Table 5.

Associations between AdipoQ gene polymorphisms and CHD risk

SNPs	Models	OR	95%CI	P	AIC
rs3774261	Codominant	1.68	1.32–2.13	<0.0001	2058.1
		1.90	1.41–2.54	<0.0001
	Dominant	1.74	1.39–2.18	<0.0001	2056.9
	Recessive	1.37	1.07–1.77	0.014	2074.6
	Overdominant	1.29	1.06–1.59	0.013	2074.5
	Log-additive	1.40	1.21–1.62	<0.0001	2059.7
rs1063537	Codominant	0.84	0.68–1.04	0.112	2082.9
		0.88	0.59–1.32	0.531
	Dominant	0.85	0.69–1.04	0.11	2080.9
	Recessive	0.95	0.64–1.40	0.78	2083.4
	Overdominant	0.85	0.70–1.05	0.14	2081.3
	Log-additive	0.89	0.76–1.05	0.16	2081.5
rs2082940	Codominant	0.81	0.66–1.00	0.056	2078.1
		0.76	0.51–1.13	0.179
	Dominant	0.80	0.66–0.99	0.035	2076.2
	Recessive	0.83	0.57–1.23	0.360	2079.8
	Overdominant	0.84	0.68–1.03	0.1	2077.9
	Log-additive	0.84	0.72–0.99	0.04	2076.4

OR odds ratio, CI confidence interval, AIC akaike information criterion. Codominant model (homozygote for the major allele serves as the reference); Dominant model (minor allele homozygote + heterozygote vs. major allele homozygote); Recessive model (minor allele homozygote vs. major allele homozygote + heterozygote); Overdominant model (heterozygote GA vs homozygote AA + GG serve as reference); Log-additive model (compares the relative significance of the minor allele : 0,1,2 in AA, AG, and GG respectively for the G allele of rs3774261; and 0,1,2 in CC, CT, and TT respectively for the T allele of rs1063537 and rs2082940). Bold values are statistically significant.

Unconditional logistic regression was used for data analyses. Age and sex, were adjusted during analyses. Bonferroni correction value =0.008 (0.05/6 = 0.008)