An early prediction model for gestational diabetes mellitus based on metabolomic biomarkers

Table 3 Unconditional logistic regression analysis of GDM outcome according to covariates and serum concentrations of selected ACs (isovalerylcarnitine (C5) and tiglylcarnitine (C5: 1)) adjusted by covariates (age, parity, and gestational BMI)

Characteristic	β coefficient	Standard error	Wald test value	Significance	95% CI
Age per 1 year increment	0.0817	0.0586	1.40	0.163	− 0.0330–0.1966
Parity per 1 pregnancy increment	1.2923	0.3457	3.74	0.0001	0.6147–1.9700
Gestational BMI per 1 unit kg/m² increment	0.1190	0.0559	2.13	0.033	0.0093–0.2287
Age + parity + gestational BMI per 1 unit increment	0.0012	0.0003	3.77	0.0001	0.0006–0.0019
Adjusted by age, parity, gestational BMI	β coefficient	Standard error	Wald test value	Significance	95% CI
C5 (isovalerylcarnitine)per 1 µmol/L increment	24.8118	5.7870	4.29	0.0001	13.4693–36.1543
C5:1 (tiglylcarnitine) per 1 µmol/L increment	24.9211	5.8966	4.23	0.0001	13.3639–36.4783
C5 + C5:1 per 1 µmol/L increment	92.8482	25.5747	3.94	0.0001	46.6426–139.053

Bold values denote statistical significance at the p < 0.05 level
Estimated βcoefficient with Wald 95% confidence interval in covariates alone and after adjustment for covariates (age, parity, and gestational BMI) are shown for the selected metabolites previously on the Random Forest analysis (standardized serum acylcarnitines concentrations)

ISSN: 1758-5996