Pregnancy to postpartum transition of serum metabolites in women with gestational diabetes

Postpartum defects in inflammatory response after gestational diabetes precede progression to type 2 diabetes: a nested case-control study within the SWIFT study

BACKGROUND

Gestational diabetes (GDM) is a distinctive form of diabetes that first presents in pregnancy. While most women return to normoglycemia after delivery, they are nearly ten times more likely to develop type 2 diabetes than women with uncomplicated pregnancies. Current prevention strategies remain limited due to our incomplete understanding of the early underpinnings of progression.

AIM

To comprehensively characterize the postpartum profiles of women shortly after a GDM pregnancy and identify key mechanisms responsible for the progression to overt type 2 diabetes using multi-dimensional approaches.

METHODS

We conducted a nested case-control study of 200 women from the Study of Women, Infant Feeding and Type 2 Diabetes After GDM Pregnancy (SWIFT) to examine biochemical, proteomic, metabolomic, and lipidomic profiles at 6-9 weeks postpartum (baseline) after a GDM pregnancy. At baseline and annually up to two years, SWIFT administered research 2-hour 75-gram oral glucose tolerance tests. Women who developed incident type 2 diabetes within four years of delivery (incident case group, n = 100) were pair-matched by age, race, and pre-pregnancy body mass index to those who remained free of diabetes for at least 8 years (control group, n = 100). Correlation analyses were used to assess and integrate relationships across profiling platforms.

RESULTS

At baseline, all 200 women were free of diabetes. The case group was more likely to present with dysglycemia (e.g., impaired fasting glucose levels, glucose tolerance, or both). We also detected differences between groups across all omic platforms. Notably, protein profiles revealed an underlying inflammatory response with perturbations in protease inhibitors, coagulation components, extracellular matrix components, and lipoproteins, whereas metabolite and lipid profiles implicated disturbances in amino acids and triglycerides at individual and class levels with future progression. We identified significant correlations between profile features and fasting plasma insulin levels, but not with fasting glucose levels. Additionally, specific cross-omic relationships, particularly among proteins and lipids, were accentuated or activated in the case group but not the control group.

CONCLUSIONS

Overall, we applied orthogonal, complementary profiling techniques to uncover an inflammatory response linked to elevated triglyceride levels shortly after a GDM pregnancy, which is more pronounced in women who progress to overt diabetes.

Mass spectrometry-based "omics" technologies for the study of gestational diabetes and the discovery of new biomarkers

Gestational diabetes (GDM) is one of the most common complications occurring during pregnancy. Diagnosis is performed by oral glucose tolerance test, but harmonized testing methods and thresholds are still lacking worldwide. Short-term and long-term effects include obesity, type 2 diabetes, and increased risk of cardiovascular disease. The identification and validation of sensitidve, selective, and robust biomarkers for early diagnosis during the first trimester of pregnancy are required, as well as for the prediction of possible adverse outcomes after birth. Mass spectrometry (MS)-based omics technologies are nowadays the method of choice to characterize various pathologies at a molecular level. Proteomics and metabolomics of GDM were widely investigated in the last 10 years, and various proteins and metabolites were proposed as possible biomarkers. Metallomics of GDM was also reported, but studies are limited in number. The present review focuses on the description of the different analytical methods and MS-based instrumental platforms applied to GDM-related omics studies. Preparation procedures for various biological specimens are described and results are briefly summarized. Generally, only preliminary findings are reported by current studies and further efforts are required to determine definitive GDM biomarkers.

