Early prediction of gestational diabetes mellitus using machine learning-integrated metabolomic and clinical features.

ABSTRACT:

Background

Gestational diabetes mellitus (GDM), a prevalent metabolic disorder associated with pregnancy, which often postpones intervention until after metabolic complications have developed. This study seeks to develop an integrated predictive model that combines first trimester metabolomic signatures with established clinical risk factors to enable the early detection of high-risk pregnancies prior to the onset of irreversible metabolic damages.

Methods

A total of 89 pregnant women [45 with GDM, 44 with normal glucose tolerance (NGT)] was recruited at Hainan Provincial People's Hospital. Serum and urine samples were subjected to untargeted metabolomic profiling employing UPLC-MS/MS. Metabolite identification was conducted using the Human Metabolome Database and Metlin dat

SUBMITTER: Ji Q 

PROVIDER: S-EPMC12658359 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature