Advances in Risk Prediction of Type 2 Diabetes: Integrating Genetic Scores With Framingham Risk Models

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 metaanlysis 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. They found a modest but significant 12% relative risk increase of T2D incidence per risk allele. Irrespective of FORS though, individuals possessing the highest GRS (.21 genotype score) compared with those with the lowest GRS (,15 genotype score) had increased T2D risk (odds ratio [OR] 2.6) (8). Concurrently, a Scandinavian study took a similar approach using a 16-SNP risk model and similarly found that the GRS had a slight improvement for incident T2D prediction when compared with a score of clinical risk factors alone (9). A 2011 study by de Miguel-Yanes et al. (10) used longer follow-up periods of incident T2D outcomes in FOS, where 446 individuals (12.8%) developed T2D over a period of 34 years, and used a 40-SNP risk score to show utility for T2D risk prediction in those aged ,50 years but not in older individuals. Vassy et al. (11) recently reanalyzed the same FOS data from the de Miguel-Yanes 2011 study (10), together with a smaller data set from younger Coronary Artery Risk Development in Young Adults (CARDIA) subjects, but using an updated 62-SNP model, also showed a modest increase in the added value of T2D incident prediction. This suggests that GRS may be more useful in younger individuals, fitting the biological model that diseases that affect young individuals are more likely to have stronger genetic determinants. In this issue of Diabetes, a new article by Talmud et al. (12) employed a weighted T2D GRS using 65 variants derived from recent large-scale T2D association metaanalyses to examine the impact on T2D risk assessment in seven prospective studies from the UK-based UCLEB (University College London-London School of Hygiene and Tropical Medicine-Edinburgh-Bristol) Consortium of prospective studies. Of 13,294 individuals who were initially free from T2D, 804 developed T2D over a median of 10 years’ follow-up. Talmud et al. (12)