Proteins and metabolites fingerprints of gestational diabetes mellitus forming protein-metabolite interactomes are its potential biomarkers

Gestational diabetes mellitus (GDM) is a consequence of glucose intolerance with an inadequate production of insulin that happens during pregnancy and leads to adverse health consequences for both mother and fetus. GDM patients are at higher risk for preeclampsia, and developing diabetes mellitus type 2 in later life, while the child born to GDM mothers are more prone to macrosomia, and hypoglycemia. The universally accepted diagnostic criteria for GDM are lacking, therefore there is a need for a diagnosis of GDM that can identify GDM at its early stage (first trimester). We have reviewed the literature on proteins and metabolites fingerprints of GDM. Further, we have performed protein-protein, metabolite-metabolite, and protein-metabolite interaction network studies on GDM proteins and metabolites fingerprints. Notably, some proteins and metabolites fingerprints are forming strong interaction networks at high confidence scores. Therefore, we have suggested that those proteins and metabolites that are forming protein-metabolite interactomes are the potential biomarkers of GDM. The protein-metabolite biomarkers interactome may help in a deep understanding of the prognosis, pathogenesis of GDM, and also detection of GDM. The protein-metabolites interactome may be further applied in planning future therapeutic strategies to promote long-term health benefits in GDM mothers and their children.

An early prediction model for gestational diabetes mellitus based on metabolomic biomarkers

BACKGROUND

Gestational diabetes mellitus (GDM) represents the main metabolic alteration during pregnancy. The available methods for diagnosing GDM identify women when the disease is established, and pancreatic beta-cell insufficiency has occurred.The present study aimed to generate an early prediction model (under 18 weeks of gestation) to identify those women who will later be diagnosed with GDM.

METHODS

A cohort of 75 pregnant women was followed during gestation, of which 62 underwent normal term pregnancy and 13 were diagnosed with GDM. Targeted metabolomics was used to select serum biomarkers with predictive power to identify women who will later be diagnosed with GDM.

RESULTS

Candidate metabolites were selected to generate an early identification model employing a criterion used when performing Random Forest decision tree analysis. A model composed of two short-chain acylcarnitines was generated: isovalerylcarnitine (C5) and tiglylcarnitine (C5:1). An analysis by ROC curves was performed to determine the classification performance of the acylcarnitines identified in the study, obtaining an area under the curve (AUC) of 0.934 (0.873-0.995, 95% CI). The model correctly classified all cases with GDM, while it misclassified ten controls as in the GDM group. An analysis was also carried out to establish the concentrations of the acylcarnitines for the identification of the GDM group, obtaining concentrations of C5 in a range of 0.015-0.25 μmol/L and of C5:1 with a range of 0.015-0.19 μmol/L.

CONCLUSION

Early pregnancy maternal metabolites can be used to screen and identify pregnant women who will later develop GDM. Regardless of their gestational body mass index, lipid metabolism is impaired even in the early stages of pregnancy in women who develop GDM.

Gestational diabetes is driven by microbiota-induced inflammation months before diagnosis

OBJECTIVE

Gestational diabetes mellitus (GDM) is a condition in which women without diabetes are diagnosed with glucose intolerance during pregnancy, typically in the second or third trimester. Early diagnosis, along with a better understanding of its pathophysiology during the first trimester of pregnancy, may be effective in reducing incidence and associated short-term and long-term morbidities.

DESIGN

We comprehensively profiled the gut microbiome, metabolome, inflammatory cytokines, nutrition and clinical records of 394 women during the first trimester of pregnancy, before GDM diagnosis. We then built a model that can predict GDM onset weeks before it is typically diagnosed. Further, we demonstrated the role of the microbiome in disease using faecal microbiota transplant (FMT) of first trimester samples from pregnant women across three unique cohorts.

RESULTS

We found elevated levels of proinflammatory cytokines in women who later developed GDM, decreased faecal short-chain fatty acids and altered microbiome. We next confirmed that differences in GDM-associated microbial composition during the first trimester drove inflammation and insulin resistance more than 10 weeks prior to GDM diagnosis using FMT experiments. Following these observations, we used a machine learning approach to predict GDM based on first trimester clinical, microbial and inflammatory markers with high accuracy.

