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Phenotype‑based targeted treatment of SGLT2 inhibitors and GLP‑1 receptor agonists in type 2 diabetes – published online 22/02/2024

Cardoso graphical abstract

Pedro Cardoso, Katie G. Young, Anand T. N. Nair, Rhian Hopkins, Andrew P. McGovern, Eram Haider, Piyumanga Karunaratne, Louise Donnelly, Bilal A. Mateen, Naveed Sattar, Rury R. Holman, Jack Bowden, Andrew T. Hattersley, Ewan R. Pearson, Angus G. Jones, Beverley M. Shields, Trevelyan J. McKinley, John M. Dennis, on behalf of the MASTERMIND consortium

A central aim of type 2 diabetes precision medicine is to accurately target specific drug treatments to the individuals most likely to benefit from them. In this issue, Cardoso et al (https://doi.org/10.1007/s00125-024-06099-3) apply cutting-edge Bayesian causal forest models to develop and validate a model to predict differences in the glycaemic efficacy of glucagon-like peptide-1 (GLP-1) receptor agonists and sodium−glucose cotransporter 2 (SGLT2) inhibitors for individuals based on their routine clinical characteristics. Using large-scale UK routine clinical data (n=46,394), the authors identify robust and clinically relevant differences in glycaemic response for many individuals. Sex is identified as a major treatment response modifier, with greater glycaemic efficacy of GLP-1 receptor agonists in females, a finding confirmed in independent trial data. Beyond glycaemia, targeting of both therapies based on predicted HbA1c response was associated with improved short-term tolerability and lower longer-term risk of new-onset microvascular complications. The authors conclude that the use of routine clinical features for type 2 diabetes treatment selection could support low-cost precision medicine worldwide.

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