The Value of Genetic Information for Diabetes Risk Prediction – Differences According to Sex, Age, Family History and Obesity

Background Genome-wide association studies have identified numerous single nucleotide polymorphisms associated with type 2 diabetes through the past years. In previous studies, the usefulness of these genetic markers for prediction of diabetes was found to be limited. However, differences may exist between substrata of the population according to the presence of major diabetes risk factors. This study aimed to investigate the added predictive value of genetic information (42 single nucleotide polymorphisms) in subgroups of sex, age, family history of diabetes, and obesity. Methods A case-cohort study (random subcohort N = 1,968; incident cases: N = 578) within the European Prospective Investigation into Cancer and Nutrition Potsdam study was used. Prediction models without and with genetic information were evaluated in terms of the area under the receiver operating characteristic curve and the integrated discrimination improvement. Stratified analyses included subgroups of sex, age (<50 or ≥50 years), family history (positive if either father or mother or a sibling has/had diabetes), and obesity (BMI< or ≥30 kg/m2). Results A genetic risk score did not improve prediction above classic and metabolic markers, but – compared to a non-invasive prediction model – genetic information slightly improved the area under the receiver operating characteristic curve (difference [95%-CI]: 0.007 [0.002–0.011]). Stratified analyses showed stronger improvement in the older age group (0.010 [0.002–0.018]), the group with a positive family history (0.012 [0.000–0.023]) and among obese participants (0.015 [−0.005–0.034]) compared to the younger participants (0.005 [−0.004–0.014]), participants with a negative family history (0.003 [−0.001–0.008]) and non-obese (0.007 [0.000–0.014]), respectively. No difference was found between men and women. Conclusion There was no incremental value of genetic information compared to standard non-invasive and metabolic markers. Our study suggests that inclusion of genetic variants in diabetes risk prediction might be useful for subgroups with already manifest risk factors such as older age, a positive family history and obesity.

[1]  A. Hofman,et al.  American Journal of Epidemiology Practice of Epidemiology Improvement of Risk Prediction by Genomic Profiling: Reclassification Measures versus the Area under the Receiver Operating Characteristic Curve , 2022 .

[2]  R. D'Agostino,et al.  Genotype score in addition to common risk factors for prediction of type 2 diabetes. , 2008, The New England journal of medicine.

[3]  Ewout W Steyerberg,et al.  Predictive testing for complex diseases using multiple genes: Fact or fiction? , 2006, Genetics in Medicine.

[4]  Simon J. Griffin,et al.  Risk Assessment Tools for Identifying Individuals at Risk of Developing Type 2 Diabetes , 2011, Epidemiologic reviews.

[5]  H. Boeing,et al.  Reproducibility and relative validity of energy and macronutrient intake of a food frequency questionnaire developed for the German part of the EPIC project. European Prospective Investigation into Cancer and Nutrition. , 1997, International journal of epidemiology.

[6]  M. Schulze,et al.  Plasma Fetuin-A Levels and the Risk of Type 2 Diabetes , 2008, Diabetes.

[7]  Ayellet V. Segrè,et al.  Twelve type 2 diabetes susceptibility loci identified through large-scale association analysis , 2010, Nature Genetics.

[8]  H. Aberg,et al.  Oral contraceptives and back pain in women in a Swedish community. , 1997, International journal of epidemiology.

[9]  P. Franks.  Genetic risk scores ascertained in early adulthood and the prediction of type 2 diabetes later in life , 2012, Diabetologia.

[10]  Sara M. Willems,et al.  A Methodological Perspective on Genetic Risk Prediction Studies in Type 2 Diabetes: Recommendations for Future Research , 2011, Current diabetes reports.

[11]  Ewout W Steyerberg,et al.  Extensions of net reclassification improvement calculations to measure usefulness of new biomarkers , 2011, Statistics in medicine.

[12]  Tianxi Cai,et al.  Joint Effects of Common Genetic Variants on the Risk for Type 2 Diabetes in U.S. Men and Women of European Ancestry , 2009, Annals of Internal Medicine.

[13]  E. DeLong,et al.  Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. , 1988, Biometrics.

[14]  Richard W. Grant,et al.  Genetic Risk Reclassification for Type 2 Diabetes by Age Below or Above 50 Years Using 40 Type 2 Diabetes Risk Single Nucleotide Polymorphisms , 2010, Diabetes Care.

[15]  H Boeing,et al.  Reproducibility and relative validity of food group intake in a food frequency questionnaire developed for the German part of the EPIC project. European Prospective Investigation into Cancer and Nutrition. , 1997 .

[16]  M. Pencina,et al.  Evaluating the added predictive ability of a new marker: From area under the ROC curve to reclassification and beyond , 2008, Statistics in medicine.

[17]  L. Groop,et al.  Genetic Prediction of Future Type 2 Diabetes , 2005, PLoS medicine.

[18]  Tanya M. Teslovich,et al.  Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes , 2012, Nature Genetics.

[19]  Marcia M. Nizzari,et al.  Genome-Wide Association Analysis Identifies Loci for Type 2 Diabetes and Triglyceride Levels , 2007, Science.

[20]  M. Fornage,et al.  A genotype risk score predicts type 2 diabetes from young adulthood: the CARDIA study , 2012, Diabetologia.

[21]  H. Boeing,et al.  Recruitment procedures of EPIC-Germany. European Investigation into Cancer and Nutrition. , 1999, Annals of nutrition & metabolism.

[22]  Heiner Boeing,et al.  Use of Multiple Metabolic and Genetic Markers to Improve the Prediction of Type 2 Diabetes: the EPIC-Potsdam Study , 2009, Diabetes Care.

[23]  M. Schulze,et al.  Assessing improvement in disease prediction using net reclassification improvement: impact of risk cut-offs and number of risk categories , 2012, European Journal of Epidemiology.

[24]  H. Boeing,et al.  Recruitment Procedures of EPIC-Germany , 1999, Annals of Nutrition and Metabolism.

[25]  K. Narayan,et al.  Clinical risk factors, DNA variants, and the development of type 2 diabetes. , 2009, The New England journal of medicine.

[26]  A. Cecile J.W. Janssens,et al.  Predicting Type 2 Diabetes Based on Polymorphisms From Genome-Wide Association Studies , 2008, Diabetes.

[27]  S. Humphries,et al.  Utility of genetic and non-genetic risk factors in prediction of type 2 diabetes: Whitehall II prospective cohort study , 2010, BMJ : British Medical Journal.

[28]  Jean Tichet,et al.  Predicting Diabetes: Clinical, Biological, and Genetic Approaches , 2008, Diabetes Care.

[29]  Kurt Hoffmann,et al.  An Accurate Risk Score Based on Anthropometric, Dietary, and Lifestyle Factors to Predict the Development of Type 2 Diabetes , 2007, Diabetes Care.

[30]  H Boeing,et al.  Validation of a self-administered food-frequency questionnaire administered in the European Prospective Investigation into Cancer and Nutrition (EPIC) Study: comparison of energy, protein, and macronutrient intakes estimated with the doubly labeled water, urinary nitrogen, and repeated 24-h dietary , 1999, The American journal of clinical nutrition.

[31]  N J Wareham,et al.  The link between family history and risk of type 2 diabetes is not explained by anthropometric, lifestyle or genetic risk factors: the EPIC-InterAct study , 2012, Diabetologia.