Genetic Screening for the Risk of Type 2 Diabetes: Worthless or valuable?

VALERIYA LYSSENKO PHD 1 2 MARKKU LAAKSO PHD 0 0 Department of Medicine, University of Eastern Finland and Kuopio University Hospital , Kuopio , Finland 1 Steno Diabetes Center , Gentofte, Denmark; and the 2 Department of Clinical Sciences, Diabetes and Endocrinology, Lund University , Malmo , Sweden - T 2 diabetes, representing .90% of all he prevalence and incidence of type cases of diabetes, are increasing rapidly throughout the world. The International Diabetes Federation has estimated that the number of people with diabetes is expected to rise from 366 million in 2011 to 552 million by 2030 if no urgent action is taken. Furthermore, as many as 183 million people are unaware that they have diabetes (www.idf.org). Therefore, the identification of individuals at high risk of developing diabetes is of great importance and interest for investigators and health care providers. Type 2 diabetes is a complex disorder resulting from an interaction between genes and environment. Several risk factors for type 2 diabetes have been identified, including age, sex, obesity and central obesity, low physical activity, smoking, diet including low amount of fiber and high amount of saturated fat, ethnicity, family history, history of gestational diabetes mellitus, history of the nondiabetic elevation of fasting or 2-h glucose, elevated blood pressure, dyslipidemia, and different drug treatments (diuretics, unselected b-blockers, etc.) (13). There is also ample evidence that type 2 diabetes has a strong genetic basis. The concordance of type 2 diabetes in monozygotic twins is ~70% compared with 20 30% in dizygotic twins (4). The lifetime risk of developing the disease is ~40% in offspring of one parent with type 2 diabetes, greater if the mother is affected (5), and approaching 70% if both parents have diabetes. In prospective studies, we have demonstrated that first-degree family history is associated with twofold increased risk of future type 2 diabetes (1,6). The challenge has been to find genetic markers that explain the excess risk associated with family history of diabetes. Advances in genotyping technology during the last 5 years have facilitated rapid progress in large-scale genetic studies. Since 2007, genome-wide association studies (GWAS) have identified .65 genetic variants that increase the risk of type 2 diabetes by 1030% (7,8). Most of these variants are noncoding variants, and therefore their functional consequences are challenging to investigate. Many of the variants identified to date regulate insulin secretion and not insulin action in insulin-sensitive tissues. In a review by Noble et al. (3), a total of 43 different studies were presented where nongenetic prediction models for type 2 diabetes, including known risk factors for type 2 diabetes with different combinations, had been analyzed. Heterogeneity of data and highly variable methodology of primary studies precluded meta-analysis. Altogether, 84 different risk prediction models were presented in 43 studies. C statistics varied from 0.60 to 0.91 (from 0.60 to 0.69 in 5 c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c models, from 0.70 to 0.79 in 44 models, from 0.80 to 0.89 in 32 models, and $0.90 in 3 models). These results indicate that clinical, laboratory, and other easily collected information by interview constitutes in most cases a solid basis for nongenetic prediction models in type 2 diabetes. Identification of a large number of novel genetic variants increasing susceptibility to type 2 diabetes and related traits opened up opportunity, not existing thus far, to translate this genetic information to the clinical practice and possibly improve risk prediction. However, available data to date do not yet provide convincing evidence to support use of genetic screening for the prediction of type 2 diabetes. In this review, we summarize the current evidence on the role of genetic variants to predict type 2 diabetes above and beyond nongenetic factors and discuss the limitations and future potential of genetic studies. Genetic prediction models for type 2 diabetes: evidence from cross-sectional and longitudinal studies Several studies have indicated that different genetic variants (single nucleotide polymorphisms [SNPs]) are associated with type 2 diabetes. Genetic risk models for type 2 diabetes, based on both crosssectional (917) and longitudinal (1,17 24) studies, are summarized in Table 1. Cross-sectional studies. In crosssectional studies including 3,0009,000 individuals with and without type 2 diabetes, the discriminatory ability of the combined SNP information has been assessed by grouping individuals based on the number of risk alleles and determining relative odds of type 2 diabetes, as well as by calculating the area under the receiver operating characteristic curve (AUC). As shown in Table 1, the AUC of the genetic risk score (GRS), which combines the information from all risk variants included in the study, has ranged from 0.54 to 0.63, indicating that genetic factors have limited use in predicting an individuals risk of the disease. In contrast, the AUC has been considerably Table 1dComparison of clinical and genetic prediction models for type 2 diabetes M, men; NA, information not available; W, women. larger (from 0.61 to 0.95) for clinical models including different combinations of clinical and laboratory parameters (age, sex, and BMI in all models and family history of diabetes and fasting glucose in most of the models) predicting the risk of type 2 diabetes. Adding the GRS in the same model shows that in addition to clinical and laboratory parameters, risk variants increase only minimally the predictive value at the population level, although the model improvement could be statistically significant (P , 0.05) in some cases. Perhaps the most important clinical question in cross-sectional studies is trying to identify undiagnosed individuals with type 2 diabetes. We addressed this question in our large population-based Metabolic Syndrome in Men (METSIM) Study (16). We identified undiagnosed type 2 diabetic patients using the Finnish Diabetes Risk Score alone (25), which was the best single indicator of prevalent undiagnosed diabetes among all variables tested in our study. The AUC based on logistic regression models for the identification of previously undiagnosed type 2 diabetic subjects with the Finnish AUC clinical model AUC GRS plus clinical model Diabetes Risk Score alone was 0.727, and it was 0.772 after adding total triglycerides, HDL cholesterol, adiponectin, and alanine transaminase in the model. Adding type 2 diabetes risk alleles (20 SNPs) did not further improve the model (0.772) (16). Therefore, in our study common genetic variants did not seem to add any information on the identification of people having undiagnosed diabetes. Longitudinal studies. Longitudinal studies can address the question of what the nongenetic (...truncated)