An Unbiased Lipidomics Approach Identifies Early Second Trimester Lipids Predictive of Maternal Glycemic Traits and Gestational Diabetes Mellitus

Metabolic profiling and early prediction models for gestational diabetes mellitus in PCOS and non-PCOS pregnant women

BACKGROUND

Gestational diabetes mellitus (GDM) is the most common pregnancy complication, significantly affecting maternal and neonatal health. Polycystic ovary syndrome (PCOS) is a common endocrine disorder characterized by metabolic abnormalities, which notably elevates the risk of developing GDM during pregnancy.

METHODS

In this study, we utilized ultra-high-performance liquid chromatography for untargeted metabolomics analysis of serum samples from 137 pregnant women in the early-to-mid-pregnancy. The cohort consisted of 137 participants, including 70 in the PCOS group (36 who developed GDM in mid-to-late pregnancy and 34 who did not) and 67 in the non-PCOS group (37 who developed GDM and 30 who remained GDM-free). The aim was to investigate metabolic profile differences between PCOS and non-PCOS patients and to construct early GDM prediction models separately for the PCOS and non-PCOS groups.

RESULTS

Our findings revealed significant differences in the metabolic profiles of PCOS patients, which may help elucidate the higher risk of GDM in the PCOS population. Moreover, tailored early GDM prediction models for the PCOS group demonstrated high predictive performance, providing strong support for early diagnosis and intervention in clinical practice.

CONCLUSIONS

Untargeted metabolomics analysis revealed distinct metabolic patterns between PCOS patients and non-PCOS patients, particularly in pathways related to GDM. Based on these findings, we successfully constructed GDM prediction models for both PCOS and non-PCOS groups, offering a promising tool for clinical management and early intervention in high-risk populations.

Metabolomic profiling reveals early biomarkers of gestational diabetes mellitus and associated hepatic steatosis

BACKGROUND

This study aims to identify early metabolomic biomarkers of gestational diabetes mellitus (GDM) and evaluate their association with hepatic steatosis.

METHODS

We compared maternal serum metabolomic profiles between women who developed GDM (n = 118) and matched controls (n = 118) during the first (10-14 gestational weeks) and second (24-28 gestational weeks) trimesters using ultra-performance liquid chromatography coupled with mass spectrometry. Mediation analysis was performed to evaluate the mediating role of metabolic dysfunction-associated steatotic liver disease (MASLD) in the relationship between metabolites and subsequent development of GDM. A refined prediction model was developed to predict GDM using established clinical factors and selected metabolites.

RESULTS

Significant alterations in circulating metabolites, including amino acids, bile acids, and phospholipids, were observed in the GDM group compared to controls during early pregnancy. Mediation analysis revealed that several metabolites, including glycocholic acid (proportion mediated (PM) = 31.9%), butanoyl carnitine (PM = 25.7%), and uric acid (PM = 22.4%), had significant indirect effects on GDM incidence mediated by hepatic steatosis. The refined prediction model composed of clinical factors and selected metabolites in the first trimester demonstrated higher performance in predicting GDM development than the established prediction model composed solely of clinical factors (AUC, 0.85 vs. 0.63, p < 0.001).

CONCLUSIONS

Women who developed GDM exhibited altered metabolomic profiles from early pregnancy, which showed a significant correlation with GDM, with MASLD as a mediator. Selected metabolomic biomarkers may serve as predictive markers and potential targets for early risk assessment and intervention in GDM.

RESEARCH INSIGHTS

WHAT IS CURRENTLY KNOWN ABOUT THIS TOPIC?: Gestational diabetes mellitus (GDM) is a common pregnancy complication with significant health risks. Early identification of women at high risk for GDM is crucial for timely intervention and improved outcomes. WHAT IS THE KEY RESEARCH QUESTION?: What alterations in circulating metabolites during early pregnancy are associated with subsequent GDM development? Does metabolic dysfunction-associated steatotic liver disease (MASLD) mediate the association between specific metabolites and GDM risk? WHAT IS NEW?: Significant alterations in bile acids, amino acids, phosphatidylethanolamines, and phosphatidylinositols were observed in early pregnancy sera of women who later developed GDM. MASLD significantly mediated the effects of several metabolites on GDM risk, with mediation proportions ranging from 9.7 to 31.9%. A refined prediction model composed of clinical factors and metabolites significantly improved the performance in predicting GDM development. HOW MIGHT THIS STUDY INFLUENCE CLINICAL PRACTICE?: These results provide new insights into early metabolic alterations associated with GDM development and highlight the potential mediating role of MASLD. This comprehensive metabolomic approach may contribute to the development of improved risk prediction models and targeted interventions for GDM prevention.

Maternal lipids in overweight and obesity: implications for pregnancy outcomes and offspring's body composition

Overweight and obesity (OWO) are linked to dyslipidemia and low-grade chronic inflammation, which is fueled by lipotoxicity and oxidative stress. In the context of pregnancy, maternal OWO has long been known to negatively impact on pregnancy outcomes and maternal health, as well as to imprint a higher risk for diseases in offspring later in life. Emerging research suggests that individual lipid metabolites, which collectively form the lipidome, may play a causal role in the pathogenesis of OWO-related diseases. This can be applied to the onset of pregnancy complications such as gestational diabetes mellitus (GDM) and hypertensive disorders of pregnancy (HDP), which in fact occur more frequently in women affected by OWO. In this review, we summarize current knowledge on maternal lipid metabolites in pregnancy and highlight associations between the maternal lipidome and the risk to develop GDM, HDP and childhood OWO. Emerging data underpin that dysregulations in maternal triglyceride, phospholipid and polyunsaturated fatty acid (PUFA) metabolism may play a role in modulating the risk for adverse pregnancy outcomes and childhood OWO, but it is yet premature to convert currently available insights into clinical guidelines. Well-designed large-scale lipidomic studies, combined with translational approaches including animal models of obesity, will likely facilitate the recognition of underling pathways of OWO-related pregnancy complications and child's health outcomes, based on which clinical guidelines and recommendations can be updated.

Advancement in predictive biomarkers for gestational diabetes mellitus diagnosis and related outcomes: a scoping review

OBJECTIVE

Gestational diabetes mellitus (GDM) is a metabolic disorder associated with adverse maternal and neonatal outcomes. While GDM is diagnosed by oral glucose tolerance testing between 24-28 weeks, earlier prediction of risk of developing GDM via circulating biomarkers has the potential to risk-stratify women and implement targeted risk reduction before adverse obstetric outcomes. This scoping review aims to collate biomarkers associated with GDM development, associated perinatal outcome and medication requirement in GDM.

DESIGN

The Preferred Reporting Items for Systematic Reviews and Meta-Analysis extension for scoping reviews was used to guide the study.

DATA SOURCES

This review searched for articles on PubMed, Embase, Scopus, Cochrane Central Register of Controlled Trials, the Cumulative Index to Nursing and Allied Health Literature and the Web of Science from January 2013 to February 2023.

ELIGIBILITY CRITERIA

The eligibility criteria included analytical observational studies published in English, focusing on pregnant women with maternal plasma or serum biomarkers collected between 6 and 24 weeks of gestation. Studies were excluded if they evaluated drug effects, non-GDM diabetes types or involved twin pregnancies, microbiota, genetic analyses or non-English publications.

DATA EXTRACTION AND SYNTHESIS

Two independent reviewers extracted data. One reviewer extracted data from papers included in the scoping review using Covidence. From the 8837 retrieved records, 137 studies were included.

RESULTS

A total of 278 biomarkers with significant changes in individuals with GDM compared with controls were identified. The univariate predictive biomarkers exhibited insufficient clinical sensitivity and specificity for predicting GDM, perinatal outcomes, and the necessity of medication. Multivariable models combining maternal risk factors with biomarkers provided more accurate detection but required validation for use in clinical settings.

CONCLUSION

This review recommends further research integrating novel omics technology for building accurate models for predicting GDM, perinatal outcome, and the necessity of medication while considering the optimal testing time.

