Table 2.

The relationship of GDM with offspring’s CIMT at birth

	Mean (SD), mm	Model 1 (n=200)		Model 2 (n=165)		Model 3 (n=165)
		Mean difference (95% CI), mm	P value	Mean difference (95% CI), mm	P value	Mean difference (95% CI), mm	P value
Non-GDM	0.30 (0.04)	Ref	Ref	Ref	Ref	Ref	Ref
GDM	0.30 (0.04)	0.00 (–0.01 to 0.01)	0.96	0.00 (–0.02 to 0.01)	0.47	0.00 (–0.02 to 0.01)	0.45

Estimates were obtained from linear regression models with the following specification: Model 1: unadjusted estimates; Model 2: estimates adjusted for maternal prepregnancy BMI, education and tobacco smoking; offspring family history of diabetes and sex; Model 3: estimates adjusted for maternal prepregnancy BMI, education and tobacco smoking; offspring family history of diabetes, sex, body surface area and age at CIMT assessment. The outcome variable (ie, CIMT) was continuous. The exposure variable was binary (GDM/non-GDM; the reference category was non-GDM). Similar results were obtained when Model 1 was run in the sample (n=165) with data on outcome, exposure and all covariates included in Model 2 and Model 3 (GDM: 0.00 mm (95% CI −0.02 to 0.01; p=0.54)).

BMI, body mass index; CI, confidence interval; CIMT, carotid intima–media thickness; GDM, gestational diabetes mellitus; n, number of participants; Ref, reference group; SD, standard deviation.