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Combining genetic markers and clinical risk factors improves the risk assessment of impaired glucose metabolism.
Ann Med. 2010 Apr; 42(3):196-206.AM

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).

CONCLUSION

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+Show Affiliations

Department of Preventive Medicine, Laval University, 2300 rue de la Terrasse, Quebec, Canada.No affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info available

Pub Type(s)

Journal Article
Randomized Controlled Trial
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

20384434

Citation

Ruchat, Stephanie-May, et al. "Combining Genetic Markers and Clinical Risk Factors Improves the Risk Assessment of Impaired Glucose Metabolism." Annals of Medicine, vol. 42, no. 3, 2010, pp. 196-206.
Ruchat SM, Vohl MC, Weisnagel SJ, et al. Combining genetic markers and clinical risk factors improves the risk assessment of impaired glucose metabolism. Ann Med. 2010;42(3):196-206.
Ruchat, S. M., Vohl, M. C., Weisnagel, S. J., Rankinen, T., Bouchard, C., & Pérusse, L. (2010). Combining genetic markers and clinical risk factors improves the risk assessment of impaired glucose metabolism. Annals of Medicine, 42(3), 196-206. https://doi.org/10.3109/07853890903559716
Ruchat SM, et al. Combining Genetic Markers and Clinical Risk Factors Improves the Risk Assessment of Impaired Glucose Metabolism. Ann Med. 2010;42(3):196-206. PubMed PMID: 20384434.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Combining genetic markers and clinical risk factors improves the risk assessment of impaired glucose metabolism. AU - Ruchat,Stephanie-May, AU - Vohl,Marie-Claude, AU - Weisnagel,S John, AU - Rankinen,Tuomo, AU - Bouchard,Claude, AU - Pérusse,Louis, PY - 2010/4/14/entrez PY - 2010/4/14/pubmed PY - 2010/6/19/medline SP - 196 EP - 206 JF - Annals of medicine JO - Ann. Med. VL - 42 IS - 3 N2 - 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). CONCLUSION: 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. SN - 1365-2060 UR - https://www.unboundmedicine.com/medline/citation/20384434/Combining_genetic_markers_and_clinical_risk_factors_improves_the_risk_assessment_of_impaired_glucose_metabolism_ L2 - http://www.tandfonline.com/doi/full/10.3109/07853890903559716 DB - PRIME DP - Unbound Medicine ER -