Abstract
Aim To assess the individual and combined effect of 46 type 2 diabetes related risk alleles on incidence of a composite CVD endpoint.Methods Data from the first Danish MONICA study (N = 3523) and the Inter99 study (N = 6049) was used. Using Cox proportional hazard regression the individual effect of each risk allele on incident CVD was analyzed. Risk was presented as hazard ratios (HR) per risk allele.Results During 80,859 person years 1441 incident cases of CVD (fatal and non-fatal) occurred in the MONICA study. In Inter99 942 incident cases were observed during 61,239 person years.In the Danish MONICA study four gene variants were significantly associated with incident CVD independently of known diabetes status at baseline; SLC2A2 rs11920090 (HR 1.147, 95% CI 1.027–1.283 , P = 0.0154), C2CD4A rs7172432 (1.112, 1.027–1.205 , P = 0.0089), GCKR rs780094 (1.094, 1.007–1.188 , P = 0.0335) and C2CD4B rs11071657 (1.092, 1.007–1.183 , P = 0.0323). The genetic score was significantly associated with increased risk of CVD (1.025, 1.010–1.041, P = 0.0016). In Inter99 two gene variants were associated with risk of CVD independently of diabetes; SLC2A2 (HR 1.180, 95% CI 1.038–1.341 P = 0.0116) and FTO (0.909, 0.827–0.998, P = 0.0463). Analysing the two populations together we found SLC2A2 rs11920090 (HR 1.164, 95% CI 1.070–1.267, P = 0.0004) meeting the Bonferroni corrected threshold for significance. GCKR rs780094 (1.076, 1.010–1.146, P = 0.0229), C2CD4B rs11071657 (1.067, 1.003–1.135, P = 0.0385) and NOTCH2 rs10923931 (1.104 (1.001 ; 1.217 , P = 0.0481) were found associated with CVD without meeting the corrected threshold. The genetic score was significantly associated with increased risk of CVD (1.018, 1.006–1.031, P = 0.0043).Conclusions This study showed that out of the 46 genetic variants examined only the minor risk allele of SLC2A2 rs11920090 was significantly (P = 0.0005) associated with a composite endpoint of incident CVD below the threshold for statistical significance corrected for multiple testing. This potential pathway needs further exploration.
Highlights
The western world have experienced a substantial decrease in the mortality of cardiovascular disease (CVD) during the last three or four decades [1] CVD is still a leading cause of morbidity and premature mortality worldwide
This is seen in cardiovascular risk prediction where established scoring schemes such as the European SCORE or the American Framingham model uses conventional risk factors to predict future risk of CVD, but still a substantial number of events occur in the proportion of the population that is not in high risk as assessed through classical risk factors.[2,3]
In the MONICA 1 study four genetic variants were significantly associated with incident CVD independently of baseline diabetes status; minor risk allele of SLC2A2 rs11920090 (HR 1.147, 95% CI 1.027–1.283, P = 0.0154), major risk allele of C2CD4A rs7172432 (1.112, 1.027–1.205, P = 0.0089), major risk allele of GCKR rs780094 (1.094, 1.007–1.188, P = 0.0335) and major risk allele of C2CD4B rs11071657 (1.092, 1.007–1.183, P = 0.0323)
Summary
The western world have experienced a substantial decrease in the mortality of cardiovascular disease (CVD) during the last three or four decades [1] CVD is still a leading cause of morbidity and premature mortality worldwide. The decrease in mortality can be attributed to decrease in case-fatality through improved treatment but the major contribution is due to a decrease in incidence The latter is largely the result of many years of preventive efforts targeted the classical risk factors for CVD such as smoking, elevated serum cholesterol and hypertension. Even though the classical risk factors explain most of the risk associated with CVD there is still a part of the aetiology that lacks explanation This is seen in cardiovascular risk prediction where established scoring schemes such as the European SCORE or the American Framingham model uses conventional risk factors to predict future risk of CVD, but still a substantial number of events occur in the proportion of the population that is not in high risk as assessed through classical risk factors.[2,3] This has led to an increased focus on identifying new markers of risk as reflected in the long list of new biomarkers as well as exploring genetic components of CVD [2,3,4]
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