CONCLUSION

GDM onset can be identified in the first trimester of pregnancy, earlier than currently accepted. Furthermore, the gut microbiome appears to play a role in inflammation-induced GDM pathogenesis, with interleukin-6 as a potential contributor to pathogenesis. Potential GDM markers, including microbiota, can serve as targets for early diagnostics and therapeutic intervention leading to prevention.

Prepregnancy Protein Source and BCAA Intake Are Associated with Gestational Diabetes Mellitus in the CARDIA Study

Diet quality and protein source are associated with type 2 diabetes, however relationships with GDM are less clear. This study aimed to determine whether prepregnancy diet quality and protein source are associated with gestational diabetes mellitus (GDM). Participants were 1314 Black and White women without diabetes, who had at least one birth during 25 years of follow-up in the Coronary Artery Risk Development in Young Adults (CARDIA) cohort study. The CARDIA A Priori Diet Quality Score (APDQS) was assessed in the overall cohort at enrollment and again at Year 7. Protein source and branched-chain amino acid (BCAA) intake were assessed only at the Year 7 exam (n = 565). Logistic regression analysis was used to determine associations between prepregnancy dietary factors and GDM. Women who developed GDM (n = 161) were more likely to have prepregnancy obesity and a family history of diabetes (p < 0.05). GDM was not associated with prepregnancy diet quality at enrollment (Year 0) (odds ratio [OR]: 1.01; 95% confidence interval [CI] 0.99, 1.02) or Year 7 (odds ratio [OR]: 0.97; 95% confidence interval [CI] 0.94, 1.00) in an adjusted model. Conversely, BCAA intake (OR:1.59, 95% CI 1.03, 2.43) and animal protein intake (OR: 1.06, 95% CI 1.02, 1.10) as a proportion of total protein intake, were associated with increased odds of GDM, while proportion of plant protein was associated with decreased odds of GDM (OR: 0.95, 95% CI 0.91, 0.99). In conclusion, GDM is strongly associated with source of prepregnancy dietary protein intake but not APDQS in the CARDIA study.

Plasma Metabolomic Signature of Early Abuse in Middle-Aged Women

OBJECTIVE

Metabolomic profiling may provide insights into biological mechanisms underlying the strong epidemiologic links observed between early abuse and cardiometabolic disorders in later life.

METHODS

We examined the associations between early abuse and midlife plasma metabolites in two nonoverlapping subsamples from the Nurses' Health Study II, comprising 803 (mean age = 40 years) and 211 women (mean age = 61 years). Liquid chromatography-tandem mass spectrometry assays were used to measure metabolomic profiles, with 283 metabolites consistently measured in both subsamples. Physical and sexual abuse before age 18 years was retrospectively assessed by validated questions integrating type/frequency of abuse. Analyses were conducted in each sample and pooled using meta-analysis, with multiple testing adjustment using the q value approach for controlling the positive false discovery rate.

RESULTS

After adjusting for age, race, menopausal status, body size at age 5 years, and childhood socioeconomic indicators, more severe early abuse was consistently associated with five metabolites at midlife (q value < 0.20 in both samples), including lower levels of serotonin and C38:3 phosphatidylethanolamine plasmalogen and higher levels of alanine, proline, and C40:6 phosphatidylethanolamine. Other metabolites potentially associated with early abuse (q value < 0.05 in the meta-analysis) included triglycerides, phosphatidylcholine plasmalogens, bile acids, tyrosine, glutamate, and cotinine. The association between early abuse and midlife metabolomic profiles was partly mediated by adulthood body mass index (32% mediated) and psychosocial distress (13%-26% mediated), but not by other life-style factors.

CONCLUSIONS

Early abuse was associated with distinct metabolomic profiles of multiple amino acids and lipids in middle-aged women. Body mass index and psychosocial factors in adulthood may be important intermediates for the observed association.

Early markers of gestational diabetes mellitus: what we know and which way forward?

