Genetic Tools for Coronary Risk Assessment in Type 2 Diabetes: A Cohort Study From the ACCORD Clinical Trial

Mario Luca Morieri, He Gao, Marie Pigeyre, Hetal S Shah, Jennifer Sjaarda, Christine Mendonca, Timothy Hastings, Patinut Buranasupkajorn, Alison A Motsinger-Reif, Daniel M Rotroff, Ronald J Sigal, Santica M Marcovina, Peter Kraft, John B Buse, Michael J Wagner, Hertzel C Gerstein, Josyf C Mychaleckyj, Guillaume Parè, Alessandro Doria, Mario Luca Morieri, He Gao, Marie Pigeyre, Hetal S Shah, Jennifer Sjaarda, Christine Mendonca, Timothy Hastings, Patinut Buranasupkajorn, Alison A Motsinger-Reif, Daniel M Rotroff, Ronald J Sigal, Santica M Marcovina, Peter Kraft, John B Buse, Michael J Wagner, Hertzel C Gerstein, Josyf C Mychaleckyj, Guillaume Parè, Alessandro Doria

Abstract

Objective: We evaluated whether the increasing number of genetic loci for coronary artery disease (CAD) identified in the general population could be used to predict the risk of major CAD events (MCE) among participants with type 2 diabetes at high cardiovascular risk.

Research design and methods: A weighted genetic risk score (GRS) derived from 204 variants representative of all the 160 CAD loci identified in the general population as of December 2017 was calculated in 5,360 and 1,931 white participants in the Action to Control Cardiovascular Risk in Diabetes (ACCORD) and Outcome Reduction With Initial Glargine Intervention (ORIGIN) studies, respectively. The association between GRS and MCE (combining fatal CAD events, nonfatal myocardial infarction, and unstable angina) was assessed by Cox proportional hazards regression.

Results: The GRS was associated with MCE risk in both ACCORD and ORIGIN (hazard ratio [HR] per SD 1.27, 95% CI 1.18-1.37, P = 4 × 10-10, and HR per SD 1.35, 95% CI 1.16-1.58, P = 2 × 10-4, respectively). This association was independent from interventions tested in the trials and persisted, though attenuated, after adjustment for classic cardiovascular risk predictors. Adding the GRS to clinical predictors improved incident MCE risk classification (relative integrated discrimination improvement +8%, P = 7 × 10-4). The performance of this GRS was superior to that of GRS based on the smaller number of CAD loci available in previous years.

Conclusions: When combined into a GRS, CAD loci identified in the general population are associated with CAD also in type 2 diabetes. This GRS provides a significant improvement in the ability to correctly predict future MCE, which may increase further with the discovery of new CAD loci.

Trial registration: ClinicalTrials.gov NCT00000620 NCT00069784.

© 2018 by the American Diabetes Association.

Figures

Figure 1
Figure 1
Kaplan-Meier curves for MCE according to genetic risk category.
Figure 2
Figure 2
A: ROC curves for the predictive performance of MCE using GRS (green), clinical predictors (blue), or the combination of them (red) in the ACCORD trial (5,322 subjects included in the analysis; 667 events). Clinical predictors include history of CAD, ASCVD risk score, age, sex, and ACCORD study covariates. B: Association with MCE of different GRS based on the increasing number of CAD SNPs. GRS are ordered from top to bottom in chronological order (years are reported in brackets), with an increasing number of CAD loci for each GRS. C and D: Improvement in discrimination for MCE with GRS from 2010 to 2017 (plotted against year of discovery in C and against the number of CAD loci included in each GRS in D). The y-axis shows the percent increase in rIDI when each GRS was added to the model including clinical predictors.

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Source: PubMed

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