A prospective study of associations between in utero exposure to gestational diabetes mellitus and metabolomic profiles during late childhood and adolescence.

AIMS/HYPOTHESIS: This study aimed to: (1) identify metabolite patterns during late childhood that differ with respect to exposure to maternal gestational diabetes mellitus (GDM); (2) examine the persistence of GDM/metabolite associations 5 years later, during adolescence; and (3) investigate the associations of metabolite patterns with adiposity and metabolic biomarkers from childhood through adolescence. METHODS: This study included 592 mother-child pairs with information on GDM exposure (n = 92 exposed), untargeted metabolomics data at age 6-14 years (T1) and at 12-19 years (T2), and information on adiposity and metabolic risk biomarkers at T1 and T2. We first consolidated 767 metabolites at T1 into factors (metabolite patterns) via principal component analysis (PCA) and used multivariable regression to identify factors that differed by GDM exposure, at α = 0.05. We then examined associations of GDM with individual metabolites within factors of interest at T1 and T2, and investigated associations of GDM-related factors at T1 with adiposity and metabolic risk throughout T1 and T2 using mixed-effects linear regression models. RESULTS: Of the six factors retained from PCA, GDM exposure was associated with greater odds of being in quartile (Q)4 (vs Q1-3) of 'Factor 4' at T1 after accounting for age, sex, race/ethnicity, maternal smoking habits during pregnancy, Tanner stage, physical activity and total energy intake, at α = 0.05 (OR 1.78 [95% CI 1.04, 3.04]; p = 0.04). This metabolite pattern comprised phosphatidylcholines, diacylglycerols and phosphatidylethanolamines. GDM was consistently associated with elevations in a subset of individual compounds within this pattern at T1 and T2. While this metabolite pattern was not related to the health outcomes in boys, it corresponded with greater adiposity and a worse metabolic profile among girls throughout the follow-up period. Each 1-unit increment in Factor 4 corresponded with 0.17 (0.08, 0.25) units higher BMI z score, 8.83 (5.07, 12.59) pmol/l higher fasting insulin, 0.28 (0.13, 0.43) units higher HOMA-IR, and 4.73 (2.15, 7.31) nmol/l higher leptin. CONCLUSIONS/INTERPRETATION: Exposure to maternal GDM was nominally associated with a metabolite pattern characterised by elevated serum phospholipids in late childhood and adolescence at α = 0.05. This metabolite pattern was associated with greater adiposity and metabolic risk among female offspring throughout the late childhood-to-adolescence transition. Future studies are warranted to confirm our findings.

Sex-Specific Metabolite Biomarkers of NAFLD in Youth: A Prospective Study in the EPOCH Cohort.

CONTEXT: Nonalcoholic fatty liver disease (NAFLD) is the leading cause of chronic liver disease in developed nations. There are currently no accurate biomarkers of NAFLD risk in youth. OBJECTIVE: Identify sex-specific metabolomics biomarkers of NAFLD in a healthy cohort of youth. DESIGN/SETTING: This prospective study included 395 participants of the EPOCH cohort in Colorado, who were recruited 2006-2009 ("T1 visit") and followed for 5 years ("T2 visit"). We entered 767 metabolites measured at T1 into a reduced rank regression model to identify the strongest determinants of hepatic fat fraction (HFF) at T2, separately for boys and girls. We compared the capacity of metabolites versus conventional risk factors (overweight/obesity, insulin, alanine transaminase, aspartate transaminase) to predict NAFLD (HFF ≥5%) and high HFF (fourth vs first quartile) using area under the receiver operating characteristic curve (AUC). RESULTS: Prevalence of NAFLD was 7.9% (8.5% of boys, 7.1% of girls). Mean ± SD HFF was 2.5 ± 3.1%. We identified 13 metabolites in girls and 10 metabolites in boys. Metabolites were in lipid, amino acid, and carbohydrate metabolism pathways. At T1, the metabolites outperformed conventional risk factors in prediction of high HFF but not NAFLD. At T2, the metabolites were superior to conventional risk factors as predictors of high HFF (AUC for metabolites vs conventional risk factors for boys: 0.9565 vs 0.8851, P = 0.02; for girls: 0.9450 vs 0.8469, P = 0.02) with similar trends for NAFLD, although the differences were not significant. CONCLUSIONS: The metabolite profiles identified herein are superior predictors of high HFF when assessed 5 years prior and concurrently in a general-risk setting.