Across India, there has been a documented increase in the prevalence of metabolic disease including diabetes dyslipidemia and hypertension particularly in urban regions. A subset of these cases falls into a category known as lean diabetes in which diabetic patients present without concurrent obesity. In India in particular lean diabetes is on the rise, accounting for up to 50 percent of all diabetes cases in certain regions^. While there are numerous treatments for typical type 2 diabetes many patients do not achieve full remission on these medications, even with significant weight loss. This may be a product of their specific presentation or duration of diabetes that is decreased beta cell function and underlying genetic factors. Given this there is a pressing need to identify novel diabetes therapeutic targets which influence disease beyond obesity-related risk factors and may present an additional option for patients to achieve diabetic remission. While both diabetes generally and lean diabetes specifically, are known to be highly prevalent in India, few studies have explored the underlying cause of this heightened prevalence in detail. We hypothesize that lean diabetes in India has a distinct genetic architecture from non-lean diabetes both of which may be driven in part by population-enriched genetic variation which is rare or absent in other global diabetes cohorts. Through the approach detailed here we expect to uncover novel genes and genomic mechanisms influencing diabetes and related traits in India. Thus the purpose of this study is to map the genetic architecture and molecular signatures of lean with a BMI less than 23 and non lean with a BMI greater than or equal to 23 with type 2 diabetes and related metabolic traits in India. To do so we plan to recruit up to 10000 participants total, with up to 3500 participants who are diagnosed with lean diabetes up to 3500 participants diagnosed with non lean diabetes and up to 3000 individuals who do not have type 2 diabetes. Samples collected from these participants will be used to generate whole genome sequencing data along with comprehensive physiological and molecular phenotyping and health history. This phenotypic data paired with genetics will allow us to not only explore variants associated with disease risk, but also understand the mechanism by which they influence disease traits.