title: Combining genetic markers and clinical risk factors improves the risk assessment of impaired glucose metabolism.
abstract: BACKGROUND
  Although several candidate gene polymorphisms (SNPs) have been associated with increased risk of type 2 diabetes mellitus (T2DM), relatively few studies have assessed the ability of T2DM candidate genes to assess the risk of impaired fasting glucose (IFG), impaired glucose tolerance (IGT), and T2DM beyond the information provided by clinical risk factors.
  
  
  OBJECTIVE
  To test whether the inclusion of genetic markers in a regression model provides a better assessment of the risk of IFG, IGT, and T2DM than a model based only on non-genetic risk factors commonly assessed in clinical settings.
  
  
  METHODS
  Subjects (n = 485; 213 parents, 272 offspring) from the Quebec Family Study, not known to haveT2DM, were measured for several risk factors and underwent an oral glucose tolerance test. Thirty-eight SNPs in 25 susceptibility/ candidate genes previously reported to be associated with T2DM were genotyped. In order to identify risk factors associated with IFG/IGT/T2DM, two logistic regression models were tested: a full model (FM) including age, sex, body mass index (BMI), systolic and diastolic blood pressure, smoking status, and the 38 SNPs; and a reduced model (RM), in which the SNPs were dropped, which allowed us to test the null-hypothesis that the markers are not associated with the risk of IFG/IGT/T2DM. Performances of the models were compared by using a likelihood ratio test and the receiver-operating characteristic curves (ROC).The area under the curve (AUC) was calculated from the ROC curve.
  
  
  RESULTS
  The analyses showed that age (P < 0.0001), BMI (P < 0.0001), and six variants (IGF2BP2 rs4402960, P = 0.002; ADIPOQ+276 G>T, P = 0.004; UCP2Ala55Val, P = 0.01; CDKN2AI2B rs3731201, P = 0.02; rs495490, P = 0.02, and rsl 0811661, P = 0.03) were significantly associated with the risk of IFG/IGT/T2DM. Dropping genetic markers from the analysis significantly reduced the fit of the model to the data (chi-square = 38.98, P < 0.00001 contrasting RM to FM), suggesting that the genetic markers are significantly associated with the risk of IFG/IGT/T2DM. Furthermore, the AUC was higher for FM than for RM (0.85 (95% CI 0.81-0.89) versus 0.81 (95% CI 0.76-0.85), P = 0.004).
  
  
  CONCLUSIONS
  Our results suggest that combining genetic markers with traditional clinical risk factors has the potential to improve our ability to assess the risk of complex diseases such as T2DM.
authors.affiliation: Department of Preventive Medicine, Laval University, 2300 rue de la Terrasse, Quebec, Canada.
authors.forename: Stephanie-May
authors.initials: SM
authors.lastname: Ruchat
authors.forename: Marie-Claude
authors.initials: MC
authors.lastname: Vohl
authors.forename: S John
authors.initials: SJ
authors.lastname: Weisnagel
authors.forename: Tuomo
authors.initials: T
authors.lastname: Rankinen
authors.forename: Claude
authors.initials: C
authors.lastname: Bouchard
authors.forename: Louis
authors.initials: L
authors.lastname: Pérusse
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000483:Alleles; D015992:Body Mass Index; D003924:Diabetes Mellitus, Type 2 / Q000235:genetics*; D005260:Female; D005819:Genetic Markers; D005838:Genotype; D044882:Glucose Metabolism Disorders / Q000235:genetics*; D006801:Humans; D008297:Male; D008875:Middle Aged; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide; D011237:Predictive Value of Tests; D012372:ROC Curve; D018570:Risk Assessment; D012307:Risk Factors; D055815:Young Adult
chemical_list: D005819:Genetic Markers