Metabolite Traits and Genetic Risk Provide Complementary Information for the Prediction of Future Type 2 Diabetes
- Geoffrey A. Walford1,2,3⇑,
- Bianca C. Porneala4,
- Marco Dauriz3,4,5,
- Jason L. Vassy3,6,7,
- Susan Cheng3,8,
- Eugene P. Rhee3,9,
- Thomas J. Wang10,
- James B. Meigs3,4,11,
- Robert E. Gerszten3,12,13 and
- Jose C. Florez1,2,3,14⇑
- 1Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA
- 2Diabetes Unit, Massachusetts General Hospital, Boston, MA
- 3Department of Medicine, Harvard Medical School, Boston, MA
- 4General Medicine Division, Massachusetts General Hospital, Boston, MA
- 5Division of Endocrinology and Metabolic Diseases, Department of Medicine, University of Verona Medical School and Hospital Trust of Verona, Verona, Italy
- 6Section of General Internal Medicine, VA Boston Healthcare System, Boston, MA
- 7Division of General Medicine and Primary Care, Brigham and Women’s Hospital, Boston, MA
- 8Cardiovascular Division, Brigham and Women’s Hospital, Boston, MA
- 9Renal Division, Massachusetts General Hospital, Boston, MA
- 10Division of Cardiology, Vanderbilt University Medical Center, Nashville, TN
- 11Framingham Heart Study of the National Heart, Lung, and Blood Institute, Framingham, MA
- 12Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA
- 13Cardiology Division, Massachusetts General Hospital, Boston, MA
- 14Program in Medical and Population Genetics, Broad Institute, Cambridge, MA
- Corresponding authors: Geoffrey A. Walford, , and Jose C. Florez, .
G.A.W. and B.C.P. contributed equally to this study. R.E.G. and J.C.F. contributed equally to this study.
OBJECTIVE A genetic risk score (GRS) comprised of single nucleotide polymorphisms (SNPs) and metabolite biomarkers have each been shown, separately, to predict incident type 2 diabetes. We tested whether genetic and metabolite markers provide complementary information for type 2 diabetes prediction and, together, improve the accuracy of prediction models containing clinical traits.
RESEARCH DESIGN AND METHODS Diabetes risk was modeled with a 62-SNP GRS, nine metabolites, and clinical traits. We fit age- and sex-adjusted logistic regression models to test the association of these sources of information, separately and jointly, with incident type 2 diabetes among 1,622 initially nondiabetic participants from the Framingham Offspring Study. The predictive capacity of each model was assessed by area under the curve (AUC).
RESULTS Two hundred and six new diabetes cases were observed during 13.5 years of follow-up. The AUC was greater for the model containing the GRS and metabolite measurements together versus GRS or metabolites alone (0.820 vs. 0.641, P < 0.0001, or 0.820 vs. 0.803, P = 0.01, respectively). Odds ratios for association of GRS or metabolites with type 2 diabetes were not attenuated in the combined model. The AUC was greater for the model containing the GRS, metabolites, and clinical traits versus clinical traits only (0.880 vs. 0.856, P = 0.002).
CONCLUSIONS Metabolite and genetic traits provide complementary information to each other for the prediction of future type 2 diabetes. These novel markers of diabetes risk modestly improve the predictive accuracy of incident type 2 diabetes based only on traditional clinical risk factors.
This article contains Supplementary Data online at http://care.diabetesjournals.org/lookup/suppl/doi:10.2337/dc14-0560/-/DC1.
- Received March 3, 2014.
- Accepted May 9, 2014.
- © 2014 by the American Diabetes Association. Readers may use this article as long as the work is properly cited, the use is educational and not for profit, and the work is not altered.