Dietary Regulation of Lipid Metabolism in Gestational Diabetes Mellitus: Implications for Fetal Macrosomia

The primary therapeutic approach for managing hyperglycemia today is diet therapy. Lipids are not only a source of nutrients but also play a role in initiating adipocyte differentiation in the fetus, which may explain the development of fetal macrosomia and future metabolic disorders in children born to mothers with gestational diabetes mellitus (GDM). Alterations in the maternal blood lipid profile, influenced by adherence to a healthy diet in mothers with GDM and the occurrence of fetal macrosomia, represent a complex and not fully understood process. The aim of this study was to examine the characteristics of the blood plasma lipid profile in pregnant women with GDM across all trimesters based on adherence to diet therapy. The clinical part of the study followed a case-control design, including 110 women: 80 in the control group, 20 in a GDM group adhering to the diet, and 10 in a GDM group not adhering to the diet. The laboratory part was conducted as a longitudinal dynamic study, with venous blood samples collected at three time points: 11-13, 24-26, and 30-32 weeks of pregnancy. A significant impact of diet therapy on the composition of blood lipids throughout pregnancy was demonstrated, starting as early as the first trimester. ROC analysis indicated high effectiveness of the models developed, with an AUC of 0.98 for the 30- to 32-week model and sensitivity and specificity values of 1 and 0.9, respectively. An association was found between dietary habits, maternal blood lipid composition at 32 weeks, and newborn weight. The changes in lipid profiles during macrosomia development and under diet therapy were found to be diametrically opposed, confirming at the molecular level that diet therapy can normalize not only carbohydrate metabolism but also lipid metabolism in both the mother and fetus. Based on the data obtained, it is suggested that after further validation, the developed models could be used to improve the prognosis of macrosomia by analyzing blood plasma lipid profiles at various stages of pregnancy.

Characteristics of Glucose-Lipid Metabolism in Early Pregnancy Among Overweight and Obese Women and Their Predictive Value for Gestational Diabetes Mellitus

Purpose

This study explores the link between women's pre-pregnancy overweight and obesity and glucose and lipid metabolism in their early pregnancy. It assesses how early pregnancy glucose and lipid levels predict gestational diabetes mellitus (GDM) risk, aiming to offer foundational weight management strategies for overweight and obese women to prevent GDM.

Patients and Methods

This study analyzed 2172 pregnant women from 2017 to 2021 at Waitan Street Community Health Service Center, Shanghai, monitoring early pregnancy (7-10 weeks) glucose and lipid levels (TG, TC, HDL-C, LDL-C, FBG, HbA1c) and 24-week OGTT values. Pre-pregnancy BMI categorized participants into overweight and obese, normal, and underweight groups. We compared early pregnancy glycemic and lipid metrics and GDM incidence across groups, examining the relationship between pre-pregnancy BMI and early pregnancy blood metrics. The overweight and obese cohort was further split into GDM and non-GDM groups, comparing early pregnancy glycolipid indicators and assessing their predictive value for GDM development.

Results

In the overweight and obese group, maternal FBG, HbA1c, TG, and LDL-C were higher, while HDL-C was lower than in normal and underweight groups (P<0.05), with a higher GDM incidence (P<0.05). Pre-pregnancy BMI positively correlated with FBG, HbA1c, TG, and LDL-C levels (r=0.556, 0.567, 0.686, 0.214; P<0.05) but not HDL-C. Each 1-unit BMI increase raised GDM risk by 0.204 times (P<0.05). FBG, TG, and LDL-C had high predictive accuracy for GDM in overweight and obese women, with AUCs of 0.991, 0.994, and 0.935, respectively.

Conclusion

Pre-pregnancy overweight and obesity can cause early pregnancy glucose and lipid abnormalities, raising GDM risk. Early testing in such women is a strong predictor for GDM.

Mendelian randomization analysis reveals causal effects of blood lipidome on gestational diabetes mellitus

BACKGROUND

Observational studies have revealed associations between maternal lipid metabolites and gestational diabetes mellitus (GDM). However, whether these associations are causal remain uncertain.

OBJECTIVE

To evaluate the causal relationship between lipid metabolites and GDM.

METHODS

A two-sample Mendelian randomization (MR) analysis was performed based on summary statistics. Sensitivity analyses, validation analyses and reverse MR analyses were conducted to assess the robustness of the MR results. Additionally, a phenome-wide MR (Phe-MR) analysis was performed to evaluate potential side effects of the targeted lipid metabolites.

RESULTS

A total of 295 lipid metabolites were included in this study, 29 of them had three or more instrumental variables (IVs) suitable for sensitivity analyses. The ratio of triglycerides to phosphoglycerides (TG_by_PG) was identified as a potential causal biomarker for GDM (inverse variance weighted (IVW) estimate: odds ratio (OR) = 2.147, 95% confidential interval (95% CI) 1.415-3.257, P = 3.26e-4), which was confirmed by validation and reverse MR results. Two other lipid metabolites, palmitoyl sphingomyelin (d18:1/16:0) (PSM(d18:1/16:0)) (IVW estimate: OR = 0.747, 95% CI 0.583-0.956, P = 0.021) and triglycerides in very small very low-density lipoprotein (XS_VLDL_TG) (IVW estimate: OR = 2.948, 95% CI 1.197-5.215, P = 0.015), were identified as suggestive potential biomarkers for GDM using a conventional cut-off P-value of 0.05. Phe-MR results indicated that lowering TG_by_PG had detrimental effects on two diseases but advantageous effects on the other 13 diseases.

CONCLUSION

Genetically predicted elevated TG_by_PG are causally associated with an increased risk of GDM. Side-effect profiles indicate that TG_by_PG might be a target for GDM prevention, though caution is advised due to potential adverse effects on other conditions.

Metabolic profiles in gestational diabetes mellitus can reveal novel biomarkers for prediction of adverse neonatal outcomes

Background

Gestational diabetes mellitus (GDM) significantly affects the fetal metabolic environment, elevating risks of neonatal hypoglycemia and macrosomia. Metabolomics offers promising avenues for early prediction and diagnosis of GDM and associated adverse offspring outcomes.

Methods

This study analyzed serum samples from pregnant women diagnosed with GDM at 24 to 28 weeks of gestation using untargeted metabolomics. We monitored the health outcomes of their offspring to explore the correlation between initial serum metabolite profiles and subsequent health outcomes, to uncover the predictive markers for hypoglycemia and macrosomia in these offspring.

Results

Out of 200 participants, 154 had normal newborns, 33 had offspring with hypoglycemia, and 19 had offspring with macrosomia. From 448 identified metabolites, 66 showed significant differences in cases of hypoglycemia, and 45 in macrosomia. A panel of serum metabolite biomarkers achieved Area Under the Curve (AUC) values of 0.8712 for predicting hypoglycemia and 0.9434 for macrosomia.

Conclusion

The study delineated metabolic disruptions in GDM during 24-28 weeks of gestation and pinpointed biomarkers capable of forecasting adverse neonatal outcomes. These findings could inform GDM management strategies and minimize the incidence of such outcomes.

Using metabolomics and proteomics to identify the potential urine biomarkers for prediction and diagnosis of gestational diabetes

Gestational diabetes mellitus (GDM) is one of the most common metabolic complications during pregnancy, threatening both maternal and fetal health. Prediction and diagnosis of GDM is not unified. Finding effective biomarkers for GDM is particularly important for achieving early prediction, accurate diagnosis and timely intervention. Urine, due to its accessibility in large quantities, noninvasive collection and easy preparation, has become a good sample for biomarker identification. In recent years, a number of studies using metabolomics and proteomics approaches have identified differential expressed urine metabolites and proteins in GDM patients. In this review, we summarized these potential urine biomarkers for GDM prediction and diagnosis and elucidated their role in development of GDM.

The role of lipid dysregulation in gestational diabetes mellitus: Early prediction and postpartum prognosis

Gestational diabetes mellitus (GDM) is a pathological condition during pregnancy characterized by impaired glucose tolerance, and the failure of pancreatic beta-cells to respond appropriately to an increased insulin demand. However, while the majority of women with GDM will return to normoglycemia after delivery, they have up to a seven times higher risk of developing type 2 diabetes during midlife, compared with those with no history of GDM. Gestational diabetes mellitus also increases the risk of multiple metabolic disorders, including non-alcoholic fatty liver disease, obesity, and cardiovascular diseases. Lipid metabolism undergoes significant changes throughout the gestational period, and lipid dysregulation is strongly associated with GDM and the progression to future type 2 diabetes. In addition to common lipid variables, discovery-based omics techniques, such as metabolomics and lipidomics, have identified lipid biomarkers that correlate with GDM. These lipid species also show considerable potential in predicting the onset of GDM and subsequent type 2 diabetes post-delivery. This review aims to update the current knowledge of the role that lipids play in the onset of GDM, with a focus on potential lipid biomarkers or metabolic pathways. These biomarkers may be useful in establishing predictive models to accurately predict the future onset of GDM and type 2 diabetes, and early intervention may help to reduce the complications associated with GDM.

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.

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.

