Bibliographic Details
| Title: |
Metabolic profiling and early prediction models for gestational diabetes mellitus in PCOS and non-PCOS pregnant women |
| Authors: |
Jin Wang, Can Cui, Fei Hou, Zhiyan Wu, Yingying Peng, Hua Jin |
| Source: |
European Journal of Medical Research, Vol 30, Iss 1, Pp 1-11 (2025) |
| Publisher Information: |
BMC, 2025. |
| Publication Year: |
2025 |
| Collection: |
LCC:Medicine |
| Subject Terms: |
Gestational diabetes mellitus, Polycystic ovary syndrome, Untargeted metabolomics analysis, Prediction models, Medicine |
| More Details: |
Abstract Background Gestational diabetes mellitus (GDM) is the most common pregnancy complication, significantly affecting maternal and neonatal health. Polycystic ovary syndrome (PCOS) is a common endocrine disorder characterized by metabolic abnormalities, which notably elevates the risk of developing GDM during pregnancy. Methods In this study, we utilized ultra-high-performance liquid chromatography for untargeted metabolomics analysis of serum samples from 137 pregnant women in the early-to-mid-pregnancy. The cohort consisted of 137 participants, including 70 in the PCOS group (36 who developed GDM in mid-to-late pregnancy and 34 who did not) and 67 in the non-PCOS group (37 who developed GDM and 30 who remained GDM-free). The aim was to investigate metabolic profile differences between PCOS and non-PCOS patients and to construct early GDM prediction models separately for the PCOS and non-PCOS groups. Results Our findings revealed significant differences in the metabolic profiles of PCOS patients, which may help elucidate the higher risk of GDM in the PCOS population. Moreover, tailored early GDM prediction models for the PCOS group demonstrated high predictive performance, providing strong support for early diagnosis and intervention in clinical practice. Conclusions Untargeted metabolomics analysis revealed distinct metabolic patterns between PCOS patients and non-PCOS patients, particularly in pathways related to GDM. Based on these findings, we successfully constructed GDM prediction models for both PCOS and non-PCOS groups, offering a promising tool for clinical management and early intervention in high-risk populations. |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
2047-783X |
| Relation: |
https://doaj.org/toc/2047-783X |
| DOI: |
10.1186/s40001-025-02526-2 |
| Access URL: |
https://doaj.org/article/a307af323cab4be8bdb778d542bcfc89 |
| Accession Number: |
edsdoj.307af323cab4be8bdb778d542bcfc89 |
| Database: |
Directory of Open Access Journals |
