Discovery of metabolic biomarkers for gestational diabetes mellitus in a Chinese population

Abstract Background Gestational diabetes mellitus (GDM), one of the most common pregnancy complications, can lead to morbidity and mortality in both mother infant. Metabolomics has provided new insights into pathology GDM systemic analysis with metabolites is required for providing more clues diagnosis mechanism research. This study aims reveal metabolic differences between normal pregnant women patients second- third-trimester stages confirm clinical relevance these findings. Methods Metabolites were quantitated serum samples 200 healthy second trimester, 199 controls, third trimester. Both function pathway analyses applied explore biological roles involved two sets metabolites. Then trimester stage-specific metabolite biomarkers identified by combining machine learning approaches, logistic regression models constructed evaluate predictive efficiency. Finally, weighted gene co-expression network method was used further capture associations modules indices. Results revealed that 57 differentially expressed (DEMs) discovered second-trimester group, among which significant 3-methyl-2-oxovaleric acid. Similarly, 72 DEMs found ketoleucine alpha-ketoisovaleric These mainly metabolism amino acids, fatty acids bile acids. The selected achieved area under curve values 0.807 0.81 groups. Furthermore, DEMs/biomarkers GDM-related Conclusions Metabolic found. Associations indices also investigated, may provide GDM.