Abstract
AbstractBackgroundBoth sodium-glucose cotransporter-2 (SGLT2) inhibitors and GLP-1 receptor agonists (GLP1a) demonstrated benefits against cardiovascular diseases in type 2 diabetes (T2D). However, the effects of SGLT2I amongst patients already on GLP1a users remain unknown.ObjectiveThis real-world study compared the risks of cardiovascular diseases with and without exposure to SGLT2I amongst GLP1a users.MethodsThis was a retrospective population-based cohort study of patients with type-2 diabetes mellitus (T2DM) on GLP1a between 1st January 2015 and 31st December 2020 using a territory-wide registry from Hong Kong. The primary outcomes were new-onset myocardial infarction, atrial fibrillation, heart failure, and stroke/transient ischaemic attack (TIA). The secondary outcome was all-cause mortality. Propensity score matching (1:2 ratio) using the nearest neighbour search was performed. Multivariable Cox regression was used to identify significant associations. The machine learning causal inference analysis was used to estimate the treatment effect.ResultsThis cohort included 2526 T2DM patients on GLP1a (median age: 52.5 years old [SD: 10.9]; 57.34 % males). The SGLT2I users and non-SGLT2I users consisted of 1968 patients and 558 patients, respectively. After matching, non-SGLT2I users were associated with high risks of myocardial infarction (Hazard ratio [HR]: 2.91; 95% Confidence Interval [CI]: 1.30-6.59) and heart failure (HR: 2.49; 95% CI: 1.22-5.08) compared to non-SGLT2I users after adjusting for demographics, comorbidities, medications, renal function, and glycaemic tests. However, non-SGLT2I users were not associated with the risks of atrial fibrillation (HR: 1.52; 95% CI: 0.65-3.53) and stroke/TIA (HR: 1.72; 95% CI: 0.70-4.24). The results remained consistent in the competing risk and the sensitivity analyses.ConclusionsSGLT2I non-users was associated with higher risks of myocardial infarction and heart failure when compared to SGLT2I users after adjustments amongst T2DM patients on GLP1a. The result remained consistent in the machine learning causal inference analysis.Graphical abstract
Publisher
Cold Spring Harbor Laboratory