The dynamic inflammatory profile of pregnancy can be monitored using a novel lipid-based mass spectrometry technique

The lipid environment changes throughout pregnancy both physiologically with emergent insulin resistance and pathologically e.g., gestational diabetes mellitus (GDM). Novel mass spectrometry (MS) techniques applied to minimally processed blood might lend themselves to monitoring changing lipid profiles to inform care decisions across pregnancy. In this study we use an intact-sandwich, MALDI-ToF MS method to identify phosphatidylcholine (PC) and lysophosphatidylcholine (LPC) species and calculate their ratio as an indicator of inflammation. Plasma and sera were prepared from venous blood of non-pregnant women (aged 18-40) and pregnant women at 16 weeks, 28 weeks (including GDM-positive women), and 37+ weeks (term) of gestation alongside umbilical cord blood (UCB). Women with a normal menstrual cycle and age-matched men provided finger-prick derived capillary sera at 6 time-points over a month. Serum rather than plasma was preferable for PC/LPC measurement. As pregnancy progresses, an anti-inflammatory phenotype dominates the maternal circulation, evidenced by increasing PC/LPC ratio. In contrast, the PC/LPC ratio of UCB was aligned to that of non-pregnant donors. BMI had no significant effect on the PC/LPC ratio, but GDM-complicated pregnancies had significantly lower PC/LPC at 16 weeks of gestation. To further translate the use of the PC/LPC ratio clinically, the utility of finger-prick blood was evaluated; no significant difference between capillary versus venous serum was found and we revealed the PC/LPC ratio oscillates with the menstrual cycle. Overall, we show that the PC/LPC ratio can be measured simply in human serum and has the potential to be used as a time-efficient and less invasive biomarker of (mal)adaptative inflammation.

Plasma lipidomic profiling reveals metabolic adaptations to pregnancy and signatures of cardiometabolic risk: a preconception and longitudinal cohort study

BACKGROUND

Adaptations in lipid metabolism are essential to meet the physiological demands of pregnancy and any aberration may result in adverse outcomes for both mother and offspring. However, there is a lack of population-level studies to define the longitudinal changes of maternal circulating lipids from preconception to postpartum in relation to cardiometabolic risk factors.

METHODS

LC-MS/MS-based quantification of 689 lipid species was performed on 1595 plasma samples collected at three time points in a preconception and longitudinal cohort, Singapore PREconception Study of long-Term maternal and child Outcomes (S-PRESTO). We mapped maternal plasma lipidomic profiles at preconception (N = 976), 26-28 weeks' pregnancy (N = 337) and 3 months postpartum (N = 282) to study longitudinal lipid changes and their associations with cardiometabolic risk factors including pre-pregnancy body mass index, body weight changes and glycaemic traits.

RESULTS

Around 56% of the lipids increased and 24% decreased in concentration in pregnancy before returning to the preconception concentration at postpartum, whereas around 11% of the lipids went through significant changes in pregnancy and their concentrations did not revert to the preconception concentrations. We observed a significant association of body weight changes with lipid changes across different physiological states, and lower circulating concentrations of phospholipids and sphingomyelins in pregnant mothers with higher pre-pregnancy BMI. Fasting plasma glucose and glycated haemoglobin (HbA1c) concentrations were lower whereas the homeostatic model assessment of insulin resistance (HOMA-IR), 2-h post-load glucose and fasting insulin concentrations were higher in pregnancy as compared to both preconception and postpartum. Association studies of lipidomic profiles with these glycaemic traits revealed their respective lipid signatures at three physiological states. Assessment of glycaemic traits in relation to the circulating lipids at preconception with a large sample size (n = 936) provided an integrated view of the effects of hyperglycaemia on plasma lipidomic profiles. We observed a distinct relationship of lipidomic profiles with different measures, with the highest percentage of significant lipids associated with HOMA-IR (58.9%), followed by fasting insulin concentration (56.9%), 2-h post-load glucose concentration (41.8%), HbA1c (36.7%), impaired glucose tolerance status (31.6%) and fasting glucose concentration (30.8%).

CONCLUSIONS

We describe the longitudinal landscape of maternal circulating lipids from preconception to postpartum, and a comprehensive view of trends and magnitude of pregnancy-induced changes in lipidomic profiles. We identified lipid signatures linked with cardiometabolic risk traits with potential implications both in pregnancy and postpartum life. Our findings provide insights into the metabolic adaptations and potential biomarkers of modifiable risk factors in childbearing women that may help in better assessment of cardiometabolic health, and early intervention at the preconception period.

TRIAL REGISTRATION

ClinicalTrials.gov, NCT03531658.

Maternal plasma diacylglycerols and triacylglycerols in the prediction of gestational diabetes mellitus

OBJECTIVE

To define the lipidomic profile in plasma across pregnancy, and identify lipid biomarkers for gestational diabetes mellitus (GDM) prediction in early pregnancy.

DESIGN

Case-control study.

SETTING

Tertiary referral maternity unit.

POPULATION OR SAMPLE

Plasma samples from 100 GDM and 100 normal glucose tolerance (NGT) women, divided into a training set (GDM first trimester = 50, GDM second trimester = 40, NGT first trimester = 50, NGT second trimester = 50) and a validation set (GDM first trimester = 45, GDM second trimester = 34, NGT first trimester = 44, NGT second trimester = 40).

METHODS

Plasma samples were collected in the first (11⁺⁰ to 13⁺⁶ weeks), second (19⁺⁰ to 24⁺⁶ weeks), and third trimesters (30⁺⁰ to 34⁺⁶ weeks), and tested by ultra-high-performance liquid chromatography coupled with electrospray ionisation-quadrupole-time of flight-mass spectrometry; The GDM prediction model was established by the machine-learning method of random forest.

MAIN OUTCOME MEASURES

Gestational diabetes mellitus.

RESULTS

In both the GDM and NGT group, lyso-glycerophospholipids were down-regulated, whereas ceramides, sphingomyelins, cholesteryl ester, diacylglycerols (DGs) and triacylglycerols (TGs) and glucosylceramide were up-regulated across the three trimesters of pregnancy. In the training dataset, seven TGs and five DGs demonstrated good performance in the prediction of GDM in the first and second trimesters (area under the curve [AUC] = 0.96 with 95% confidence interval [CI] of 0.93-1 and AUC = 0.97 with 95% CI of 0.95-1, respectively), independent of maternal body mass index (BMI) and ethnicity. In the validation dataset, the predictive model achieved an AUC of 0.88 and 0.94 at the first and second trimesters, respectively.

CONCLUSIONS

Our results have proposed new lipid biomarkers for the first trimester prediction of GDM, independent of ethnicity and BMI.

Lipidomics in gestational diabetes mellitus

PURPOSE OF REVIEW

Epidemiological and mechanistic studies have reported relationships between blood lipids, mostly measured by traditional method in clinical settings, and gestational diabetes mellitus (GDM). Recent advances of high-throughput lipidomics techniques have made available more comprehensive lipid profiling in biological samples. This review aims to summarize evidence from prospective studies in assessing relations between blood lipids and GDM, and discuss potential underlying mechanisms.

RECENT FINDINGS

Mass spectrometry and nuclear magnetic resonance spectroscopy-based analytical platforms are extensively used in lipidomics research. Epidemiological studies have identified multiple novel lipidomic biomarkers that are associated with risk of GDM, such as certain types of fatty acids, glycerolipids, glycerophospholipids, sphingolipids, cholesterol, and lipoproteins. However, the findings are inconclusive mainly due to the heterogeneities in study populations, sample sizes, and analytical platforms. Mechanistic evidence indicates that abnormal lipid metabolism may be involved in the pathogenesis of GDM by impairing pancreatic β-cells and inducing insulin resistance through several etiologic pathways, such as inflammation and oxidative stress.

SUMMARY

Lipidomics is a powerful tool to study pathogenesis and biomarkers for GDM. Lipidomic biomarkers and pathways could help to identify women at high risk for GDM and could be potential targets for early prevention and intervention of GDM.

Nontargeted metabolomics reveals the potential mechanism underlying the association between birthweight and metabolic disturbances

AIMS

The aim of this study was to characterize the metabolites associated with small- and large-gestational-age newborns in maternal and cord blood, and to investigate potential mechanisms underlying the association between birthweight and metabolic disturbances.

RESEARCH DESIGN AND METHODS

We recorded detailed anthropometric data of mother-offspring dyads. Untargeted metabolomic assays were performed on 67 pairs of cord blood and maternal fasting plasma samples including 16 pairs of small-for-gestational (SGA, < 10th percentile) dyads, 28 pairs of appropriate-for-gestational (AGA, approximate 50 percentile) dyads, and 23 pairs of large-for-gestational (LGA, > 90th percentile) dyads. The association of metabolites with newborn birthweight was conducted to screen for metabolites with U-shaped and line-shaped distributions. The association of metabolites with maternal and fetal phenotypes was also performed.

