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Genetic Risk Score Helps to Predict Future Major Cardiac Events in Type 2 Diabetes Patients

NEW YORK (GenomeWeb) – An international team of researchers has developed a genetic risk score that significantly improves the ability of clinicians to predict future major coronary artery disease events (MCE) in type 2 diabetes patients.

As they reported this week in the journal Diabetes Care, the researchers set out to evaluate whether the increasing number of published genetic loci for coronary artery disease (CAD) identified in the general population could be used to predict the risk of MCE among patients with type 2 diabetes, who are at high cardiovascular risk.

The risk of CAD is between two and five times higher in patients with type 2 diabetes than in the general population, making CAD the most common long-term complication of type 2 diabetes, the researchers noted.

They calculated a weighted genetic risk score (GRS) derived from 204 variants representative of the 160 CAD loci identified in the general population as of December 2017 in 5,360 and 1,931 participants of European ancestry in the Action to Control Cardiovascular Risk in Diabetes (ACCORD) and Outcome Reduction With Initial Glargine Intervention (ORIGIN) studies, respectively.

The ACCORD trial was aimed at evaluating whether intensive treatments targeting hyperglycemia, elevated blood pressure, and dyslipidemia could reduce the risk of major adverse cardiovascular events among patients with high-risk type 2 diabetes. The ORIGIN trial investigated the effect of titrated basal insulin versus standard care and of n-3 fatty acid supplements versus placebo on the occurrence of major cardiovascular events among 12,537 participants with impaired fasting glucose, impaired glucose tolerance or type 2 diabetes, and high cardiovascular risk.

For ACCORD, 67 of the 204 SNPs considered in the study were genotyped and the remaining 137 imputed at high quality. For ORIGIN, 64 of the 204 SNPs were genotyped and 138 were imputed at high quality. The researchers then derived a weighted GRS from the genotypes by adding the numbers of risk alleles at each variant after weighing them for the effect magnitudes reported for each variant in the literature. They then assessed the association between GRS and MCE using Cox proportional hazards regression.

The researchers found that the GRS was associated with MCE risk in both the ACCORD and ORIGIN cohorts, and that this association was independent from the medical interventions tested in the trials and persisted after adjustment for classic cardiovascular risk predictors. Further, adding the GRS to clinical predictors improved incident MCE risk classification, and the performance of the researchers' GRS was superior to that of GRS based on the smaller number of CAD loci available in previous years.

"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," the authors wrote.

A total of 675 participants enrolled in the ACCORD study suffered one or more MCE over a median follow-up of 4.7 years. The GRS was significantly associated with the risk of experiencing one of these MCE, the team noted, adding that these results were then replicated in the ORIGIN cohort.

"In this study, we found that a GRS capturing the information provided by all the CAD loci identified to date in the general population was strongly associated with the prevalence and incidence of CAD also among subjects with type 2 diabetes. This association was independent of classical clinical cardiovascular risk factors, family history of cardiovascular disease, previous history of CAD, and duration of diabetes and did not influence the effectiveness of interventions aimed at decreasing cardiovascular morbidity such as intensive glycemic and blood pressure control and lipid-lowering treatment with fenofibrate," the authors concluded.

They also noted that while clinical risk factors still outperformed the GRS as predictors of incident MCE, the addition of the GRS to a clinical prediction model provided a modest but significant improvement in discriminating participants who developed events from those who did not. "Such improvement depended on the number of loci captured by the GRS, with the most updated list of CAD loci clearly outperforming GRS based on the smaller number of CAD loci available in previous years," they added.