbA1c. Clinical features associated with differential HbA1c outcome on the two therapies were identified in CPRD (n=26 877), and replicated in reanalysis of 14 clinical trials (n=10 414). An algorithm to predict individual-level differential HbA1c outcome on the two therapies was developed in CPRD (derivation; n=14 069) and validated in head-to-head trials (n=2499) and CPRD (independent validation; n=9376). In CPRD, we further explored heterogeneity in 6-month weight change and treatment discontinuation. Findings: Among 10 253 patients initiating SGLT2 inhibitors and 16 624 patients initiating DPP-4 inhibitors in CPRD, baseline HbA1c, age, BMI, eGFR, and alanine aminotransferase were associated with differential HbA1c outcome with SGLT2 inhibitor and DPP-4 inhibitor therapies. The median age of participants was 62·0 years (IQR 55·0–70·0). 10 016 (37·3%) were women and 16 861 (62·7%) were men. An algorithm based on these five features identified a subgroup, representing around four in ten CPRD patients, with a 5 mmol/mol or greater observed benefit with SGLT2 inhibitors in all validation cohorts (CPRD 8·8 mmol/mol [95% CI 7·8–9·8]; CANTATA-D and CANTATA-D2 trials 5·8 mmol/mol [3·9–7·7]; BI1245.20 trial 6·6 mmol/mol [2·2–11·0]). In CPRD, predicted differential HbA1c response with SGLT2 inhibitor and DPP-4 inhibitor therapies was not associated with weight change. Overall treatment discontinuation within 6 months was similar in patients predicted to have an HbA1c benefit with SGLT2 inhibitors over DPP-4 inhibitors (median 15·2% [13·2–20·3] vs 14·4% [12·9–16·7]). A smaller subgroup predicted to have greater HbA1c reduction with DPP-4 inhibitors were twice as likely to discontinue SGLT2 inhibitors than DPP-4 inhibitors (median 26·8% [23·4–31·0] vs 14·8% [12·9–16·8]). Interpretation: A validated treatment selection algorithm for SGLT2 inhibitor and DPP-4 inhibitor therapies can support decisions on optimal treatment for people with type 2 diabetes. Funding: BHF-Turing Cardiovascular Data Science Award and the UK Medical Research Council.

• Item Type: Articles
• Additional Information: This research was supported by a BHF-Turing Cardiovascular Data Science Award (SP/19/6/34809), and the Medical Research Council (UK) (MR/N00633X/1).
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Sattar, Professor Naveed
• Authors: Dennis, J. M., Young, K. G., McGovern, A. P., Mateen, B. A., Vollmer, S. J., Simpson, M. D., Henley, W. E., Holman, R. R., Sattar, N., Pearson, E. R., Hattersley, A. T., Jones, A. G., and Shields, B. M.
• College/School: College of Medical Veterinary and Life Sciences > School of Cardiovascular & Metabolic Health
• Journal Name: Lancet Digital Health
• Publisher: Lancet Publishing Group
• ISSN: 2589-7500
• ISSN (Online): 2589-7500
• Published Online: 22 November 2022
• Copyright Holders: Copyright © 2022 The Authors
• First Published: First published in Lancet Digital Health 4(12): e873-e883
• Publisher Policy: Reproduced under a Creative Commons License

Project Code	Award No	Project Name	Principal Investigator	Funder's Name	Funder Ref	Lead Dept
172300		MRC APBI STratification and Extreme Response Mechanism IN Diabetes - MASTERMIND (MRC Stratifying Medicine Pilot)	Naveed Sattar	Medical Research Council (MRC)	MR/N00633X/1	Institute of Cardiovascular & Medical Sciences