Metabolomics Study Of Gestational Diabetes Mellitus

Metabolomics is a technology that developed in the 1990s,which can reflect the metabolic profile of the organism that changes in response to environmental factors or internal physiological status.Through metabolomics analysis,important metabolites that are related to the occurrence and development of diseases can be found.What’s more,we are able to investigate metabolic pathways,analyze pathophysiological states of human beings,and explore underlying mechanism of diseases.Analytical method based on mass spectroscopy is commonly used in metabolomics study.Since it has high sensitivity and selectivity,it is widely used in studies of diseases.Gestational diabetes mellitus(GDM),defined as abnormal glucose tolerance with onset or first recognition during pregnancy,is a common complication during gestation.If it is out of control,then GDM can lead to several complications for both mothers and infants.Even if GDM mothers are under good glycemic control,they are linked to a greater risk of diabetes,metabolic syndrome,and cardiovascular diseases after pregnancy.Currently,pregnancies mainly undergo GDM screening from 24 to 28 gestational weeks in China.As a commonly accepted metabolic disease,typical metabolic biomarkers for GDM mechanism and prognosis research are not found.Thus,in this study we aimed to find GDM-related metabolic biomarkers using metabolomics-based technique,explore metabolic pathways that metabolic biomarkers participate in,and offer clues for the molecular mechanism of GDM.In this study,we conducted a 1:1 matched case-control study according to age,pre-gestational BMI,and gestational week.The diagnostic criteria for GDM women were based on the standards recommended by the International Association of the Diabetes and Pregnancy Study Group.Finally,798 participants were involved in this study,containing 200 healthy pregnant women and 200 GDM women in the second trimester,199 normal controls,and 199 GDM patients in the third trimester.Serum samples obtained from 24 to the end of 27 gestational weeks were defined as the second-trimester group,while those after 28 gestational weeks were defined as the third-trimester group.Serum samples from participants were collected.We then used ultra-performance liquid chromatography coupled to tandem mass spectrometry system and multivariate analyses including principal component analysis,partial least square discriminant analysis,and orthogonal partial least square discriminant analysis to detect,revealing that there were differences between GDM patients and normal controls.Combining the results of univariate and multivariate analyses,57 metabolites in the second trimester and 40 metabolites in the third trimester were found differentially expressed in GDM women.The differentially expressed metabolites for each comparison group were evaluated using enrichment analysis with SMPDB.In the second-trimester group,three pathways were observed,including the alpha linolenic acid and linoleic acid metabolism,beta oxidation of very long chain fatty acids,and valine-leucine-isoleucine degradation.In the third-trimester group,11 pathways were found,including urea cycle,ammonia recycling,glycine and serine metabolism,valine-leucine-isoleucine degradation,arginine and proline metabolism,alanine metabolism,glutamate metabolism,aspartate metabolism,glucose-alanine cycle,phenylalanine and tyrosine metabolism,and carnitine synthesis.These pathways were significantly associated with the corresponding metabolites.Metabolites were further analyzed using random forest for identifying potential biomarkers based on which logistic regression models were constructed for evaluating predictive efficiency.Area under the curve for receiver operating characteristic curves were calculated for assessing the performance of potential biomarkers with logistic regression models for GDM.The second-trimester group-specific logistic regression model achieved a AUC of 0.807.Similarly,for the third-trimester group,the metabolite biomarkers were selected using random forest,and achieved a AUC of 0.810.Finally,the weighted gene co-expression network analysis was used for inferring the association between metabolite modules and clinical indices.Results showed that four modules were detected for the second-trimester group and five modules were found for the third-trimester group.These metabolite modules were related to some clinical indices.Consequently,metabolomics study based on mass spectroscopy can provide clues for potential biomarkers and pathogenesis of GDM.