RESULTS

We found 2 types of metabolites that changed in different patterns according to newborn birthweight. One type of metabolite exhibited a "U-shaped" trend of abundance fluctuation in the SGA-AGA-LGA groups. The results demonstrated that cuminaldehyde level was lower in the SGA and LGA groups, and its abundance in cord blood was negatively correlated with maternal BMI (r = -0.352 p = 0.009) and weight gain (r = -0.267 p = 0.043). 2-Methoxy-estradiol-17b 3-glucuronide, which showed enrichment in the SGA and LGA groups, was positively correlated with homocysteine (r = 0.44, p < 0.001) and free fatty acid (r = 0.42, p < 0.001) in maternal blood. Serotonin and 13(S)-HODE were the second type of metabolites, denoted as "line-shaped", which both showed increasing trends in the SGA-AGA-LGA groups in both maternal and cord blood and were both significantly positively correlated with maternal BMI before pregnancy. Moreover, cuminaldehyde, serotonin, 13(S)-HODE and some lipid metabolites showed a strong correlation between maternal and cord blood.

CONCLUSIONS

These investigations demonstrate broad-scale metabolomic differences associated with newborn birthweight in both pregnant women and their newborns. The U-shaped metabolites associated with both the SGA and LGA groups might explain the U-shaped association between birthweight and metabolic dysregulation. The line-shaped metabolites might participate in intrauterine growth regulation. These observations might help to provide new insights into the insulin resistance and the risk of metabolic disturbance of SGA and LGA babies in adulthood and might identify potential new markers for adverse newborn outcomes in pregnant women.

African American Women with Cardiometabolic Complications of Pregnancy Have Decreased Serum Abundance of Specialized Pro-Resolving Lipid Mediators and Endocannabinoids

African American (AA) women experience higher rates of maternal morbidity and mortality compared to US women of other racial/ ethnic groups. Cardiometabolic complications of pregnancy (including gestational diabetes, gestational hypertension, and preeclampsia) are leading contributors to maternal morbidity and mortality. Marked changes in circulating lipids are known to accompany cardiometabolic complications of pregnancy. Serum concentrations of docosahexaenoic acid (DHA) have been shown to be inversely correlated with risk for preeclampsia. DHA is a biosynthetic precursor of a class of specialized pro-resolving mediators (SPMs), resolvins, that have anti-inflammatory properties and are also associated with hypertensive disorders of pregnancy. We employed targeted lipidomics to characterize the distribution of DHA-containing phospholipids and SPMs in maternal serum collected in early and late pregnancy (8-14 weeks and 24-30 weeks gestation, respectively) to identify key lipids that are dysregulated during pregnancy in AA women who develop cardiometabolic complications. We identified a lipid signature in early pregnancy serum samples of AA women that is predictive of cardiometabolic complications of pregnancy with 74% accuracy. These are Resolvin D1, Resolvin E1, 2-AG, PGE2-glyerol ester, and 36:6 PC. These findings suggest that there are blood-based markers detectable in early pregnancy that can potentially identify persons at risk and tailor clinical interventions.

Plasma Exosomal miRNAs Associated With Metabolism as Early Predictor of Gestational Diabetes Mellitus

To date, the miRNA expression profile of plasma exosomes in women whose pregnancy is complicated by gestational diabetes mellitus (GDM) has not been fully clarified. In this study, differentially expressed miRNAs in plasma exosomes were identified by high-throughput small-RNA sequencing in 12 pregnant women with GDM and 12 with normal glucose tolerance (NGT) and validated in 102 pregnant women with GDM and 101 with NGT. A total of 22 exosomal miRNAs were found, five of which were verified by real-time qPCR. Exosomal miR-423-5p was upregulated, whereas miR-122-5p, miR-148a-3p, miR-192-5p, and miR-99a-5p were downregulated in women whose pregnancy was complicated by GDM. IGF1R and GYS1 as target genes of miR-423-5p, and G6PC3 and FDFT1 as target genes of miR-122-5p were associated with insulin and AMPK signaling pathways and may participate in the regulation of metabolism in GDM. The five exosomal miRNAs had an area under the curve of 0.82 (95%CI, 0.73, ∼0.91) in early prediction of GDM. Our study demonstrates that dysregulated exosomal miRNAs in plasma from pregnant women with GDM might influence the insulin and AMPK signaling pathways and could contribute to the early prediction of GDM.

Differences in lipid metabolism in acquired versus preexisting glucose intolerance during gestation: role of free fatty acids and sphingosine-1-phosphate

BACKGROUND

The prevalence of gestational diabetes mellitus (GDM) is increasing worldwide. There is increasing evidence that GDM is a heterogeneous disease with different subtypes. An important question in this context is whether impaired glucose tolerance (IGT), which is a typical feature of the disease, may already be present before pregnancy and manifestation of the disease. The latter type resembles in its clinical manifestation prediabetes that has not yet manifested as type 2 diabetes (T2DM). Altered lipid metabolism plays a crucial role in the disorder's pathophysiology. The aim was to investigate the role of lipids which are relevant in diabetes-like phenotypes in these both models with different time of initial onset of IGT.

METHODS

Two rodent models reflecting different characteristics of human GDM were used to characterize changes in lipid metabolism occurring during gestation. Since the New Zealand obese (NZO)-mice already exhibit IGT before and during gestation, they served as a subtype model for GDM with preexisting IGT (preIGT) and were compared with C57BL/6 N mice with transient IGT acquired during gestation (aqIGT). While the latter model does not develop manifest diabetes even under metabolic stress conditions, the NZO mouse is prone to severe disease progression later in life. Metabolically healthy Naval Medical Research Institute (NMRI) mice served as controls.

RESULTS

In contrast to the aqIGT model, preIGT mice showed hyperlipidemia during gestation with elevated free fatty acids (FFA), triglycerides (TG), and increased atherogenic index. Interestingly, sphingomyelin (SM) concentrations in the liver decreased during gestation concomitantly with an increase in the sphingosine-1-phosphate (S1P) concentration in plasma. Further, preIGT mice showed impaired hepatic weight adjustment and alterations in hepatic FFA metabolism during gestation. This was accompanied by decreased expression of peroxisome proliferator-activated receptor alpha (PPARα) and lack of translocation of fatty acid translocase (FAT/CD36) to the hepatocellular plasma membrane.

CONCLUSION

The preIGT model showed impaired lipid metabolism both in plasma and liver, as well as features of insulin resistance consistent with increased S1P concentrations, and in these characteristics, the preIGT model differs from the common GDM subtype with aqIGT. Thus, concomitantly elevated plasma FFA and S1P concentrations, in addition to general shifts in sphingolipid fractions, could be an interesting signal that the metabolic disorder existed before gestation and that future pregnancies require more intensive monitoring to avoid complications. This graphical abstract was created with BioRender.com .

Value of PAPP-A combined with BMI in predicting the prognosis of gestational diabetes mellitus: an observational study

The aim of this study was to investigate the potential of pregnancy-associated plasma protein A (PAPP-A) and clinical data in predicting gestational diabetes mellitus (GDM). Clinical data of 318 pregnant women with GDM and 200 healthy pregnant women were retrospectively analysed. The age, BMI and caesarean section in GDM were significantly higher than in normal group. Serum and placental levels of PAPP-A were significantly lower in GDM than in normal group. Pearson's correlation analysis showed that serum levels of PAPP-A were negatively correlated with BMI and blood glucose level. Binary logistic regression analysis displayed that PAPP-A were the potential factors influencing GDM. The area under the ROC curve (AUC) for PAPP-A combined with BMI in predicting GDM was 0.941, significantly higher than that of the single one. The potential of PAPP-A in the first trimester is limited in predicting GDM. PAPP-A combined with BMI is highly conductive for predicting GDM.Impact statementWhat is already known on this subject? GDM not only increases the risk of perinatal morbidity, but also results in an increased risk of long-term sequelae for both mother and child including diabetes, cardiovascular disease obesity. Previous data indicate that besides glycemic control in the second trimester, interventions initiated early in pregnancy can reduce the rate of GDM in pregnant women. The expression of PAPP-A in serum of GDM pregnant women was decreased in the first trimester. Whereas, whether PAPP-A can be as an early predictor of GDM is not clear.What do the results of this study add? The present study shows that PAPP-A MoM was less than 0.6757 in the first trimester of pregnancy is more prone to GDM. The potential of PAPP-A in the first trimester is limited in predicting GDM. PAPP-A combined with BMI is highly conductive for predicting GDM.What are the implications of these findings for clinical practice and/or further research? Early GDM prediction is crucial for prevention and management of GDM, to cope with the rising prevalence of GDM and reduce later life chronic disease of both mother and child. Based on the level of PAPP-A MoM and BMI, interventions such as lifestyle changes initiated early in pregnancy shouldbeenabledin pregnant women.

Branched-Chain Amino Acids and Their Interactions With Lipid Metabolites for Increased Risk of Gestational Diabetes

OBJECTIVE

We aimed to explore associations of branched-chain amino acids (BCAA) in early pregnancy with gestational diabetes mellitus (GDM), and whether high BCAAs and lipidomics markers had interactive effects on the risk of GDM.

