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

Participants with GDM and NGT show great difference in the levels of many metabolites. The ML model according to the metabolites in the first trimester and clinical feature demonstrates high accuracy for early GDM prediction. The result of this research highlighted the potential of metabolites in the prediction of GDM in the early stage of pregnancy.

Authors

Institutions

Publication Details

Journal
Frontiers in Endocrinology
Published
2025-11-13
DOI
https://doi.org/10.3389/fendo.2025.1687146
Primary Topic
Gestational Diabetes Research and Management
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

Lan Gao, Yingbei Lin, Bidong Fu, Qun Ji et al.
Frontiers in Endocrinology
Gestational Diabetes Research and Management
article

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

Lan Gao, Yingbei Lin, Bidong Fu, Qun Ji, Fei Wang, Xiaofang Chen, Haiwei Liu
article en

Abstract

Participants with GDM and NGT show great difference in the levels of many metabolites. The ML model according to the metabolites in the first trimester and clinical feature demonstrates high accuracy for early GDM prediction. The result of this research highlighted the potential of metabolites in the prediction of GDM in the early stage of pregnancy.

Frontiers in EndocrinologyVol. 16
Hainan General Hospital (CN), Hainan Medical University (CN)
Openalex Percentile: Top 57%
Gestational Diabetes Research and Management
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.