Abstract
Glucagon-like peptide-1 receptor agonists (GLP-1RAs) have become an essential drug class for treating type 2 diabetes, offering proven benefits in glycemic control, weight reduction, and cardiovascular and renal protection. However, growing evidence of heterogeneity in GLP-1RA treatment effects highlights the potential for developing precision medicine approaches to more accurately allocate GLP-1RAs to maximize patient benefit. In this Review, we explore the evidence for treatment effect heterogeneity with GLP-1RAs, focusing on clinical and genetic factors that robustly influence established therapeutic outcomes. We also highlight the potential of recent predictive models that integrate routine clinical data with personalize treatment decisions, comparing GLP-1RA to other major type 2 diabetes drug classes. While such models have shown considerable promise in identifying optimal type 2 diabetes treatment based on glycemic response, their utility for informing treatment choice for other clinical outcomes remains largely unexplored.