Advances in risk prediction of type 2 diabetes: integrating genetic scores with Framingham risk models.

Abstract

Type 2 diabetes (T2D) is a worldwide epidemic affecting 8.3% of the current global adult population (1), with a growing prevalence in both developed and developing nations. It is estimated that of 29.1 million U.S. individuals with T2D, 8.1 million (27.8%) are undiagnosed—up from 7.0 million (27.1%) in 2010. T2D is strongly heritable, with estimates ranging from 30% to 70% (2). As elevated blood glucose can precede the diagnosis of T2D by up to 10 years, irreversible T2D-related complications including neuropathies and renal disease often begin to develop before diagnosis. Lean offspring of T2D patients are more likely to be insulin resistant than lean offspring of nondiabetic subjects, which appears to be caused in part by inherited mitochondrial defects that result in reduced glucose and fatty acid oxidation efficiency, leading to lipotoxicity and fat accumulation inside of muscle cells (3). With the rapid advancement of high-throughput genotyping technologies and the aggregation and meta-anlysis of large-scale genome-wide association studies (GWAS) and gene-centric T2D studies (4–6), >70 loci have been identified, although only ∼10% of the genetic variance of T2D risk is explained by these signals. Over the last five years, the field has progressed toward integrating genetic risk scores (GRS) with conventional T2D risk scores such as the Framingham Offspring Study risk score (FORS) (7). If sufficient improvement in risk prediction can be attained with integration of GRS into conventional risk models, there may be clinical utility in earlier identification of higher-risk individuals, particularly those individuals with lower BMI or who lack the traditional nongenetic risk factors. In 2008, the first T2D GRS analysis in the Framingham Offspring Study (FOS) used an 18-SNP T2D risk score in 2,377 T2D-free participants followed for 28 years, of whom 225 (9.5%) developed T2D. …