Dataset Information

Gestational diabetes mellitus epigenetically affects genes predominantly involved in metabolic diseases.

ABSTRACT: Offspring exposed to gestational diabetes mellitus (GDM) have an increased risk for chronic diseases, and one promising mechanism for fetal metabolic programming is epigenetics. Therefore, we postulated that GDM exposure impacts the offspring's methylome and used an epigenomic approach to explore this hypothesis. Placenta and cord blood samples were obtained from 44 newborns, including 30 exposed to GDM. Women were recruited at first trimester of pregnancy and followed until delivery. GDM was assessed after a 75-g oral glucose tolerance test at 24-28 weeks of pregnancy. DNA methylation was measured at>485,000 CpG sites (Infinium HumanMethylation450 BeadChips). Ingenuity Pathway Analysis was conducted to identify metabolic pathways epigenetically affected by GDM. Our results showed that 3,271 and 3,758 genes in placenta and cord blood, respectively, were potentially differentially methylated between samples exposed or not to GDM (p-values down to 1 × 10(-06); none reached the genome-wide significance levels), with more than 25% (n = 1,029) being common to both tissues. Mean DNA methylation differences between groups were 5.7 ± 3.2% and 3.4 ± 1.9% for placenta and cord blood, respectively. These genes were likely involved in the metabolic diseases pathway (up to 115 genes (11%), p-values for pathways = 1.9 × 10(-13)

SUBMITTER: Ruchat SM

PROVIDER: S-EPMC3883770 | biostudies-literature | 2013 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Gestational diabetes mellitus epigenetically affects genes predominantly involved in metabolic diseases.

Ruchat Stephanie-May SM Houde Andrée-Anne AA Voisin Grégory G St-Pierre Julie J Perron Patrice P Baillargeon Jean-Patrice JP Gaudet Daniel D Hivert Marie-France MF Brisson Diane D Bouchard Luigi L

Epigenetics 20130718 9

Offspring exposed to gestational diabetes mellitus (GDM) have an increased risk for chronic diseases, and one promising mechanism for fetal metabolic programming is epigenetics. Therefore, we postulated that GDM exposure impacts the offspring's methylome and used an epigenomic approach to explore this hypothesis. Placenta and cord blood samples were obtained from 44 newborns, including 30 exposed to GDM. Women were recruited at first trimester of pregnancy and followed until delivery. GDM was as ...[more]

PMID: 23975224

Similar Datasets

Project description:BackgroundPregnant women with gestational diabetes mellitus (GDM) or type 2 diabetes mellitus (T2DM) are at increased risks of pre-term labor, hypertension and preeclampsia. In this study, metabolic profiling of blood samples collected from GDM, T2DM and control pregnant women was undertaken to identify potential diagnostic biomarkers in GDM/T2DM and compared to pregnancy outcome.MethodsSixty-seven pregnant women (21 controls, 32 GDM, 14 T2DM) in their second trimester underwent targeted metabolomics of plasma samples using tandem mass spectrometry with the Biocrates MxP® Quant 500 Kit. Linear regression models were used to identify the metabolic signature of GDM and T2DM, followed by generalized linear model (GLMNET) and Receiver Operating Characteristic (ROC) analysis to determine best predictors of GDM, T2DM and pre-term labor.ResultsThe gestational age at delivery was 2 weeks earlier in T2DM compared to GDM and controls and correlated negatively with maternal HbA1C and systolic blood pressure and positively with serum albumin. Linear regression models revealed elevated glutamate and branched chain amino acids in GDM + T2DM group compared to controls. Regression models also revealed association of lower levels of triacylglycerols and diacylglycerols containing oleic and linoleic fatty acids with pre-term delivery. A generalized linear model ROC analyses revealed that that glutamate is the best predictors of GDM compared to controls (area under curve; AUC = 0.81). The model also revealed that phosphatidylcholine diacyl C40:2, arachidonic acid, glycochenodeoxycholic acid, and phosphatidylcholine acyl-alkyl C34:3 are the best predictors of GDM + T2DM compared to controls (AUC = 0.90). The model also revealed that the triacylglycerols C17:2/36:4 and C18:1/34:1 are the best predictors of pre-term delivery (≤ 37 weeks) (AUC = 0.84).ConclusionsThis study highlights the metabolite alterations in women in their second trimester with diabetes mellitus and identifies predictive indicators of pre-term delivery. Future studies to confirm these associations in other cohorts and investigate their functional relevance and potential utilization for targeted therapies are warranted.

Project description:BackgroundGestational diabetes mellitus (GDM), one of the most common pregnancy complications, can lead to morbidity and mortality in both the mother and the infant. Metabolomics has provided new insights into the pathology of GDM and systemic analysis of GDM with metabolites is required for providing more clues for GDM diagnosis and mechanism research. This study aims to reveal metabolic differences between normal pregnant women and GDM patients in the second- and third-trimester stages and to confirm the clinical relevance of these new findings.MethodsMetabolites were quantitated with the serum samples of 200 healthy pregnant women and 200 GDM women in the second trimester, 199 normal controls, and 199 GDM patients in the third trimester. Both function and pathway analyses were applied to explore biological roles involved in the two sets of metabolites. Then the trimester stage-specific GDM metabolite biomarkers were identified by combining machine learning approaches, and the logistic regression models were constructed to evaluate predictive efficiency. Finally, the weighted gene co-expression network analysis method was used to further capture the associations between metabolite modules with biomarkers and clinical indices.ResultsThis study revealed that 57 differentially expressed metabolites (DEMs) were discovered in the second-trimester group, among which the most significant one was 3-methyl-2-oxovaleric acid. Similarly, 72 DEMs were found in the third-trimester group, and the most significant metabolites were ketoleucine and alpha-ketoisovaleric acid. These DEMs were mainly involved in the metabolism pathway of amino acids, fatty acids and bile acids. The logistic regression models for selected metabolite biomarkers achieved the area under the curve values of 0.807 and 0.81 for the second- and third-trimester groups. Furthermore, significant associations were found between DEMs/biomarkers and GDM-related indices.ConclusionsMetabolic differences between healthy pregnant women and GDM patients were found. Associations between biomarkers and clinical indices were also investigated, which may provide insights into pathology of GDM.

Dataset Information

Gestational diabetes mellitus epigenetically affects genes predominantly involved in metabolic diseases.

Publications

Gestational diabetes mellitus epigenetically affects genes predominantly involved in metabolic diseases.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets