Future trials could instead test specific precision medicine algorithms based on multiple factors (potentially both clinical and genetic features), to test whether use of an algorithm results in improved outcomes for patients. using routinely available clinical features. This Perspective reviews this current evidence and discusses how differences in drug response could inform selection of ARHGDIB optimal type 2 diabetes treatment in the near future. It presents a novel framework for developing and screening precision medicineCbased strategies to enhance treatment, harnessing existing routine clinical and trial data sources. This framework was recently applied to demonstrate that subtype methods, in which people are classified into subgroups based on features reflecting underlying pathophysiology, are likely to have less clinical utility compared with methods that combine the same features as continuous steps in probabilistic individualized prediction models. Introduction Type 2 diabetes is usually a complex disease, characterized by hyperglycemia associated with varying degrees of insulin resistance and impaired insulin secretion and influenced by nongenetic and genetic factors. Despite this, glucose-lowering treatment is similar for most people. Current type 2 diabetes guidelines recommend the choice between glucose-lowering treatment options is based on clinical characteristics (1), an approach in line with the central goal of precision medicine: the tailoring of medical treatment Enasidenib to an individual. After initial metformin, the most recent guidelines recommend glucagon-like peptide 1 receptor agonists (GLP-1RA) or sodiumCglucose cotransporter 2 inhibitors (SGLT2i) in people with established atherosclerotic cardiovascular disease, heart failure, or chronic kidney disease, but this Enasidenib stratification only applies to up to 15C20% of people with type 2 diabetes (2,3). For the remaining majority, evidence of benefit beyond glucose lowering with these drug classes has not been robustly exhibited, and the optimal treatment pathway is not clear (1). Evidence on the key considerations, notably glucose-lowering efficacy, tolerability, and side effects, is usually mainly derived from average treatment effects from clinical trials. This means there is little information available on whether a specific person in the medical center is more or less likely than the average trial participant to respond well to a particular treatment or develop side effects. Given this knowledge gap, there is currently great desire for developing approaches that can characterize people beyond the standard type 2 diabetes phenotype and use this heterogeneity to optimize the selection of glucose-lowering treatment. Any successful implementation of precision medicine in type 2 diabetes is likely to be very different from your most successful examples of precision medicine to date. These have been in malignancy and single-gene diseases such as monogenic diabetes, where expensive genetic screening defines the etiology and the specific etiology helps to determine treatment (4,5). In type 2 diabetes, unlike malignancy, tissue is not available, and unlike rare forms of diabetes, current genetic testing does not allow clear definition of the underlying pathophysiology (6). This makes identification of discrete, nonoverlapping subtypes of type 2 diabetes much less likely (7). In this Perspective, I focus on a fundamental aim Enasidenib of precision medicinethe selection of optimal type 2 diabetes treatment based on likely differences in drug effect (henceforth, heterogeneity of treatment effect [HTE]). I provide an overview of the evidence from recent studies of HTE in type 2 diabetes and present a framework for using existing program clinical Enasidenib and trial data sources to develop and test precision medicineCbased strategies to optimize treatment. The focus is usually on glycemic response, as nearly all current evidence of HTE for diabetes drugs is for differences in HbA1c. However, the framework layed out can easily be extended to evaluate HTE for nonglycemic end points, including microvascular and macrovascular complications. Type 2 diabetes is usually a highly prevalent condition with relatively inexpensive treatment, meaning precision medicine approaches based on inexpensive markers have best potential to translate into clinical practice in the near future. As a result, this article concentrates on the use of routinely.