Metabolic dynamics and prediction og gestational ange and time to delivery in pregant women
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo Sapiens
TISSUE(S): Blood
PROVIDER: ST001430 | MetabolomicsWorkbench |
REPOSITORIES: MetabolomicsWorkbench
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Project description:Metabolism during pregnancy is a dynamic and precisely programmed process, the failure of which can bring devastating consequences to the mother and fetus. To define a high-resolution temporal profile of metabolites during healthy pregnancy, we analyzed the untargeted metabolome of 784 weekly blood samples from 30 pregnant women. Broad changes and a highly choreographed profile were revealed: 4,995 metabolic features (of 9,651 total), 460 annotated compounds (of 687 total), and 34 human metabolic pathways (of 48 total) were significantly changed during pregnancy. Using linear models, we built a metabolic clock with five metabolites that time gestational age in high accordance with ultrasound (R = 0.92). Furthermore, two to three metabolites can identify when labor occurs (time to delivery within two, four, and eight weeks, AUROC ≥ 0.85). Our study represents a weekly characterization of the human pregnancy metabolome, providing a high-resolution landscape for understanding pregnancy with potential clinical utilities.
| S-EPMC7327522 | biostudies-literature
Project description:Aims/hypothesisAntenatal obesity and associated gestational diabetes (GDM) are increasing worldwide. While pre-existing insulin resistance is implicated in GDM in obese women, the responsible metabolic pathways remain poorly described. Our aim was to compare metabolic profiles in blood of obese pregnant women with and without GDM 10 weeks prior to and at the time of diagnosis by OGTT.MethodsWe investigated 646 women, of whom 198 developed GDM, in this prospective cohort study, a secondary analysis of UK Pregnancies Better Eating and Activity Trial (UPBEAT), a multicentre randomised controlled trial of a complex lifestyle intervention in obese pregnant women. Multivariate regression analyses adjusted for multiple testing, and accounting for appropriate confounders including study intervention, were performed to compare obese women with GDM with obese non-GDM women. We measured 163 analytes in serum, plasma or whole blood, including 147 from a targeted NMR metabolome, at time point 1 (mean gestational age 17 weeks 0 days) and time point 2 (mean gestational age 27 weeks 5 days, at time of OGTT) and compared them between groups.ResultsMultiple significant differences were observed in women who developed GDM compared with women without GDM (false discovery rate corrected p values <0.05). Most were evident prior to diagnosis. Women with GDM demonstrated raised lipids and lipoprotein constituents in VLDL subclasses, greater triacylglycerol enrichment across lipoprotein particles, higher branched-chain and aromatic amino acids and different fatty acid, ketone body, adipokine, liver and inflammatory marker profiles compared with those without GDM.Conclusions/interpretationAmong obese pregnant women, differences in metabolic profile, including exaggerated dyslipidaemia, are evident at least 10 weeks prior to a diagnosis of GDM in the late second trimester.
| S-EPMC6448883 | biostudies-literature
Project description:Asthma is a complex syndrome associated with episodic decompensations provoked by aeroaller-gen exposures. The underlying pathophysiological states driving exacerbations are latent in the resting state and do not adequately inform biomarker-driven therapy. A better understanding of the pathophysiological pathways driving allergic exacerbations is needed. We hypothesized that disease-associated pathways could be identified in humans by unbiased metabolomics of bron-choalveolar fluid (BALF) during the peak inflammatory response provoked by a bronchial aller-gen challenge. We analyzed BALF metabolites in samples from 12 volunteers who underwent segmental bronchial antigen provocation (SBP-Ag). Metabolites were quantified using liquid chromatography-tandem mass spectrometry (LC–MS/MS) followed by pathway analysis and cor-relation with airway inflammation. SBP-Ag induced statistically significant changes in 549 fea-tures that mapped to 72 uniquely identified metabolites. From these features, two distinct induci-ble metabolic phenotypes were identified by the principal component analysis, partitioning around medoids (PAM) and k-means clustering. Ten index metabolites were identified that in-formed the presence of asthma-relevant pathways, including unsaturated fatty acid produc-tion/metabolism, mitochondrial beta oxidation of unsaturated fatty acid, and bile acid metabolism. Pathways were validated using proteomics in eosinophils. A segmental bronchial allergen chal-lenge induces distinct metabolic responses in humans, providing insight into pathogenic and pro-tective endotypes in allergic asthma.