METHODS

We conducted a 1:1 case-control study (n = 486) nested in a prospective cohort of pregnant women in Tianjin, China. Blood samples were collected at their first antenatal care visit (median 10 gestational weeks). Serum BCAAs, saturated fatty acids (SFA) and lysophosphatidylcholines (LPC) were measured by liquid chromatography-tandem mass spectrometry analysis. Conditional logistic regression was performed to examine associations of BCAAs with the risk of GDM. Interactions between high BCAAs and high SFA16:0 for GDM were examined using additive interaction measures.

RESULTS

High serum valine, leucine, isoleucine, and total BCAAs were associated with markedly increased risk of GDM (OR of top vs bottom tertiles: 1.91 [95% CI, 1.22-3.01]; 1.87 [1.20-2.91]; 2.23 [1.41-3.52]; 1.93 [1.23-3.02], respectively). The presence of high SFA16:0 defined as ≥ 17.1 nmol/mL (ie, median) markedly increased the ORs of high leucine alone and high isoleucine alone up to 4.56 (2.37-8.75) and 4.41 (2.30-8.43) for the risk of GDM, with significant additive interaction. After adjustment for LPCs, the ORs were greatly elevated (6.33, 2.25-17.80 and 6.53, 2.39-17.86) and the additive interactions became more significant.

CONCLUSION

BCAAs in early pregnancy were positively associated with the risk of GDM, and high levels of leucine and isoleucine enhanced the risk association of high SFA16:0 with GDM, independent of LPCs.

Lipid metabolism is dysregulated in a mouse model of diabetes

Much evidence for diabetes mellitus being associated with dysregulated lipid metabolism has been accrued from studies using blood plasma. However, the systemic dysregulation these results point to is not understood. This study used Lipid Traffic Analysis on data from a mouse model of diabetes to test the hypothesis that the systemic control of lipid metabolism differed in a model of diabetes. This provided eidence for changes in the systemic control of both triglyceride and phospholipid metabolism that were not attributable to dietary intake. This supports the conclusion that diabetes is a systemic condition associated with dysregulated lipid metabolism through several pathways.

A mouse model of gestational diabetes shows dysregulated lipid metabolism post-weaning, after return to euglycaemia

BACKGROUND

Gestational diabetes is associated with increased risk of type 2 diabetes mellitus and cardiovascular disease for the mother in the decade after delivery. However, the molecular mechanisms that drive these effects are unknown. Recent studies in humans have shown that lipid metabolism is dysregulated before diagnosis of and during gestational diabetes and we have shown previously that lipid metabolism is also altered in obese female mice before, during and after pregnancy. These observations led us to the hypothesis that this persistent dysregulation reflects an altered control of lipid distribution throughout the organism.

METHODS

We tested this in post-weaning (PW) dams using our established mouse model of obese GDM (high fat, high sugar, obesogenic diet) and an updated purpose-built computational tool for plotting the distribution of lipid variables throughout the maternal system (Lipid Traffic Analysis v2.3).

RESULTS

This network analysis showed that unlike hyperglycaemia, lipid distribution and traffic do not return to normal after pregnancy in obese mouse dams. A greater range of phosphatidylcholines was found throughout the lean compared to obese post-weaning dams. A range of triglycerides that were found in the hearts of lean post-weaning dams were only found in the livers of obese post-weaning dams and the abundance of odd-chain FA-containing lipids differed locally in the two groups. We have therefore shown that the control of lipid distribution changed for several metabolic pathways, with evidence for changes to the regulation of phospholipid biosynthesis and FA distribution, in a number of tissues.

CONCLUSIONS

We conclude that the control of lipid metabolism is altered following an obese pregnancy. These results support the hypothesis that obese dams that developed GDM maintain dysregulated lipid metabolism after pregnancy even when glycaemia returned to normal, and that these alterations could contribute to the increased risk of later type 2 diabetes and cardiovascular disease.

A Prospective Study of Early-pregnancy Thyroid Markers, Lipid Species, and Risk of Gestational Diabetes Mellitus

CONTEXT

While the associations between thyroid markers and gestational diabetes mellitus (GDM) have been extensively studied, the results are inconclusive and the mechanisms remain unclear.

OBJECTIVE

We aimed to investigate the prospective associations of thyroid markers in early gestation with GDM risk, and examine the mediating effects through lipid species.

METHODS

This study included 6068 pregnant women from the Tongji-Shuangliu Birth Cohort. Maternal serum thyroid markers (free triiodothyronine (fT3), free thyroxine (fT4), thyroid-stimulating hormone, thyroid peroxidase antibody, and thyroglobulin antibody) were measured before 15 weeks. Deiodinase activity was assessed by fT3/fT4 ratio. Plasma lipidome were quantified in a subset of 883 participants.

RESULTS

Mean age of the participants was 26.6 ± 3.7 years, and mean gestational age was 10.3 ± 2.0 weeks. Higher levels of fT4 were associated with a decreased risk of GDM (OR = 0.73 comparing the extreme quartiles; 95% CI 0.54, 0.98, Ptrend = .043), while higher fT3/fT4 ratio was associated with an increased risk of GDM (OR = 1.43 comparing the extreme quartiles; 95% CI 1.06, 1.93, Ptrend = .010) after adjusting for potential confounders. Multiple linear regression suggested that fT3/fT4 ratio was positively associated with alkylphosphatidylcholine 36:1, phosphatidylethanolamine plasmalogen 38:6, diacylglyceride 18:0/18:1, sphingomyelin 34:1, and phosphatidylcholine 40:7 (false discovery rate [FDR] adjusted P < .05). Mediation analysis indicated 67.9% of the association between fT3/fT4 ratio and GDM might be mediated through the composite effect of these lipids.

CONCLUSION

Lower concentration of serum fT4 or higher fT3/fT4 ratio in early pregnancy was associated with an increased risk of GDM. The association of fT3/fT4 ratio with GDM was largely mediated by specific lipid species.

Gas Chromatography-Mass Spectroscopy-Based Metabolomics Analysis Reveals Potential Biochemical Markers for Diagnosis of Gestational Diabetes Mellitus

Due to many adverse effects of gestational diabetes mellitus (GDM) on the mother and fetus, its diagnosis is crucial. The presence of GDM can be confirmed by an abnormal fasting plasma glucose level (aFPG) and/or oral glucose tolerance test (OGTT) performed mostly between 24 and 28 gestational week. Both aFPG and abnormal glucose tolerance (aGT) are used to diagnose GDM. In comparison to measurement of FPG, OGTT is time-consuming, usually inconvenient for the patient, and very often needs to be repeated. Therefore, it is necessary to seek tests that will be helpful and convenient to diagnose GDM. For this reason, we investigated the differences in fasting serum metabolites between GDM women with abnGM and normal FPG (aGT-GDM group), with aFPG and normal glucose metabolism (aFPG-GDM group) as well as pregnant women with normal glucose tolerance (NGT) being a control group. Serum metabolites were measured by an untargeted approach using gas chromatography–mass spectrometry (GC–MS). In the discovery phase, fasting serum samples collected from 79 pregnant women (aFPG-GDM, n = 24; aGT-GDM, n = 26; NGT, n = 29) between 24 and 28 weeks of gestation (gwk) were fingerprinted. A set of metabolites (α–hydroxybutyric acid (α–HB), β–hydroxybutyric acid (β–HB), and several fatty acids) significant in aGT-GDM vs NGT but not significant in aFPG-GDM vs NGT comparison in the discovery phase was selected for validation. These metabolites were quantified by a targeted GC–MS method in a validation cohort consisted of 163 pregnant women (aFPG-GDM, n = 51; aGT-GDM, n = 44; and NGT, n = 68). Targeted analyses were also performed on the serum collected from 92 healthy women in the first trimester (8–14 gwk) who were NGT at this time, but in the second trimester (24–28 gwk) they were diagnosed with GDM. It was found that α–HB, β–HB, and several fatty acids were associated with aGT-GDM. A combination of α–HB, β–HB, and myristic acid was found highly specific and sensitive for the diagnosis of GDM manifested by aGT-GDM (AUC = 0.828) or to select women at a risk of aGT-GDM in the first trimester (AUC = 0.791). Our findings provide new potential markers of GDM and may have implications for its early diagnosis.

Longitudinal Plasma Lipidome and Risk of Type 2 Diabetes in a Large Sample of American Indians With Normal Fasting Glucose: The Strong Heart Family Study

OBJECTIVE

Comprehensive assessment of alterations in lipid species preceding type 2 diabetes (T2D) is largely unknown. We aimed to identify plasma molecular lipids associated with risk of T2D in American Indians.

RESEARCH DESIGN AND METHODS

Using untargeted liquid chromatography-mass spectrometry, we repeatedly measured 3,907 fasting plasma samples from 1,958 participants who attended two examinations (∼5.5 years apart) and were followed up to 16 years in the Strong Heart Family Study. Mixed-effects logistic regression was used to identify lipids associated with risk of T2D, adjusting for traditional risk factors. Repeated measurement analysis was performed to examine the association between change in lipidome and change in continuous measures of T2D, adjusting for baseline lipids. Multiple testing was controlled by false discovery rate at 0.05.

