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Unique Biomarker Characteristics in Gestational Diabetes Mellitus Identified by LC-MS-Based Metabolic Profiling.
Meng, Xingjun; Zhu, Bo; Liu, Yan; Fang, Lei; Yin, Binbin; Sun, Yanni; Ma, Mengni; Huang, Yuli; Zhu, Yuning; Zhang, Yunlong.
Afiliación
  • Meng X; Department of Clinical Laboratory, Women's Hospital, School of Medicine, Zhejiang University, Hangzhou 310006, China.
  • Zhu B; Institute of Laboratory Medicine, Zhejiang University, Hangzhou 310006, China.
  • Liu Y; Department of Clinical Laboratory, Women's Hospital, School of Medicine, Zhejiang University, Hangzhou 310006, China.
  • Fang L; Institute of Laboratory Medicine, Zhejiang University, Hangzhou 310006, China.
  • Yin B; School of Traditional Chinese Medicine, Jinan University, Guangzhou 510632, China.
  • Sun Y; Department of Clinical Laboratory, Women's Hospital, School of Medicine, Zhejiang University, Hangzhou 310006, China.
  • Ma M; Institute of Laboratory Medicine, Zhejiang University, Hangzhou 310006, China.
  • Huang Y; Department of Clinical Laboratory, Women's Hospital, School of Medicine, Zhejiang University, Hangzhou 310006, China.
  • Zhu Y; Institute of Laboratory Medicine, Zhejiang University, Hangzhou 310006, China.
  • Zhang Y; Department of Clinical Laboratory, Women's Hospital, School of Medicine, Zhejiang University, Hangzhou 310006, China.
J Diabetes Res ; 2021: 6689414, 2021.
Article en En | MEDLINE | ID: mdl-34212051
BACKGROUND: Gestational diabetes mellitus (GDM) is a type of glucose intolerance disorder that first occurs during women's pregnancy. The main diagnostic method for GDM is based on the midpregnancy oral glucose tolerance test. The rise of metabolomics has expanded the opportunity to better identify early diagnostic biomarkers and explore possible pathogenesis. METHODS: We collected blood serum from 34 GDM patients and 34 normal controls for a LC-MS-based metabolomics study. RESULTS: 184 metabolites were increased and 86 metabolites were decreased in the positive ion mode, and 65 metabolites were increased and 71 were decreased in the negative ion mode. Also, it was found that the unsaturated fatty acid metabolism was disordered in GDM. Ten metabolites with the most significant differences were selected for follow-up studies. Since the diagnostic specificity and sensitivity of a single differential metabolite are not definitive, we combined these metabolites to prepare a ROC curve. We found a set of metabolite combination with the highest sensitivity and specificity, which included eicosapentaenoic acid, docosahexaenoic acid, docosapentaenoic acid, arachidonic acid, citric acid, α-ketoglutaric acid, and genistein. The area under the curves (AUC) value of those metabolites was 0.984 between the GDM and control group. CONCLUSIONS: Our results provide a direction for the mechanism of GDM research and demonstrate the feasibility of developing a diagnostic test that can distinguish between GDM and normal controls clearly. Our findings were helpful to develop novel biomarkers for precision or personalized diagnosis for GDM. In addition, we provide a critical insight into the pathological and biological mechanisms for GDM.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Diabetes Gestacional / Metabolómica Tipo de estudio: Diagnostic_studies / Observational_studies / Prognostic_studies Límite: Adult / Female / Humans / Pregnancy Idioma: En Revista: J Diabetes Res Año: 2021 Tipo del documento: Article País de afiliación: China Pais de publicación: Reino Unido
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Diabetes Gestacional / Metabolómica Tipo de estudio: Diagnostic_studies / Observational_studies / Prognostic_studies Límite: Adult / Female / Humans / Pregnancy Idioma: En Revista: J Diabetes Res Año: 2021 Tipo del documento: Article País de afiliación: China Pais de publicación: Reino Unido