Relationships of SLC2A4, RBP4, PCK1, and PI3K Gene Polymorphisms with Gestational Diabetes Mellitus in a Chinese Population

Table 5

The association of genetic variants of SLC2A4, RBP4, PCK1, and PIK3R1 with OGTT, fasting insulin, and HOMA-IR levels.

Gene	SNP	Genetic model	Fasting BG^#		1 h BG^#		2 h BG^#		Fasting insulin		HOMA-IR
			Beta	p	Beta	p	Beta	p	Beta	p	Beta	p
SLC2A4	rs5435	Recessive model	0.171	0.042	0.338	0.180	0.287	0.169	3.166	0.002	0.879	<0.001
RBP4	rs7091052	Dominance model	0.109	0.195	0.542	0.030	0.004	0.987	-0.060	0.927	0.025	0.890
PCK1	rs1042531	Recessive model	0.176	0.133	0.455	0.185	0.428	0.134	5.443	<0.001	1.485	<0.001
		Additive model	0.052	0.271	0.089	0.520	0.139	0.230	1.228	0.030	0.341	0.014
PCK1	rs2236745	Dominance model	0.040	0.515	-0.056	0.755	-0.002	0.989	1.571	0.032	0.352	0.051
PIK3R1	rs34309	Recessive model	-0.222	0.011	-0.701	0.007	-0.395	0.069	0.250	0.813	-0.088	0.735

The covariates in these linear regression analyses were maternal age, prepregnancy BMI, and weekly BMI growth. BG^# is the abbreviation for blood glucose.p value was less than 0.004, which was α after Bonferroni correction (α=0.05/14=0.004); fourteen SNPs were included in the analyses. § meant that the p value was less than 0.001, which was α after stringent Bonferroni correction (α=0.05/(143)=0.001); three genetic models of each SNP have been analysed.