2022-06-09 | PXD033172 | Pride
Project description:Objectives To perceive the temporal features of breast milk proteome between women with gestational diabetes mellitus (GDM) and healthy controls across various lactation periods, as well as to explore the potential impacts of these differences on the growth of offspring.
Methods The study cohort included twenty mothers with GDM and twenty healthy mothers. Human milk samples were obtained at four distinct time points: colostrum (4-6 days postpartum), transitional milk (12-14 days postpartum), early mature milk (42 days postpartum) and mature milk (4 months postpartum). Shotgun proteomics with label free quantification was applied to analyze the milk proteome. Gene Ontology (GO) enrichment analysis, alongside other bioinfomatic tools were conducted to elucidate the function of differentially expressed proteins. Subsequently, a random forest model was utilized to discern proteins that could reliably differentiate milk samples from mothers with gestational diabetes mellitus (GDM) from those of healthy counterparts. Furthermore, correlative analysis was employed to investigate the association between these proteins and the anthropometric indices of infants.
Results Principal coordinate analysis (PCoA) revealed distinct separations in the milk proteome of GDM and healthy mothers during the initial lactation stages, with these differences diminishing over time. The up-regulated proteins in GDM were predominantly associated with the innate immune system, complement and coagulation cascades, cellular secretion, enzymatic and binding activity, and platelet activation. Six proteins were identified as effective markers for distinguishing milk samples from the two groups, with an average area under the curve (AUC) value of 0.91. Twenty-eight proteins exhibited consistent changes between GDM and healthy groups across at least two lactation stages, many of which were significantly correlated to the anthropometric indices of the offsprings.
Conclusions GDM significantly influences the milk proteome, with the extent of alteration diminishing as lactation progresses into the mature milk phase.
| MSV000095983 | MassIVE
Project description:Metabolic syndrome (MetS) is an established predisposing condition for type 2 diabetes mellitus (T2DM). However, it is not thoroughly evaluated whether MetS increases the risk of T2DM in women with a previous history of gestational diabetes mellitus (GDM) who already at high risk of T2DM compared with the general population. We investigated the impact of MetS on the development of postpartum diabetes in women with a history of GDM.This was a multicenter, prospective cohort study of women diagnosed with GDM. The follow-up evaluations, including the oral glucose tolerance test, were completed at 6 weeks postpartum and annually thereafter. MetS was diagnosed at the initial postpartum evaluation according to the revised criteria of the National Cholesterol Education Program-Adult Treatment Panel III. The risk of developing type 2 diabetes (T2DM) in the follow-up period was analyzed based on the presence of MetS, and the adjusted risk was calculated using a Cox proportional hazards model.A total of 412 women without diabetes at the initial postpartum evaluation participated in the annual follow-up for median 3.8 years. MetS was prevalent in 66 (19.2%) women at the initial postpartum evaluation. The incidences of diabetes in women with and without MetS were 825 and 227 per 10,000 person-years, respectively (P?<?0.001). The presence of MetS was an independent risk factor for T2DM, with a hazard ratio (HR) of 2.23 (95% confidence interval 1.04-5.08) in multivariate analysis after adjustment for clinical and metabolic parameters. When we considered MetS and impaired fasting glucose (IFG) separately, women with MetS, IFG, or both had an increased risk of T2DM, with HRs of 4.17, 4.36, and 6.98, respectively.The presence of MetS during the early postpartum period is an independent risk factor for the development of T2DM in women with a previous history of GDM.
