Maternal Serum Metabolomics in Mid-Pregnancy Identifies Lipid Pathways as a Key Link to Offspring Obesity in Early Childhood

Abstract

Maternal metabolism during pregnancy shapes offspring health via in utero programming. In the Healthy Start study, we identified five subgroups of pregnant women based on conventional metabolic biomarkers: Reference (n = 360); High HDL-C (n = 289); Dyslipidemic-High TG (n = 149); Dyslipidemic-High FFA (n = 180); Insulin Resistant (IR)-Hyperglycemic (n = 87). These subgroups not only captured metabolic heterogeneity among pregnant participants but were also associated with offspring obesity in early childhood, even among women without obesity or diabetes. Here, we utilize metabolomics data to enrich characterization of the metabolic subgroups and identify key compounds driving between-group differences. We analyzed fasting blood samples from 1065 pregnant women at 18 gestational weeks using untargeted metabolomics. We used weighted gene correlation network analysis (WGCNA) to derive a global network based on the Reference subgroup and characterized distinct metabolite modules representative of the different metabolomic profiles. We used the mummichog algorithm for pathway enrichment and identified key compounds that differed across the subgroups. Eight metabolite modules representing pathways such as the carnitine-acylcarnitine translocase system, fatty acid biosynthesis and activation, and glycerophospholipid metabolism were identified. A module that included 189 compounds related to DHA peroxidation, oxidative stress, and sex hormone biosynthesis was elevated in the Insulin Resistant-Hyperglycemic vs. the Reference subgroup. This module was positively correlated with total cholesterol (R:0.10; p-value < 0.0001) and free fatty acids (R:0.07; p-value < 0.05). Oxidative stress and inflammatory pathways may underlie insulin resistance during pregnancy, even below clinical diabetes thresholds. These findings highlight potential therapeutic targets and strategies for pregnancy risk stratification and reveal mechanisms underlying the developmental origins of metabolic disease risk.

Keywords: WGCNA; gestational diabetes mellitus; lipids; metabolomics; pregnancy.

Figure 1

Schematic showing the global metabolomic network, as well as key modules within the network, each representing distinct metabolomic profiles. The metabolomic network and modules were derived in the Reference subgroup. The dendrogram and module colors show the hierarchical clustering, which captures the connectedness between compounds while considering their relationship to all other compounds within the network. Each color represents a module configuration characterized by highly connected compounds with few having many connections with neighboring compounds. In this figure we highlight the Brown module. The first eigenvector of the module is thought of as the average metabolomic pattern or profile captured by that module, with higher values indicating greater similarity with the module profile and lower values indicating less similarity. Within the module, the compounds that are most strongly correlated with the first eigenvector are considered to be key compounds of the profile and, accordingly, the overall module. In this figure, we show a generated example and have increased the size of the circle to indicate it is a key compound as well as increased the width of the edges to indicate it is highly connected to other compounds in the module profile.

Figure 2

Pearson correlation between each module metabolomic profile and conventional metabolic markers. The metabolomic network and modules were derived in the Reference subgroup. Pearson correlation was used to assess the correlation between the first eigenvector of each module and the conventional metabolic biomarkers. Darker blue indicates a stronger negative correlation, darker red indicates a stronger positive correlation. * p-value < 0.05, ** p-value < 0.001, *** p-value < 0.0001.

Figure 3

Differences in mean Brown module metabolomic profile across metabolic subgroups. The metabolomic network and modules were derived in the Reference subgroup. In this figure we show the Brown module. The first eigenvector of the module represents the average metabolomic pattern or profile captured by that module, with higher values indicating greater similarity with the module profile and lower values indicating less similarity. Pairwise test is Dunnett, with significant differences shown by a horizontal bar.