RESULTS

Higher baseline level of 33 lipid species, including triacylglycerols, diacylglycerols, phosphoethanolamines, and phosphocholines, was significantly associated with increased risk of T2D (odds ratio [OR] per SD increase in log2-transformed baseline lipids 1.50-2.85) at 5-year follow-up. Of these, 21 lipids were also associated with risk of T2D at 16-year follow-up. Aberrant lipid profiles were also observed in prediabetes (OR per SD increase in log2-transformed baseline lipids 1.30-2.19 for risk lipids and 0.70-0.78 for protective lipids). Longitudinal changes in 568 lipids were significantly associated with changes in continuous measures of T2D. Multivariate analysis identified distinct lipidomic signatures differentiating high- from low-risk groups.

CONCLUSIONS

Lipid dysregulation occurs many years preceding T2D, and novel molecular lipids (both baseline level and longitudinal change over time) are significantly associated with risk of T2D beyond traditional risk factors. Our findings shed light on the mechanisms linking dyslipidemia to T2D and may yield novel therapeutic targets for early intervention tailored to American Indians.

Plasma lipidomics in early pregnancy and risk of gestational diabetes mellitus: a prospective nested case-control study in Chinese women

BACKGROUND

Lipid metabolism plays an important role in the pathogenesis of diabetes. There is little evidence regarding the prospective association of the maternal lipidome with gestational diabetes mellitus (GDM), especially in Chinese populations.

OBJECTIVES

We aimed to identify novel lipid species associated with GDM risk in Chinese women, and assess the incremental predictive capacity of the lipids for GDM.

METHODS

We conducted a nested case-control study using the Tongji-Shuangliu Birth Cohort with 336 GDM cases and 672 controls, 1:2 matched on age and week of gestation. Maternal blood samples were collected at 6-15 wk, and lipidomes were profiled by targeted ultra-HPLC-tandem MS. GDM was diagnosed by oral-glucose-tolerance test at 24-28 wk. The least absolute shrinkage and selection operator is a regression analysis method that was used to select novel biomarkers. Multivariable conditional logistic regression was used to estimate the associations.

RESULTS

Of 366 detected lipids, 10 were selected and found to be significantly associated with GDM independently of confounders: there were positive associations with phosphatidylinositol 40:6, alkylphosphatidylcholine 36:1, phosphatidylethanolamine plasmalogen 38:6, diacylglyceride 18:0/18:1, and alkylphosphatidylethanolamine 40:5 (adjusted ORs per 1 log-SD increment range: 1.34-2.86), whereas there were inverse associations with sphingomyelin 34:1, dihexosyl ceramide 24:0, mono hexosyl ceramide 18:0, dihexosyl ceramide 24:1, and phosphatidylcholine 40:7 (adjusted ORs range: 0.48-0.68). Addition of these novel lipids to the classical GDM prediction model resulted in a significant improvement in the C-statistic (discriminatory power of the model) to 0.801 (95% CI: 0.772, 0.829). For every 1-point increase in the lipid risk score of the 10 lipids, the OR of GDM was 1.66 (95% CI: 1.50, 1.85). Mediation analysis suggested the associations between specific lipid species and GDM were partially explained by glycemic and insulin-related indicators.

CONCLUSIONS

Specific plasma lipid biomarkers in early pregnancy were associated with GDM in Chinese women, and significantly improved the prediction for GDM.

Lipid Metabolism Is Dysregulated before, during and after Pregnancy in a Mouse Model of Gestational Diabetes

The aim of the current study was to test the hypothesis that maternal lipid metabolism was modulated during normal pregnancy and that these modulations are altered in gestational diabetes mellitus (GDM). We tested this hypothesis using an established mouse model of diet-induced obesity with pregnancy-associated loss of glucose tolerance and a novel lipid analysis tool, Lipid Traffic Analysis, that uses the temporal distribution of lipids to identify differences in the control of lipid metabolism through a time course. Our results suggest that the start of pregnancy is associated with several changes in lipid metabolism, including fewer variables associated with de novo lipogenesis and fewer PUFA-containing lipids in the circulation. Several of the changes in lipid metabolism in healthy pregnancies were less apparent or occurred later in dams who developed GDM. Some changes in maternal lipid metabolism in the obese-GDM group were so late as to only occur as the control dams' systems began to switch back towards the non-pregnant state. These results demonstrate that lipid metabolism is modulated in healthy pregnancy and the timing of these changes is altered in GDM pregnancies. These findings raise important questions about how lipid metabolism contributes to changes in metabolism during healthy pregnancies. Furthermore, as alterations in the lipidome are present before the loss of glucose tolerance, they could contribute to the development of GDM mechanistically.

Association of circulating omega 3, 6 and 9 fatty acids with gestational diabetes mellitus: a systematic review

BACKGROUND

Gestational diabetes mellitus (GDM) is associated with increased risks of disease for mother and child during pregnancy and after that. Early diagnosis of GDM would promote both maternal and fetal health. Metabolomics can simplify and develop our understanding of the etiology, manifestation, or pathophysiology of the disease. This systematic review investigates the association of circulating omega 3, 6, and 9 fatty acids with GDM.

METHODS

We conducted a systematic search of PubMed, Scopus, Web of Science, and EMBASE databases up to May 8, 2020, using the key term combinations of all types of omega fatty acids with gestational diabetes mellitus. Additional articles were identified through searching the reference lists of included studies.

RESULTS

This systematic review included 15 articles. Five were cohort studies, four included nested case-control studies and four were case-control studies. The results of this study demonstrate an increasing trend in the amount of oleic acid and palmitoleic acid in the second trimester and an increase in decosahexanoic acid in the third trimester of GDM mothers. The changes in other fatty acids of interest are either not significant or if significant, their results are inconsistent with the other existing articles.

CONCLUSIONS

Omega fatty acids, as potential biomarkers, are considered to be associated with GDM risk and thus provide useful information regarding the prevention and early diagnosis of GDM. Moreover, existing metabolomic studies on GDM are shown to provide conflicting results about metabolite profile characteristics. This systematic review was registered at PROSPERO ( www.crd.york.ac.uk/PROSPERO ) as CRD42020196122.

Metabolomic Biomarkers in Gestational Diabetes Mellitus: A Review of the Evidence

Gestational diabetes mellitus (GDM) is the fastest growing type of diabetes, affecting between 2 to 38% of pregnancies worldwide, varying considerably depending on diagnostic criteria used and sample population studied. Adverse obstetric outcomes include an increased risk of macrosomia, and higher rates of stillbirth, instrumental delivery, and birth trauma. Metabolomics, which is a platform used to analyse and characterise a large number of metabolites, is increasingly used to explore the pathophysiology of cardiometabolic conditions such as GDM. This review aims to summarise metabolomics studies in GDM (from inception to January 2021) in order to highlight prospective biomarkers for diagnosis, and to better understand the dysfunctional metabolic pathways underlying the condition. We found that the most commonly deranged pathways in GDM include amino acids (glutathione, alanine, valine, and serine), carbohydrates (2-hydroxybutyrate and 1,5-anhydroglucitol), and lipids (phosphatidylcholines and lysophosphatidylcholines). We also highlight the possibility of using certain metabolites as predictive markers for developing GDM, with the use of highly stratified modelling techniques. Limitations for metabolomic research are evaluated, and future directions for the field are suggested to aid in the integration of these findings into clinical practice.

Plasma metabolites, especially lipid metabolites, are altered in pregnant women with gestational diabetes mellitus

BACKGROUND AND AIMS

Gestational diabetes mellitus (GDM) is a pathological condition of glucose intolerance associated with adverse pregnancy outcomes and increased risk of developing maternal type 2 diabetes later in life. Metabolomics is finding increasing use in the study of GDM. To date, GDM-specific metabolomic changes have not been completely elucidated.

MATERIALS AND METHODS

In this pilot study, metabolomics fingerprinting data, obtained by ultra-performance liquid chromatography quadrupole time-of-flight mass spectrometry (UHPLC/Q-TOF-MS), of 54 healthy pregnant women and 49 patients with GDM at the second and third gestational trimesters were analyzed. Multilevel statistical methods were used to process complex metabolomic data from the retrospective cohorts.

RESULTS

Using univariate analysis (p < 0.05), 41 metabolites were identified as having the most significant differences between these two groups. Lipid metabolites, particularly glycerophospholipids, were the most prevalent class of altered compounds. In addition, metabolites with previously unknown connection to GDM - such as monoacylglycerol, dihydrobiopterin, and 13S-hydroxyoctadecadienoic acid - were identified with strong discriminative power. The main metabolic pathways affected by GDM included glycerophospholipid metabolism, linoleic acid metabolism, and D-arginine and D-ornithine metabolism.