Women’s metabolism during pregnancy undergoes numerous changes that can lead to gestational diabetes mellitus (GDM). The cause and pathogenesis of GDM, a heterogeneous disease, are not completely clear, but GDM is increasing in prevalence and is associated with the modern lifestyle. Most diagnoses of GDM are made via the guidelines from the International Association of Diabetes and Pregnancy Study Groups (IADSPG), which involve an oral glucose tolerance test (OGTT) between 24 and 28 weeks of pregnancy. Diagnosis in this stage of pregnancy can lead to short- and long-term implications for the mother and child. Therefore, there is an urgent need for earlier GDM markers in order to enable prevention and earlier treatment. Routine GDM biomarkers (plasma glucose, insulin, C-peptide, homeostatic model assessment of insulin resistance, and sex hormone-binding globulin) can differentiate between healthy pregnant women and those with GDM but are not suitable for early GDM diagnosis. In this article, we present an overview of the potential early biomarkers for GDM that have been investigated recently. We also present our view of future developments in the laboratory diagnosis of GDM.

Branched-chain amino acids, history of gestational diabetes, and breastfeeding: The Bogalusa Heart Study

BACKGROUND AND AIMS

To examine the associations between history of gestational diabetes mellitus (GDM) and breastfeeding with branched-chain amino acids (BCAA) and their metabolites in later life.

METHODS AND RESULTS

638 women (mean age 48.0 y) who had participated in the Bogalusa Heart Study and substudies of pregnancy history had untargeted, ultrahigh performance liquid chromatography-tandem mass spectroscopy conducted by Metabolon© on serum samples. Metabolites were identified that were BCAA or associated with BCAA metabolic pathways. History of GDM at any pregnancy (self-reported, confirmed with medical records when possible) as well as breastfeeding were examined as predictors of BCAA using linear models, controlling for age, race, BMI, waist circumference, and menopausal status. None of the BCAA differed statistically by history of either GDM or breastfeeding, although absolute levels of each of the BCAA were higher with GDM and lower with breastfeeding. Of the 27 metabolites on the leucine, isoleucine and valine metabolism subpathway, 1-carboxyethylleucine, 1-carboxyethyvaline, and 3-hydroxy-2-ethylpropionate were higher in women with a history of GDM, but lower in women in women with a history of breastfeeding. Similar results were found for alpha-hydroxyisocaproate, 1-carboxyethylisoleucine, and N-acetylleucine.

CONCLUSIONS

GDM and breastfeeding are associated in opposite directions with several metabolites on the BCAA metabolic pathway.

Associations of Arginine with Gestational Diabetes Mellitus in a Follow-Up Study

In the reported study we applied the targeted metabolomic profiling employing high pressure liquid chromatography coupled with triple quadrupole tandem mass spectrometry (HPLC–MS/MS) to understand the pathophysiology of gestational diabetes mellitus (GDM), early identification of women who are at risk of developing GDM, and the differences in recovery postpartum between these women and normoglycemic women. We profiled the peripheral blood from patients during the second trimester of pregnancy and three months, and one year postpartum. In the GDM group Arg, Gln, His, Met, Phe and Ser were downregulated with statistical significance in comparison to normoglycemic (NGT) women. From the analysis of the association of all amino acid profiles of GDM and NGT women, several statistical models predicting diabetic status were formulated and compared with the literature, with the arginine-based model as the most promising of the screened ones (area under the curve (AUC) = 0.749). Our research results have shed light on the critical role of arginine in the development of GDM and may help in precisely distinguishing between GDM and NGT and earlier detection of GDM but also in predicting women with the increased type 2 diabetes mellitus (T2DM) risk.

