Relationship of the p22phox (CYBA) gene polymorphism C242T with risk of coronary artery disease: a meta-analysis.

backgroundObservational 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.

methodsWe 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.

resultsThe 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, I² = 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, I² = 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, I² = 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).

conclusionTaken 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.