CONCLUSION

Our data provide a comprehensive overview of metabolite changes at different stages of pregnancy, which offers further insights into the pathogenesis of GDM.

Plasma lipidomics profile in pregnancy and gestational diabetes risk: a prospective study in a multiracial/ethnic cohort

INTRODUCTION

Disruption of lipid metabolism is implicated in gestational diabetes (GDM). However, prospective studies on lipidomics and GDM risk in race/ethnically diverse populations are sparse. Here, we aimed to (1) identify lipid networks in early pregnancy to mid-pregnancy that are associated with subsequent GDM risk and (2) examine the associations of lipid networks with glycemic biomarkers to understand the underlying mechanisms.

RESEARCH DESIGN AND METHODS

This study included 107 GDM cases confirmed using the Carpenter and Coustan criteria and 214 non-GDM matched controls from the National Institute of Child Health and Human Development Fetal Growth Studies-Singleton cohort, untargeted lipidomics data of 420 metabolites (328 annotated and 92 unannotated), and information on glycemic biomarkers in maternal plasma at visit 0 (10-14 weeks) and visit 1 (15-26 weeks). We constructed lipid networks using weighted correlation network analysis technique. We examined prospective associations of lipid networks and individual lipids with GDM risk using linear mixed effect models. Furthermore, we calculated Pearson's partial correlation for GDM-related lipid networks and individual lipids with plasma glucose, insulin, C-peptide and glycated hemoglobin at both study visits.

RESULTS

Lipid networks primarily characterized by elevated plasma diglycerides and short, saturated/low unsaturated triglycerides and lower plasma cholesteryl esters, sphingomyelins and phosphatidylcholines were associated with higher risk of developing GDM (false discovery rate (FDR) <0.05). Among individual lipids, 58 metabolites at visit 0 and 96 metabolites at visit 1 (40 metabolites at both time points) significantly differed between women who developed GDM and who did not (FDR <0.05). Furthermore, GDM-related lipid networks and individual lipids showed consistent correlations with maternal glycemic markers particularly in early pregnancy at visit 0.

CONCLUSIONS

Plasma lipid metabolites in early pregnancy both individually and interactively in distinct networks were associated with subsequent GDM risk in race/ethnically diverse US women. Future research is warranted to assess lipid metabolites as etiologic markers of GDM.

Serum lipids and their association with birth weight in metformin and insulin treated patients with gestational diabetes

AIMS

To compare the effects of metformin and insulin treatment on maternal serum lipids in patients with gestational diabetes (GDM), and to analyse the associations between individual lipids and birth weight (BW).

METHODS

This is a secondary analysis of a randomized trial comparing metformin (n = 110) and insulin (n = 107) treatment of GDM. Fasting serum lipidome was measured at baseline (the time of diagnosis, mean 30 gestational weeks, gw) and at 36 gw using nuclear magnetic resonance spectroscopy.

RESULTS

Total and VLDL triglycerides, and VLDL cholesterol increased from baseline to 36 gw in both treatment groups. The rise in triglycerides was greater in the metformin treated patients (p < 0.01). Baseline total and VLDL triglycerides, VLDL cholesterol, and apolipoprotein B to A-1 ratio (apoB/apoA-1) associated positively with BW, more strongly in the metformin group. Among patients in the highest baseline VLDL cholesterol or apoB/apoA-1 quartile, those treated with insulin had lower BWs than those treated with metformin (p < 0.03).

CONCLUSION

Compared to insulin, metformin treatment of GDM led to higher maternal serum concentrations of triglyceride-rich lipoproteins. Especially triglycerides and cholesterol in VLDL were positively associated with BW. Women with high VLDL cholesterol or high apoB/apoA-1 may benefit from insulin treatment over metformin with respect to offspring BW.

Lipidomic Biomarkers of Extracellular Vesicles for the Prediction of Preterm Birth in the Early Second Trimester

Preterm birth is the leading cause of infant death worldwide and results in a high societal economic burden associated with newborn care. Recent studies have shown that extracellular vesicles (EVs) play an important role in fetal development during pregnancy. Lipids in EVs related to preterm birth remain undefined. Here, we fully investigated differences in lipids in plasma, microvesicles (MVs), and exosomes (Exos) between 27 preterm and 66 full-term pregnant women in the early second trimester (12-24 weeks) using an untargeted lipidomics approach. Independent of other characteristics of samples, we detected 97, 58, and 10 differential features (retention time (RT) and m/z) with identification in plasma, MVs, and Exos, respectively. A panel of five lipids from MVs has an area under the receiver operating characteristic curve (AUC) of 0.87 for the prediction of preterm birth. One lipid of the panel (PS (34:0)) was validated in an additional 83 plasma samples (41 preterm and 42 full-term deliveries) by the pseudotargeted lipidomics method (AUC = 0.71). Our results provide useful information about the early prediction of preterm birth, as well as a better understanding of the underlying mechanisms and intervention of preterm birth. The MS data have been deposited in the CNSA (https://db.cngb.org/cnsa/) of CNGBdb with accession code CNP0001076.

Underlying dyslipidemia postpartum in women with a recent GDM pregnancy who develop type 2 diabetes

Approximately, 35% of women with Gestational Diabetes (GDM) progress to Type 2 Diabetes (T2D) within 10 years. However, links between GDM and T2D are not well understood. We used a well-characterised GDM prospective cohort of 1035 women following up to 8 years postpartum. Lipidomics profiling covering >1000 lipids was performed on fasting plasma samples from participants 6–9 week postpartum (171 incident T2D vs. 179 controls). We discovered 311 lipids positively and 70 lipids negatively associated with T2D risk. The upregulation of glycerolipid metabolism involving triacylglycerol and diacylglycerol biosynthesis suggested activated lipid storage before diabetes onset. In contrast, decreased sphingomyelines, hexosylceramide and lactosylceramide indicated impaired sphingolipid metabolism. Additionally, a lipid signature was identified to effectively predict future diabetes risk. These findings demonstrate an underlying dyslipidemia during the early postpartum in those GDM women who progress to T2D and suggest endogenous lipogenesis may be a driving force for future diabetes onset.

Altered Metabolome of Lipids and Amino Acids Species: A Source of Early Signature Biomarkers of T2DM

Diabetes mellitus, a disease of modern civilization, is considered the major mainstay of mortalities around the globe. A great number of biochemical changes have been proposed to occur at metabolic levels between perturbed glucose, amino acid, and lipid metabolism to finally diagnoe diabetes mellitus. This window period, which varies from person to person, provides us with a unique opportunity for early detection, delaying, deferral and even prevention of diabetes. The early detection of hyperglycemia and dyslipidemia is based upon the detection and identification of biomarkers originating from perturbed glucose, amino acid, and lipid metabolism. The emerging “OMICS” technologies, such as metabolomics coupled with statistical and bioinformatics tools, proved to be quite useful to study changes in physiological and biochemical processes at the metabolic level prior to an eventual diagnosis of DM. Approximately 300–400 such metabolites have been reported in the literature and are considered as predicting or risk factor-reporting metabolic biomarkers for this metabolic disorder. Most of these metabolites belong to major classes of lipids, amino acids and glucose. Therefore, this review represents a snapshot of these perturbed plasma/serum/urinary metabolic biomarkers showing a significant correlation with the future onset of diabetes and providing a foundation for novel early diagnosis and monitoring the progress of metabolic syndrome at early symptomatic stages. As most metabolites also find their origin from gut microflora, metabolism and composition of gut microflora also vary between healthy and diabetic persons, so we also summarize the early changes in the gut microbiome which can be used for the early diagnosis of diabetes.

Extraction of Lipids from Liquid Biological Samples for High-Throughput Lipidomics

Extraction of the lipid fraction is a key part of acquiring lipidomics data. High-throughput lipidomics, the extraction of samples in 96w plates that are then run on 96 or 384w plates, has particular requirements that mean special development work is needed to fully optimise an extraction method. Several methods have been published as suitable for it. Here, we test those methods using four liquid matrices: milk, human serum, homogenised mouse liver and homogenised mouse heart. In order to determine the difference in performance of the methods as objectively as possible, we used the number of lipid variables identified, the total signal strength and the coefficient of variance to quantify the performance of the methods. This showed that extraction methods with an aqueous component were generally better than those without for these matrices. However, methods without an aqueous fraction in the extraction were efficient for milk samples. Furthermore, a mixture containing a chlorinated solvent (dichloromethane) appears to be better than an ethereal solvent (tert-butyl methyl ether) for extracting lipids. This study suggests that a 3:1:0.005 mixture of dichloromethane, methanol and triethylammonium chloride, with an aqueous wash, is the most efficient of the currently reported methods for high-throughput lipid extraction and analysis. Further work is required to develop non-aqueous extraction methods that are both convenient and applicable to a broad range of sample types.