Diabetes Leads to Alterations in Normal Metabolic Transitions of Pregnancy as Revealed by Time-Course Metabolomics

Women with diabetes during pregnancy are at increased risk of poor maternal and neonatal outcomes. Despite this, the effects of pre-gestational (PGDM) or gestational diabetes (GDM) on metabolism during pregnancy are not well understood. In this study, we utilized metabolomics to identify serum metabolic changes in women with and without diabetes during pregnancy and the cord blood at birth. We observed elevations in tricarboxylic acid (TCA) cycle intermediates, carbohydrates, ketones, and lipids, and a decrease in amino acids across gestation in all individuals. In early gestation, PGDM had elevations in branched-chain amino acids and sugars compared to controls, whereas GDM had increased lipids and decreased amino acids during pregnancy. In both GDM and PGDM, carbohydrate and amino acid pathways were altered, but in PGDM, hemoglobin A1c and isoleucine were significantly increased compared to GDM. Cord blood from GDM and PGDM newborns had similar increases in carbohydrates and choline metabolism compared to controls, and these alterations were not maternal in origin. Our results revealed that PGDM and GDM have distinct metabolic changes during pregnancy. A better understanding of diabetic metabolism during pregnancy can assist in improved management and development of therapeutics and help mitigate poor outcomes in both the mother and newborn.

Artificial Intelligence to Decode Cancer Mechanism: Beyond Patient Stratification for Precision Oncology

The multitude of multi-omics data generated cost-effectively using advanced high-throughput technologies has imposed challenging domain for research in Artificial Intelligence (AI). Data curation poses a significant challenge as different parameters, instruments, and sample preparations approaches are employed for generating these big data sets. AI could reduce the fuzziness and randomness in data handling and build a platform for the data ecosystem, and thus serve as the primary choice for data mining and big data analysis to make informed decisions. However, AI implication remains intricate for researchers/clinicians lacking specific training in computational tools and informatics. Cancer is a major cause of death worldwide, accounting for an estimated 9.6 million deaths in 2018. Certain cancers, such as pancreatic and gastric cancers, are detected only after they have reached their advanced stages with frequent relapses. Cancer is one of the most complex diseases affecting a range of organs with diverse disease progression mechanisms and the effectors ranging from gene-epigenetics to a wide array of metabolites. Hence a comprehensive study, including genomics, epi-genomics, transcriptomics, proteomics, and metabolomics, along with the medical/mass-spectrometry imaging, patient clinical history, treatments provided, genetics, and disease endemicity, is essential. Cancer Moonshot℠ Research Initiatives by NIH National Cancer Institute aims to collect as much information as possible from different regions of the world and make a cancer data repository. AI could play an immense role in (a) analysis of complex and heterogeneous data sets (multi-omics and/or inter-omics), (b) data integration to provide a holistic disease molecular mechanism, (c) identification of diagnostic and prognostic markers, and (d) monitor patient's response to drugs/treatments and recovery. AI enables precision disease management well beyond the prevalent disease stratification patterns, such as differential expression and supervised classification. This review highlights critical advances and challenges in omics data analysis, dealing with data variability from lab-to-lab, and data integration. We also describe methods used in data mining and AI methods to obtain robust results for precision medicine from "big" data. In the future, AI could be expanded to achieve ground-breaking progress in disease management.

Omics-based biomarkers in the diagnosis of diabetes

Diabetes mellitus (DM) is a group of metabolic diseases related to the dysfunction of insulin, causing hyperglycaemia and life-threatening complications. Current early screening and diagnostic tests for DM are based on changes in glucose levels and autoantibody detection. This review evaluates recent studies on biomarker candidates in diagnosing type 1, type 2 and gestational DM based on omics classification, whilst highlighting the relationship of these biomarkers with the development of diabetes, diagnostic accuracy, challenges and future prospects. In addition, it also focuses on possible non-invasive biomarker candidates besides common blood biomarkers.