| S-EPMC5008552 | biostudies-other
Project description:Epidemiological and physiological similarities among Gestational Diabetes Mellitus (GDM) and Type 2 Diabetes (T2D) suggest that both diseases, share a common genetic background. T2D risk variants have been associated to GDM susceptibility. However, the genetic architecture of GDM is not yet completely understood. We analyzed 176 SNPs for 115 loci previously associated to T2D, GDM and body mass index (BMI), as well as a set of 118 Ancestry Informative Markers (AIMs), in 750 pregnant Mexican women. Association with GDM was found for two of the most frequently replicated T2D loci: a TCF7L2 haplotype (CTTC: rs7901695, rs4506565, rs7903146, rs12243326; P=2.16 x 10(-06); OR=2.95) and a KCNQ1 haplotype (TTT: rs2237892, rs163184, rs2237897; P=1.98 x 10(-05); OR=0.55). In addition, we found two loci associated to glycemic traits: CENTD2 (60' OGTT glycemia: rs1552224, P=0.03727) and MTNR1B (HOMA B: rs1387153, P=0.05358). Remarkably, a major susceptibility SLC16A11 locus for T2D in Mexicans was not shown to play a role in GDM risk. The fact that two of the main T2D associated loci also contribute to the risk of developing GDM in Mexicans, confirm that both diseases share a common genetic background. However, lack of association with a Native American contribution T2D risk haplotype, SLC16A11, suggests that other genetic mechanisms may be in play for GDM.
| S-EPMC4431878 | biostudies-literature
Project description:Gestational diabetes mellitus (GDM) is associated with fetal macrosomia and maternal postpartum dysglycemia, insulin resistance, and ?-cell dysfunction. Indeed, in practice, a prior pregnancy that resulted in a large-for-gestational-age (LGA) delivery is often considered presumptive evidence of GDM, whether or not it was diagnosed at the time. If this clinical assumption is correct, however, we would expect these women to exhibit postpartum metabolic dysfunction. Thus, to test this hypothesis, we assessed metabolic function during and after pregnancy in a cohort of women stratified according to the presence/absence of GDM and LGA delivery, respectively.A total of 562 women underwent metabolic characterization, including oral glucose tolerance test (OGTT), in late pregnancy and at 3 months' postpartum. The women were stratified into three groups: those with neither GDM nor LGA delivery (nonGDM, n = 364), those without GDM but with LGA delivery (nonGDM-LGA, n = 46), and those with GDM (n = 152).On logistic regression, GDM predicted postpartum glucose intolerance (OR 4.1 [95% CI 2.5-6.8]; P < 0.0001), whereas nonGDM-LGA did not (P = 0.65). At 3 months' postpartum, the mean adjusted levels of fasting glucose and area under the glucose curve on the OGTT were significantly higher in the GDM women compared with either nonGDM or nonGDM-LGA (all P < 0.05), with no differences between the latter two groups. In a similar manner, mean adjusted insulin sensitivity (Matsuda index) and ?-cell function (Insulin Secretion-Sensitivity Index-2) were lower in GDM women compared with either nonGDM or nonGDM-LGA (all P < 0.05), again with no differences between the latter two groups.Women with nonGDM-LGA do not exhibit postpartum metabolic dysfunction, arguing against the assumption of undiagnosed GDM in these patients.
| S-EPMC3220842 | biostudies-other
Project description:Gestational diabetes mellitus (GDM) is one of the most prevalent obstetric conditions, particularly among women with obesity. Pathways to hyperglycaemia remain obscure and a better understanding of the pathophysiology would facilitate early detection and targeted intervention. Among obese women from the UK Pregnancies Better Eating and Activity Trial (UPBEAT), we aimed to compare metabolic profiles early and mid-pregnancy in women identified as high-risk of developing GDM, stratified by GDM diagnosis. Using a GDM prediction model combining maternal age, mid-arm circumference, systolic blood pressure, glucose, triglycerides and HbA1c, 231 women were identified as being at higher-risk, of whom 119 women developed GDM. Analyte data (nuclear magnetic resonance and conventional) were compared between higher-risk women who developed GDM and those who did not at timepoint 1 (15+0-18+6 weeks) and at timepoint 2 (23+2-30+0 weeks). The adjusted regression analyses revealed some differences in the early second trimester between those who developed GDM and those who did not, including lower adiponectin and glutamine concentrations, and higher C-peptide concentrations (FDR-adjusted p < 0.005, < 0.05, < 0.05 respectively). More differences were evident at the time of GDM diagnosis (timepoint 2) including greater impairment in β-cell function (as assessed by HOMA2-%B), an increase in the glycolysis-intermediate pyruvate (FDR-adjusted p < 0.001, < 0.05 respectively) and differing lipid profiles. The liver function marker γ-glutamyl transferase was higher at both timepoints (FDR-adjusted p < 0.05). This exploratory study underlines the difficulty in early prediction of GDM development in high-risk women but adds to the evidence that among pregnant women with obesity, insulin secretory dysfunction may be an important discriminator for those who develop GDM.
