Advancing personalized medicine in type 2 diabetes through better prediction of drug efficacy

Current clinical practice guidelines for patients with type 2 diabetes (T2DM) are primarily based on evidence from clinical trials and population-based studies. These guidelines emphasized the importance of improving glycemic control and controlling other risk factors, such as blood pressure and cholesterol level, to reduce the risk of micro- and macrovascular complications.

However, the guidelines do not account for individual variability in response to drugs used to manage these risk factors. This is problematic since many patients do not respond adequately to drugs used to manage kidney and cardiovascular complications. As a result, many patients with T2DM do not respond adequately to these drugs and are unnecessarily exposed to potential side effects. Therefore, the development of personalized treatment strategies that consider individual variability is crucial to improving outcomes in T2DM care.

In this thesis, we demonstrated the importance of targeting multiple risk factors as part of comprehensive risk management in T2DM. We validated the multiple Parameter Response Efficacy (PRE) score, which predicts the long-term drug effects on kidney and cardiovascular endpoints, based on short-term drug-induced changes on multiple risk markers. This score could be implemented in clinical trials to predict the long-term effect of drugs on clinical outcomes at a population level. If further validated, the score may predict the drug effect at an individual level. Finally, at an individual level, we also showed a multivariable risk-based approach outperformed the treatment strategy using a single surrogate (e.g., HbA1c or UACR) to guide the treatment initiation of a sodium-glucose co-transporter-2 inhibitor.