This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Research article
Hua Zhang
Department of Obstetrics and Gyenecology, The First Affiliated Hospital of Chongqing Medical University, No.1 Youyi Road, Yuzhong District, Chongqing, People's Republic of China
Corresponding Author
ORCiD: https://orcid.org/0000-0003-3501-8385
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background: Gestational diabetes mellitus (GDM) is characterized by impaired glucose tolerance in pregnancy and without a history of diabetes mellitus. It can lead to adverse maternal and neonatal outcomes. The incidence of GDM is closely related to maternal age, but there are only a few pregnancy-related metabolomic studies involving advanced maternal age (AMA) in China.
Methods: 20 GDM and 20 normal pregnant participants(≥35 years old) were recruited from the Complex Lipids in Mothers and Babies (CLIMB) study. Maternal plasma and urine metabolomes collected at the first and third trimester were analyzed using gas chromatography-mass spectrometry (GC-MS).
Results: Of the metabolites identified using GC–MS, 165 metabolites and 192 metabolites were found in plasma and urine respectively. However, urine metabolomic profiles were unable to distinguish GDM from controls, while there were 14 and 39 significantly different metabolites in plasma of the two groups in first and third trimester. Especially, by combining seven metabolites including cysteine, malonic acid, stearic acid, alanine, 11,14-eicosadienoic acid, 2-methyloctadecanoic acid, and arachidic acid using multivariant receiver operating characteristic(ROC) models, we were capable of discriminating GDM from healthy pregnancies with an area under curve (AUC) of 0.928 at early gestation.
Conclusion: This study explores metabolomic profiles between GDM and normal pregnancies longitudinally. Several metabolites have the potential to be biomarkers to predict GDM with AMA. Besides, the discordant metabolome profiles between the two groups could be helpful to understand the etiology of elderly GDM.
Keywords
metabolomics, gestational diabetes mellitus, advanced maternal age, pregnancy
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This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Research article
Hua Zhang
Department of Obstetrics and Gyenecology, The First Affiliated Hospital of Chongqing Medical University, No.1 Youyi Road, Yuzhong District, Chongqing, People's Republic of China
Corresponding Author
ORCiD: https://orcid.org/0000-0003-3501-8385
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
History
CURRENT STATUS: Posted
Version 1
Posted 19 Nov, 2020
No community comments so far
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background: Gestational diabetes mellitus (GDM) is characterized by impaired glucose tolerance in pregnancy and without a history of diabetes mellitus. It can lead to adverse maternal and neonatal outcomes. The incidence of GDM is closely related to maternal age, but there are only a few pregnancy-related metabolomic studies involving advanced maternal age (AMA) in China.
Methods: 20 GDM and 20 normal pregnant participants(≥35 years old) were recruited from the Complex Lipids in Mothers and Babies (CLIMB) study. Maternal plasma and urine metabolomes collected at the first and third trimester were analyzed using gas chromatography-mass spectrometry (GC-MS).
Results: Of the metabolites identified using GC–MS, 165 metabolites and 192 metabolites were found in plasma and urine respectively. However, urine metabolomic profiles were unable to distinguish GDM from controls, while there were 14 and 39 significantly different metabolites in plasma of the two groups in first and third trimester. Especially, by combining seven metabolites including cysteine, malonic acid, stearic acid, alanine, 11,14-eicosadienoic acid, 2-methyloctadecanoic acid, and arachidic acid using multivariant receiver operating characteristic(ROC) models, we were capable of discriminating GDM from healthy pregnancies with an area under curve (AUC) of 0.928 at early gestation.
Conclusion: This study explores metabolomic profiles between GDM and normal pregnancies longitudinally. Several metabolites have the potential to be biomarkers to predict GDM with AMA. Besides, the discordant metabolome profiles between the two groups could be helpful to understand the etiology of elderly GDM.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
This is a list of supplementary files associated with this preprint. Click to download.
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