| S-EPMC9612230 | biostudies-literature
Project description:BackgroundNew technologies have given rise to an abundance of -omics data, particularly metabolomic data. The scale of these data introduces new challenges for the interpretation and extraction of knowledge, requiring the development of innovative computational visualization methodologies. Here, we present GEM-Vis, an original method for the visualization of time-course metabolomic data within the context of metabolic network maps. We demonstrate the utility of the GEM-Vis method by examining previously published data for two cellular systems-the human platelet and erythrocyte under cold storage for use in transfusion medicine.ResultsThe results comprise two animated videos that allow for new insights into the metabolic state of both cell types. In the case study of the platelet metabolome during storage, the new visualization technique elucidates a nicotinamide accumulation that mirrors that of hypoxanthine and might, therefore, reflect similar pathway usage. This visual analysis provides a possible explanation for why the salvage reactions in purine metabolism exhibit lower activity during the first few days of the storage period. The second case study displays drastic changes in specific erythrocyte metabolite pools at different times during storage at different temperatures.ConclusionsThe new visualization technique GEM-Vis introduced in this article constitutes a well-suitable approach for large-scale network exploration and advances hypothesis generation. This method can be applied to any system with data and a metabolic map to promote visualization and understand physiology at the network level. More broadly, we hope that our approach will provide the blueprints for new visualizations of other longitudinal -omics data types. The supplement includes a comprehensive user's guide and links to a series of tutorial videos that explain how to prepare model and data files, and how to use the software SBMLsimulator in combination with further tools to create similar animations as highlighted in the case studies.
| S-EPMC7119163 | biostudies-literature
Project description:Introduction: This study aimed to investigate the association between components of metabolic syndrome (MetS) at first trimester and development of Gestational diabetes mellitus (GDM) in 498 Saudi pregnant women. Materials and Methods: Biochemical and anthropometric parameters were determined at the first trimester and MetS components were defined. Participants were screened for GDM at follow up according to International Association of Diabetes and Pregnancy Study Group (IADPSG) criteria. The main outcome measures were development of GDM and GDM risk vs. MetS components at first trimester. Results: One hundred twenty three (24.7%) were diagnosed with GDM according to IADPSG criteria. GDM risk was significantly higher for participants with hypertriglyceridemia at 1st trimester even after adjusting for age, BMI and parity (OR: 1.82; CI: 1.1-3.7, p = 0.04). Furthermore, the odds of hyperglycemia at 1st trimester was significantly higher in GDM than in non-GDM participants even after adjustments (OR: 2.13, 95% CI: 1.1 to 4.3, p = 0.038). The receiver operating characteristic (ROC) for predicting GDM revealed an area under the curve (AUC) of 0.69 (95% CI: 0.64 to 0.74, p < 0.001) and 0.71 (95% CI: 0.65 to 0.77, p < 0.001) for first-trimester hyperglycemia and hypertriglyceridemia respectively. Conclusions: The incidence of GDM in Saudi pregnant women was strongly associated with hyperglycemia and hypertriglyceridemia at first trimester. These findings are of clinical importance, as an assessment of MetS in early pregnancy can identify women at higher risk of developing GDM.
| S-EPMC7056831 | biostudies-literature