Integrated Non-targeted and Targeted Metabolomics Uncovers Dynamic Metabolic Effects during Short-Term Abstinence in Methamphetamine Self-Administering Rats

Persistent neurochemical disturbances by repeating drug reward and withdrawal lead to addiction. Particularly, drug withdrawal, usually starting within hours of the last dose, is considered as a critical step in the transition to addiction and a treatment clue. The aim of this study was to uncover metabolic effects associated with methamphetamine (MA) short-term abstinence using both non-targeted and targeted metabolomics. Metabolic alterations were investigated in rat plasma collected immediately after 16 days of MA self-administration and after 12 and 24 h of abstinence. Principal component analysis revealed that the highest level of separation occurred between the 24 h and saline (control) groups based on the significantly changed ion features, 257/320/333 and 331/409/388, in the SA/12 h/24 h groups in positive and negative modes of UPLC-QTOF-ESI-MS, respectively. Targeted metabolomics revealed dynamic changes in the biosynthesis/metabolism of amino acids, including the phenylalanine, tyrosine, and tryptophan biosynthesis and the valine, leucine, and isoleucine biosynthesis. Integrating non-targeted and targeted metabolomics data uncovered rapid and distinct changes in the metabolic pathways involved in energy metabolism, the nervous system, and membrane lipid metabolism. These findings provide essential knowledge of the dynamic metabolic effects associated with short-term MA abstinence and may help identify early warning signs of MA dependence.

Metabolomics for predicting hyperglycemia in pregnancy: a protocol for a systematic review and potential meta-analysis

BACKGROUND

Hyperglycemia in pregnancy (HIP) has been recently differentiated between diabetes in pregnancy (DIP) and gestational diabetes mellitus (GDM). The proposed protocol is relevant, and clinical concern is due to the higher risk of adverse pregnancy outcomes (APO) and long-term effects on both the mother and the fetus. Fasting plasma glucose level (FPG) and oral glucose tolerance test (OGTT) are current diagnostic tools. However, controversy persists concerning diagnostic criteria, cut-off points, and even selective or universal screening. The objective of this systematic review is to assess the performance of metabolomic markers in the prediction of HIP.

METHODS

This is a protocol for a systematic review with potential meta-analysis. The primary outcome is GDM, defined as glucose intolerance identified in the second and third trimesters of pregnancy (any FPG ≥ 92 mg/dL and < 126 mg/dL OR when 75-g OGTT shows one altered value among these: FPG ≥ 92 mg/dL or 1-h post glucose load ≥ 180 mg/dL or 2-h post glucose load ≥ 153 mg/dL); the secondary outcome is HIP, defined as hyperglycemia detected in the first trimester of pregnancy (any FPG ≥ 126 mg/dL). A detailed systematic literature search will be carried out in electronic databases and conference abstracts, using the keywords "gestational diabetes mellitus," "metabolomics," "pregnancy," and "screening" (and their variations). We will include original peer-reviewed articles published from Jan 1, 1999, to Dec 31, 2018. Original studies including diabetes diagnosed before pregnancy (T2DM and T1DM), multiple pregnancies, and congenital malformations will be excluded. All results regarding samples, participant characteristics, metabolomic techniques, and diagnostic accuracy measures will be retrieved and analyzed. Since this is a systematic review, no ethical approval is necessary.

DISCUSSION

This systematic review may have the potential to provide significant evidence-based findings on the prediction performance of metabolomics. There are short and long-term repercussions for the mother and the newborn. Therefore, both may benefit from an accurate prediction technique for HIP.

SYSTEMATIC REVIEW REGISTRATION

This protocol was registered in the PROSPERO platform under number CRD42018100175 .

Association of Circulating Branched-Chain Amino Acids with Gestational Diabetes Mellitus: A Meta-Analysis

CONTEXT

Recently, the relationship between branched-chain amino acids (BCAAs) and diabetes mellitus (DM) has attracted worldwide attention. However, the results related to plasma BCAAs concentrations and gestational diabetes mellitus (GDM) lack statistical power due to the small sample size of a single article.

