Relationship of the p22phox (CYBA) Gene Polymorphism C242T with Risk of Coronary Artery Disease: A Meta-Analysis

BACKGROUND: Observational and experimental studies have thus far been unable to resolve whether the CYBA C242T polymorphism is associated with coronary artery disease (CAD). Therefore, we undertook a comprehensive meta-analysis to more precisely evaluate the influence of this polymorphism on CAD and potential biases. METHODS: We screened MEDLINE, Embase, CNKI, Wanfang and CBM up to January 2013 and extracted data from 22 studies with 9,279 CAD patients and 9,349 controls. A random-effects model was exploited to synthesize the inconsistent outcomes of the individual studies, while addressing between-study heterogeneity and publication bias. RESULTS: The CYBA C242T polymorphism conformed to Hard-Weinberg Equilibrium for all studies (P>0.05). Overall comparison of the T allele with the C allele produced a non-significant risk estimate for CAD but with striking heterogeneity (T versus C: P = 0.87, OR = 0.99, 95%CI 0.89–1.11, P(heterogeneity)<0.0001, I(2) = 67.8%). However, subgroup analysis by ethnicity documented that the T allele carriers had a marginal risk increase (21%) of CAD among Caucasians (recessive genetic model: P = 0.05, 95%CI 1.00–1.46, P(heterogeneity) = 0.15, I(2) = 29.1%). Then data were divided into study design, the significance of CAD risk increase was substantially strengthened in matched case-control studies (allele comparison: P = 0.02, OR = 1.13, 95%CI 1.02–1.26, P(heterogeneity) = 0.24, I(2) = 21.6%).Further meta-regression analysis identified that a large proportion of heterogeneity was explained by body mass index (BMI) (P = 0.03, OR = 1.07, 95%CI 1.01–1.15) and study design (P = 0.03, OR = 1.30, 95%CI 1.02–1.64).There was no obvious publication bias as verified by funnel plot and Egger's linear regression test (t = −0.25, P = 0.81 for allele comparison). CONCLUSION: Taken together, our results suggested the CYBA C242T polymorphism might be a risk-conferring factor on developing CAD and BMI and study design were probable sources of between-study heterogeneity.