Metabolomic profiling reveals early biomarkers of gestational diabetes mellitus and associated hepatic steatosis

Abstract

Background: This study aims to identify early metabolomic biomarkers of gestational diabetes mellitus (GDM) and evaluate their association with hepatic steatosis.

Methods: We compared maternal serum metabolomic profiles between women who developed GDM (n = 118) and matched controls (n = 118) during the first (10-14 gestational weeks) and second (24-28 gestational weeks) trimesters using ultra-performance liquid chromatography coupled with mass spectrometry. Mediation analysis was performed to evaluate the mediating role of metabolic dysfunction-associated steatotic liver disease (MASLD) in the relationship between metabolites and subsequent development of GDM. A refined prediction model was developed to predict GDM using established clinical factors and selected metabolites.

Results: Significant alterations in circulating metabolites, including amino acids, bile acids, and phospholipids, were observed in the GDM group compared to controls during early pregnancy. Mediation analysis revealed that several metabolites, including glycocholic acid (proportion mediated (PM) = 31.9%), butanoyl carnitine (PM = 25.7%), and uric acid (PM = 22.4%), had significant indirect effects on GDM incidence mediated by hepatic steatosis. The refined prediction model composed of clinical factors and selected metabolites in the first trimester demonstrated higher performance in predicting GDM development than the established prediction model composed solely of clinical factors (AUC, 0.85 vs. 0.63, p < 0.001).

Conclusions: Women who developed GDM exhibited altered metabolomic profiles from early pregnancy, which showed a significant correlation with GDM, with MASLD as a mediator. Selected metabolomic biomarkers may serve as predictive markers and potential targets for early risk assessment and intervention in GDM.

Research insights: WHAT IS CURRENTLY KNOWN ABOUT THIS TOPIC?: Gestational diabetes mellitus (GDM) is a common pregnancy complication with significant health risks. Early identification of women at high risk for GDM is crucial for timely intervention and improved outcomes. WHAT IS THE KEY RESEARCH QUESTION?: What alterations in circulating metabolites during early pregnancy are associated with subsequent GDM development? Does metabolic dysfunction-associated steatotic liver disease (MASLD) mediate the association between specific metabolites and GDM risk? WHAT IS NEW?: Significant alterations in bile acids, amino acids, phosphatidylethanolamines, and phosphatidylinositols were observed in early pregnancy sera of women who later developed GDM. MASLD significantly mediated the effects of several metabolites on GDM risk, with mediation proportions ranging from 9.7 to 31.9%. A refined prediction model composed of clinical factors and metabolites significantly improved the performance in predicting GDM development. HOW MIGHT THIS STUDY INFLUENCE CLINICAL PRACTICE?: These results provide new insights into early metabolic alterations associated with GDM development and highlight the potential mediating role of MASLD. This comprehensive metabolomic approach may contribute to the development of improved risk prediction models and targeted interventions for GDM prevention.

Keywords: Early biomarker; Gestational diabetes mellitus; Hepatic steatosis; Mediation analysis; Metabolomics.

Fig. 1

Schematic overviews of the study

Fig. 2

Alterations of circulating metabolites in serum samples from pregnant women during the first trimester. (A) bile acid (BA) analysis, (B) polar semi-targeted analysis, and (C) lipidomic analysis. Fold changes were calculated by dividing the median value of GDM group by the median value of non-GDM group. Adjusted p-values (p_adj) were calculated using the Benjamini–Hochberg method from p-values obtained from ranked analysis of covariance (ANCOVA) test, with adjustments for maternal age, pre-pregnancy BMI, nulliparity, and family history of diabetes; ^*p_adj<0.05, ^**p_adj <0.01, and ^***p_adj <0.001

Fig. 3

Correlations between circulating metabolite levels and laboratory results in serum samples from pregnant women during the first trimester. (A) bile acid (BA) analysis, (B) polar semi-targeted analysis, and (C) lipidomic analysis. Statistical significance in Spearman’s correlation heat maps is indicated with asterisks (^*p < 0.05, ^**p < 0.01, and ^***p < 0.001)

Fig. 4

Associations between circulating metabolites and GDM / MASLD during the first trimester. (A) bile acid (BA) analysis, (B) polar semi-targeted analysis, and (C) lipidomic analysis. Multivariable–adjusted odds ratio, 95% CI and p-value were calculated from multiple logistic regression with adjustments for maternal age, pre-pregnancy BMI, nulliparity and family history of diabetes; ^*p < 0.05, ^**p < 0.01, and ^***p < 0.001 for GDM and ^#p < 0.05, ^##p < 0.01, and ^###p < 0.001 for MASLD

Fig. 5

Alterations in circulating metabolites according to MASLD status in serum samples from pregnant women during the first and second trimesters. P-values were obtained from the ranked analysis of covariance (ANCOVA) test, with adjustments for maternal age, pre-pregnancy BMI, nulliparity and family history of diabetes; ^*p < 0.05, ^**p < 0.01, and ^***p < 0.001 for GDM and ^#p < 0.05, ^##p < 0.01, and ^###p < 0.001 for MASLD

Fig. 6

Prediction model evaluation using receiver operating characteristic (ROC) curves