OBJECTIVES

This study quantitatively summarized current observational studies to evaluate the association between plasma BCAAs concentration levels and GDM.

METHODS

A systematic search was performed to select eligible publications using PubMed and EMBASE databases until July 23, 2018. The references of relevant articles were also manually searched. The quality evaluation of included studies was according to the guidelines of the Newcastle-Ottawa Scale (NOS). Data were analyzed with Review Manager 5.3 and STATA 14.0 software. In total, seven articles (including eight studies) involving 432 subjects were included.

RESULTS

The results showed that all three-individual plasma BCAAs concentration levels in the GDM group were higher than those in the control group (leucine: SMD = 3.76, 95% CI: 1.70 - 5.82, P (SMD) < 0.001; isoleucine: SMD = 3.15, 95% CI: 1.42 - 4.87, P (SMD) < 0.001; valine: SMD = 2.77, 95% CI: 1.21 - 4.32, P (SMD) = 0.001), and the differences were statistically significant. In addition, subgroup analysis indicated that age, body mass index (BMI), publication year, and ethnicity were positively associated with plasma BCAAs concentrations in GDM.

CONCLUSIONS

Plasma BCAAs, as potential biomarkers, might be associated with GDM risk, which provides useful information for the prevention and early diagnosis of GDM.

Gestational route to healthy birth (GaRBH): protocol for an Indian prospective cohort study

INTRODUCTION

Pregnancy is characterised by a high rate of metabolic shifts from early to late phases of gestation in order to meet the raised physiological and metabolic needs. This change in levels of metabolites is influenced by gestational weight gain (GWG), which is an important characteristic of healthy pregnancy. Inadequate/excessive GWG has short-term and long-term implications on maternal and child health. Exploration of gestational metabolism is required for understanding the quantitative changes in metabolite levels during the course of pregnancy. Therefore, our aim is to study trimester-specific variation in levels of metabolites in relation to GWG and its influence on fetal growth and newborn anthropometric traits at birth.

METHODS AND ANALYSIS

A prospective longitudinal study is planned (start date: February 2018; end date: March 2023) on pregnant women that are being recruited in the first trimester and followed in subsequent trimesters and at the time of delivery (total 3 follow-ups). The study is being conducted in a hospital located in Bikaner district (66% rural population), Rajasthan, India. The estimated sample size is of 1000 mother-offspring pairs. Information on gynaecological and obstetric history, socioeconomic position, diet, physical activity, tobacco and alcohol consumption, depression, anthropometric measurements and blood samples is being collected for metabolic assays in each trimester using standardised methods. Mixed effects regression models will be used to assess the role of gestational weight in influencing metabolite levels in each trimester. The association of maternal levels of metabolites with fetal growth, offspring's weight and body composition at birth will be investigated using regression modelling.

ETHICS AND DISSEMINATION

The study has been approved by the ethics committees of the Department of Anthropology, University of Delhi and Sardar Patel Medical College, Rajasthan. We are taking written informed consent after discussing the various aspects of the study with the participants in the local language.

Maternal obesity and gestational diabetes mellitus affect body composition through infancy: the PONCH study

BACKGROUND

To determine how maternal obesity or gestational diabetes mellitus (GDM) affect infant body size and body composition during the first year of life.

METHODS

Eighty three normal-weight (NW) women, 26 obese (OB) women, and 26 women with GDM were recruited during pregnancy. Infant body composition was determined by air-displacement plethysmography at 1 and 12 weeks, and anthropometric measurements made until 1 year of age.

RESULTS

Girl infants born to OB women and women with GDM had a higher body-fat percentage (BF%) at 1 and 12 weeks of age than girls born to NW women. Girls had higher BF% than boys in OB and GDM groups only. Maternal HbA1c and fasting plasma glucose correlated with girl infant BF% at 1 week of age. Maternal weight at start of pregnancy correlated with birthweight in NW and OB groups, but not the GDM group. OB group infants showed greater BMI increases from 1 week to 1 year than both NW and GDM group infants.

