Description
Mike’s main research interest is in understanding the safety and effectiveness of novel medications for adults living with diabetes and cardiovascular disease, including sodium glucose co-transporter 2 (SGLT2) inhibitors and glucagon-like peptide 1 (GLP1) analogues. Mike splits his research time between GEMINI and LKS-CHART, integrating pharmacoepidemiology with Machine Learning in his work. He primarily uses data collected from routine care (e.g., ICES, insurance claims data), as well as the GEMINI Diabetes database. His main areas of methodologic expertise are in propensity score matching and supervised machine learning (e.g., gradient boosted trees). He recently led the creation of the knowledge translation tool SGLT2Rx.com, a freely accessible online tool that clinicians can use to weigh the risks and benefits of SGLT2 inhibitors. In August 2022, he launched the DaNGER study, a multicentre international study, to investigate whether genetic factors are associated with SGLT2 inhibitor-DKA.
