Unique Biomarker Characteristics in Gestational Diabetes Mellitus Identified by LC-MS-Based Metabolic Profiling
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...
Saved in:
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
|
| Series: | Journal of Diabetes Research |
| Online Access: | http://dx.doi.org/10.1155/2021/6689414 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841524308407484416 |
|---|---|
| author | Xingjun Meng Bo Zhu Yan Liu Lei Fang Binbin Yin Yanni Sun Mengni Ma Yuli Huang Yuning Zhu Yunlong Zhang |
| author_facet | Xingjun Meng Bo Zhu Yan Liu Lei Fang Binbin Yin Yanni Sun Mengni Ma Yuli Huang Yuning Zhu Yunlong Zhang |
| author_sort | Xingjun Meng |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-da775e8471494dd89ed5b81be80b44c7 |
| institution | Kabale University |
| issn | 2314-6745 2314-6753 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Diabetes Research |
| spelling | doaj-art-da775e8471494dd89ed5b81be80b44c72025-02-03T07:23:28ZengWileyJournal of Diabetes Research2314-67452314-67532021-01-01202110.1155/2021/66894146689414Unique Biomarker Characteristics in Gestational Diabetes Mellitus Identified by LC-MS-Based Metabolic ProfilingXingjun Meng0Bo Zhu1Yan Liu2Lei Fang3Binbin Yin4Yanni Sun5Mengni Ma6Yuli Huang7Yuning Zhu8Yunlong Zhang9Department of Clinical Laboratory, Women’s Hospital, School of Medicine, Zhejiang University, Hangzhou 310006, ChinaDepartment of Clinical Laboratory, Women’s Hospital, School of Medicine, Zhejiang University, Hangzhou 310006, ChinaSchool of Traditional Chinese Medicine, Jinan University, Guangzhou 510632, ChinaDepartment of Clinical Laboratory, Women’s Hospital, School of Medicine, Zhejiang University, Hangzhou 310006, ChinaDepartment of Clinical Laboratory, Women’s Hospital, School of Medicine, Zhejiang University, Hangzhou 310006, ChinaDepartment of Clinical Laboratory, Women’s Hospital, School of Medicine, Zhejiang University, Hangzhou 310006, ChinaDepartment of Clinical Laboratory, Women’s Hospital, School of Medicine, Zhejiang University, Hangzhou 310006, ChinaDepartment of Cardiology, Shunde Hospital, Southern Medical University (The First People’s Hospital of Shunde Foshan), Foshan 528300, ChinaDepartment of Clinical Laboratory, Women’s Hospital, School of Medicine, Zhejiang University, Hangzhou 310006, ChinaKey Laboratory of Neuroscience, School of Basic Medical Sciences, Guangzhou Medical University, Guangzhou 511436, ChinaBackground. 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.http://dx.doi.org/10.1155/2021/6689414 |
| spellingShingle | Xingjun Meng Bo Zhu Yan Liu Lei Fang Binbin Yin Yanni Sun Mengni Ma Yuli Huang Yuning Zhu Yunlong Zhang Unique Biomarker Characteristics in Gestational Diabetes Mellitus Identified by LC-MS-Based Metabolic Profiling Journal of Diabetes Research |
| title | Unique Biomarker Characteristics in Gestational Diabetes Mellitus Identified by LC-MS-Based Metabolic Profiling |
| title_full | Unique Biomarker Characteristics in Gestational Diabetes Mellitus Identified by LC-MS-Based Metabolic Profiling |
| title_fullStr | Unique Biomarker Characteristics in Gestational Diabetes Mellitus Identified by LC-MS-Based Metabolic Profiling |
| title_full_unstemmed | Unique Biomarker Characteristics in Gestational Diabetes Mellitus Identified by LC-MS-Based Metabolic Profiling |
| title_short | Unique Biomarker Characteristics in Gestational Diabetes Mellitus Identified by LC-MS-Based Metabolic Profiling |
| title_sort | unique biomarker characteristics in gestational diabetes mellitus identified by lc ms based metabolic profiling |
| url | http://dx.doi.org/10.1155/2021/6689414 |
| work_keys_str_mv | AT xingjunmeng uniquebiomarkercharacteristicsingestationaldiabetesmellitusidentifiedbylcmsbasedmetabolicprofiling AT bozhu uniquebiomarkercharacteristicsingestationaldiabetesmellitusidentifiedbylcmsbasedmetabolicprofiling AT yanliu uniquebiomarkercharacteristicsingestationaldiabetesmellitusidentifiedbylcmsbasedmetabolicprofiling AT leifang uniquebiomarkercharacteristicsingestationaldiabetesmellitusidentifiedbylcmsbasedmetabolicprofiling AT binbinyin uniquebiomarkercharacteristicsingestationaldiabetesmellitusidentifiedbylcmsbasedmetabolicprofiling AT yannisun uniquebiomarkercharacteristicsingestationaldiabetesmellitusidentifiedbylcmsbasedmetabolicprofiling AT mengnima uniquebiomarkercharacteristicsingestationaldiabetesmellitusidentifiedbylcmsbasedmetabolicprofiling AT yulihuang uniquebiomarkercharacteristicsingestationaldiabetesmellitusidentifiedbylcmsbasedmetabolicprofiling AT yuningzhu uniquebiomarkercharacteristicsingestationaldiabetesmellitusidentifiedbylcmsbasedmetabolicprofiling AT yunlongzhang uniquebiomarkercharacteristicsingestationaldiabetesmellitusidentifiedbylcmsbasedmetabolicprofiling |