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Identification of differential metabolites using untargeted metabolomics between gestational diabetes and normal pregnant women.
Peng, Mei Lin; Zhang, Zheng; Zhou, Minqi; He, Chao; Xiao, Lin; Yin, Heng; Zhao, Kai.
Afiliação
  • Peng ML; Institute of Reproductive Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Zhang Z; Central China Normal University, School of Life Sciences, Wuhan, China.
  • Zhou M; Wuhan Prevention and Treatment Center for Occupational Diseases, Wuhan, China.
  • He C; Institute of Reproductive Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Xiao L; Institute of Reproductive Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Yin H; Institute of Reproductive Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Zhao K; Department of Obstetrics, Maternal and Child Health Hospital of Hubei Province, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Int J Gynaecol Obstet ; 159(3): 903-911, 2022 Dec.
Article em En | MEDLINE | ID: mdl-35514238
ABSTRACT

OBJECTIVE:

To study the metabonomics differences between pregnant women with gestational diabetes mellitus (GDM) in the third trimester and those in a group without GDM by screening a group of highly efficient and sensitive markers for GDM and validating previously published early metabolic markers of GDM.

METHODS:

A cross-sectional cohort study based on ultra performance liquid chromatography tandem mass spectrometry untargeted metabolomics analysis of serum samples collected from 59 pregnant women with GDM and 59 pregnant women without GDM.

RESULTS:

A total of 121 metabolites were detected, and 27 were identified as differential metabolites between GDM and control. The combination of 27 metabolic peaks had area under curve (AUC) values of 0.90, 0.92, and 0.93 in the prediction models using support vector machine, partial least squares, and random forest, respectively. Finally, five metabolite biomarkers were selected to construct logistic regression models L-valine, hypoxanthine, eicosapentaenoic acid, 2-amino-1,3,4-octadecanotriol, and choline. The AUC value of these metabolites was 0.769 between the GDM group and the control group.

CONCLUSIONS:

The discovery of a group of differential metabolites in pregnant women with GDM in the third trimester and in pregnant women without GDM may facilitate the study of the pathologic mechanism of GDM; it may be possible to find an efficient and sensitive alternative GDM detection method.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Diabetes Gestacional Tipo de estudo: Diagnostic_studies / Observational_studies / Prevalence_studies / Prognostic_studies / Risk_factors_studies Limite: Female / Humans / Pregnancy Idioma: En Revista: Int J Gynaecol Obstet Ano de publicação: 2022 Tipo de documento: Article País de afiliação: China

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Diabetes Gestacional Tipo de estudo: Diagnostic_studies / Observational_studies / Prevalence_studies / Prognostic_studies / Risk_factors_studies Limite: Female / Humans / Pregnancy Idioma: En Revista: Int J Gynaecol Obstet Ano de publicação: 2022 Tipo de documento: Article País de afiliação: China