CONCLUSION

Results show that both maternal glycaemia and obesity are determinants of increased early life adiposity, especially in girls, with glycaemic levels being more influential than maternal weight for infants born to women with GDM.

Prediction and Prevention of Type 2 Diabetes in Women with a History of GDM

PURPOSE OF THE REVIEW

Women with a history of gestational diabetes mellitus (GDM) have an alarmingly high risk of developing type 2 diabetes (T2D); yet, mechanisms underlying this progression are largely unknown.

RECENT FINDINGS

Clinical characteristics of a GDM pregnancy and postpartum metabolomics may contribute to risk prediction of T2D to identify those women at highest risk of progression and need for intervention. Evidence for effective postpartum lifestyle interventions from observational studies include adherence to a healthy dietary pattern, increasing physical activity, and maintaining a healthy body weight. Larger clinical trials with greater participant engagement are warranted to confirm the effectiveness of lifestyle interventions in women with recent GDM. Research is needed to refine prediction models of T2D after GDM, and to determine the most effective strategies to delay or prevent T2D onset. Incorporating novel biomarkers in the postpartum period, such as metabolomics, could offer a powerful approach.

Higher Concentrations of BCAAs and 3-HIB Are Associated with Insulin Resistance in the Transition from Gestational Diabetes to Type 2 Diabetes

AIM

Determine the metabolic profile and identify risk factors of women transitioning from gestational diabetes mellitus (GDM) to type 2 diabetes mellitus (T2DM).

METHODS

237 women diagnosed with GDM underwent an oral glucose tolerance test (OGTT), anthropometrics assessment, and completed lifestyle questionnaires six years after pregnancy. Blood was analysed for clinical variables (e.g., insulin, glucose, HbA1c, adiponectin, leptin, and lipid levels) and NMR metabolomics. Based on the OGTT, women were divided into three groups: normal glucose tolerance (NGT), impaired glucose tolerance (IGT), and T2DM.

RESULTS

Six years after GDM, 19% of subjects had T2DM and 19% IGT. After BMI adjustment, the IGT group had lower HDL, higher leptin, and higher free fatty acid (FFA) levels, and the T2DM group higher triglyceride, FFA, and C-reactive protein levels than the NGT group. IGT and T2DM groups reported lower physical activity. NMR measurements revealed that levels of branched-chain amino acids (BCAAs) and the valine metabolite 3-hydroxyisobyturate were higher in T2DM and IGT groups and correlated with measures of insulin resistance and lipid metabolism.

CONCLUSION

In addition to well-known clinical risk factors, BCAAs and 3-hydroxyisobyturate are potential markers to be evaluated as predictors of metabolic risk after pregnancy complicated by GDM.

Analytical challenges of untargeted GC-MS-based metabolomics and the critical issues in selecting the data processing strategy

Background: A challenge of metabolomics is data processing the enormous amount of information generated by sophisticated analytical techniques. The raw data of an untargeted metabolomic experiment are composited with unwanted biological and technical variations that confound the biological variations of interest. The art of data normalisation to offset these variations and/or eliminate experimental or biological biases has made significant progress recently. However, published comparative studies are often biased or have omissions. Methods: We investigated the issues with our own data set, using five different representative methods of internal standard-based, model-based, and pooled quality control-based approaches, and examined the performance of these methods against each other in an epidemiological study of gestational diabetes using plasma. Results: Our results demonstrated that the quality control-based approaches gave the highest data precision in all methods tested, and would be the method of choice for controlled experimental conditions. But for our epidemiological study, the model-based approaches were able to classify the clinical groups more effectively than the quality control-based approaches because of their ability to minimise not only technical variations, but also biological biases from the raw data. Conclusions: We suggest that metabolomic researchers should optimise and justify the method they have chosen for their experimental condition in order to obtain an optimal biological outcome.