Myocardial infarction in type 2 diabetes using sodium–glucose co-transporter-2 inhibitors, dipeptidyl peptidase-4 inhibitors or glucagon-like peptide-1 receptor agonists: proportional hazards analysis by deep neural network based machine learning

Abstract

Aims: Some hypoglycemic therapies are associated with lower risk of cardiovascular outcomes. We investigated the incidence of cardiovascular disease among patients with type 2 diabetes using antidiabetic drugs from three classes, which were sodium–glucose co-transporter-2 inhibitors (SGLT-2is), glucagon-like peptide-1 receptor agonists (GLP-1RAs) and dipeptidyl peptidase-4 inhibitors (DPP-4is).

Materials and methods: We compared the risk of myocardial infarction (MI) among these drugs and developed a machine learning model for predicting MI in patients without prior heart disease. We analyzed US health plan data for patients without prior MI or insulin therapy who were aged ≥40 years at initial prescription and had not received oral antidiabetic drugs for ≥6 months previously. After developing a machine learning model to predict MI, proportional hazards analysis of MI incidence was conducted using the risk obtained with this model and the drug classes as explanatory variables.

Results: We analyzed 199,116 patients (mean age: years), comprising 110,278 (58.6) prescribed DPP-4is, 43,538 (55.1) prescribed GLP-1RAs and 45,300 (55.3) prescribed SGLT-2is. Receiver operating characteristics analysis showed higher precision of machine learning over logistic regression analysis. Proportional hazards analysis by machine learning revealed a significantly lower risk of MI with SGLT-2is or GLP-1RAs than DPP-4is (hazard ratio: 0.81, 95% confidence interval: 0.72–0.91, p = .0004 vs. 0.63, 0.56–0.72, p < .0001). MI risk was also significantly lower with GLP-1RAs than SGLT-2is (0.77, 0.66–0.90, p = .001).

Limitations: All patients analyzed were covered by US commercial health plans, so information on patients aged ≥65 years was limited and the socioeconomic background may have been biased. Also, the observation period differed among the three classes of drugs due to differing release dates.

Conclusions: Machine learning analysis suggested the risk of MI was 37% lower for type 2 diabetes patients without prior MI using GLP-1RAs versus DPP-4is, while the risk was 19% lower for SGLT-2is versus DPP-4is.

Keywords:

• Cardiovascular disease
• machine learning
• myocardial infarction
• oral antidiabetic drugs
• type 2 diabetes

Acknowledgements

None reported.

Data availability statement

All data disclosed in this manuscript was generated from a claims database for American commercial health plans (Milliman Consolidated Health Cost Guidelines Sources Database from 2011 to 2016). All information referred to in the manuscript text is disclosed in the figures, tables and supplementary tables provided.