Plasma Lipidome and Prediction of Type 2 Diabetes in the Population-Based Malmö Diet and Cancer Cohort

OBJECTIVE

Type 2 diabetes mellitus (T2DM) is associated with dyslipidemia, but the detailed alterations in lipid species preceding the disease are largely unknown. We aimed to identify plasma lipids associated with development of T2DM and investigate their associations with lifestyle.

RESEARCH DESIGN AND METHODS

At baseline, 178 lipids were measured by mass spectrometry in 3,668 participants without diabetes from the Malmö Diet and Cancer Study. The population was randomly split into discovery (n = 1,868, including 257 incident cases) and replication (n = 1,800, including 249 incident cases) sets. We used orthogonal projections to latent structures discriminant analyses, extracted a predictive component for T2DM incidence (lipid-PC_DM), and assessed its association with T2DM incidence using Cox regression and lifestyle factors using general linear models.

RESULTS

A T2DM-predictive lipid-PC_DM derived from the discovery set was independently associated with T2DM incidence in the replication set, with hazard ratio (HR) among subjects in the fifth versus first quintile of lipid-PC_DM of 3.7 (95% CI 2.2-6.5). In comparison, the HR of T2DM among obese versus normal weight subjects was 1.8 (95% CI 1.2-2.6). Clinical lipids did not improve T2DM risk prediction, but adding the lipid-PC_DM to all conventional T2DM risk factors increased the area under the receiver operating characteristics curve by 3%. The lipid-PC_DM was also associated with a dietary risk score for T2DM incidence and lower level of physical activity.

CONCLUSIONS

A lifestyle-related lipidomic profile strongly predicts T2DM development beyond current risk factors. Further studies are warranted to test if lifestyle interventions modifying this lipidomic profile can prevent T2DM.

Exploring the causes and consequences of maternal metabolic maladaptations during pregnancy: Lessons from animal models

Pregnancy is a remarkable physiological state, during which the metabolic system of the mother adapts to ensure that nutrients are made available for transfer to the fetus for growth and development. Adaptations of maternal metabolism during pregnancy are influenced by the metabolic and nutritional status of the mother and the production of endocrine factors by the placenta that exert metabolic effects. Insufficient or inappropriate adaptations in maternal metabolism during pregnancy may lead to pregnancy complications with important short- and long-term effects for both the health of the child and mother. This is very evident in gestational diabetes, which is marked by greater glucose intolerance and insulin resistance above that expected of a normal pregnancy. Gestational diabetes is associated with increased fetal weight and/or increased adiposity, higher instrumented delivery rates and greater risks for both mother and child of developing type 2 diabetes in the long-term. However, despite the negative health impacts of such metabolic imbalances during pregnancy, the precise mechanisms responsible for orchestrating these changes remain largely unknown. The present review describes the dynamic pregnancy-specific changes that occur in the metabolic system of the mother during pregnancy. It also discusses findings using surgical, pharmacological, genetic and dietary methods in experimental animals that highlight the role of pathways in maternal tissues that lead to metabolic dysfunction, with a particular focus on gestational diabetes. Finally, it summarises the work largely employing gene targeting and hormone administration in rodents that have illuminated the involvement of placental endocrine function in driving maternal metabolic adaptations. While current animal models may not fully replicate what is observed in humans, these have been instrumental in showing that there is a dynamic interplay between changes in maternal metabolic physiology and the placental production of endocrine factors that govern the availability of nutrients to the growing fetus. However, more work is required to specifically identify the placenta-driven changes in maternal metabolic physiology that ensure the appropriate level of insulin production and action during pregnancy. In doing so, these studies may pave the way to understanding the development of pregnancy complications like gestational diabetes, as well as further our understanding of type-2 diabetes and the control of metabolic physiology more broadly.

A high-throughput platform for detailed lipidomic analysis of a range of mouse and human tissues

Lipidomics is of increasing interest in studies of biological systems. However, high-throughput data collection and processing remains non-trivial, making assessment of phenotypes difficult. We describe a platform for surveying the lipid fraction for a range of tissues. These techniques are demonstrated on a set of seven different tissues (serum, brain, heart, kidney, adipose, liver, and vastus lateralis muscle) from post-weaning mouse dams that were either obese (> 12 g fat mass) or lean (<5 g fat mass). This showed that the lipid metabolism in some tissues is affected more by obesity than others. Analysis of human serum (healthy non-pregnant women and pregnant women at 28 weeks' gestation) showed that the abundance of several phospholipids differed between groups. Human placenta from mothers with high and low BMI showed that lean placentae contain less polyunsaturated lipid. This platform offers a way to map lipid metabolism with immediate application in metabolic research and elsewhere. Graphical abstract.

Altered triglyceride and phospholipid metabolism predates the diagnosis of gestational diabetes in obese pregnancy

Gestational diabetes (GDM), a common pregnancy complication associated with obesity and long-term health risks, is usually diagnosed at approximately 28 weeks of gestation. An understanding of lipid metabolism in women at risk of GDM could contribute to earlier diagnosis and treatment. We tested the hypothesis that altered lipid metabolism at the beginning of the second trimester in obese pregnant women is associated with a diagnosis of GDM. Plasma samples from 831 participants (16-45 years, 15-18 weeks gestation, BMI ≥ 30) from the UPBEAT study of obese pregnant women were used. The lipid, sterol and glyceride fraction was isolated and analysed in a semi-quantitative fashion using direct infusion mass spectrometry. A combination of uni-, multi-variate and multi-variable statistical analyses was used to identify candidate biomarkers in plasma associated with a diagnosis of GDM (early third trimester; IADPSG criteria). Multivariable adjusted analyses showed that participants who later developed GDM had a greater abundance of several triglycerides (48:0, 50:1, 50:2, 51:5, 53:4) and phosphatidylcholine (38:5). In contrast sphingomyelins (32:1, 41:2, 42:3), lyso-phosphatidylcholine (16:0, 18:1), phosphatidylcholines (35:2, 40:7, 40:10), two polyunsaturated triglycerides (46:5, 48:6) and several oxidised triglycerides (48:6, 54:4, 56:4, 58:6) were less abundant. We concluded that both lipid and triglyceride metabolism were altered at least 10 weeks before diagnosis of GDM. Further investigation is required to determine the functional consequences of these differences and the mechanisms by which they arise.

Compartmentalized Synthesis of Triacylglycerol at the Inner Nuclear Membrane Regulates Nuclear Organization

Cells dynamically adjust organelle organization in response to growth and environmental cues. This requires regulation of synthesis of phospholipids, the building blocks of organelle membranes, or remodeling of their fatty-acyl (FA) composition. FAs are also the main components of triacyglycerols (TGs), which enable energy storage in lipid droplets. How cells coordinate FA metabolism with organelle biogenesis during cell growth remains unclear. Here, we show that Lro1, an acyltransferase that generates TGs from phospholipid-derived FAs in yeast, relocates from the endoplasmic reticulum to a subdomain of the inner nuclear membrane. Lro1 nuclear targeting is regulated by cell cycle and nutrient starvation signals and is inhibited when the nucleus expands. Lro1 is active at this nuclear subdomain, and its compartmentalization is critical for nuclear integrity. These data suggest that Lro1 nuclear targeting provides a site of TG synthesis, which is coupled with nuclear membrane remodeling.

Nutritional Intervention to Prevent Alzheimer's Disease: Potential Benefits of Xanthophyll Carotenoids and Omega-3 Fatty Acids Combined

BACKGROUND

A growing body of scientific evidence suggests that enrichment of certain nutritional compounds in the brain may reduce the risk of Alzheimer's disease (AD).

OBJECTIVE

To investigate the impact of supplemental xanthophyll carotenoids plus omega-3 fatty acids on disease progression in patients with AD.

METHODS

Three trial experiments were performed. In Trials 1 and 2 (performed on patients with AD over an 18-month period), 12 patients (AD status at baseline: 4 mild and 8 moderate) were supplemented with a xanthophyll carotenoid only formulation (Formulation 1; lutein:meso-zeaxanthin:zeaxanthin 10:10:2 mg/day) and 13 patients (AD status at baseline: 2 mild, 10 moderate, and 1 severe) were supplemented with a xanthophyll carotenoid and fish oil combination (Formulation 2; lutein:meso-zeaxanthin:zeaxanthin 10:10:2 mg/day plus 1 g/day of fish oil containing 430 mg docohexaenoic acid [DHA] and 90 mg eicopentaenoic acid [EPA]), respectively. In Trial 3, 15 subjects free of AD (the control group) were supplemented for 6 months with Formulation 1. Blood xanthophyll carotenoid response was measured in all trials by HPLC. Omega-3 fatty acids were profiled by direct infusion mass spectrometry.

RESULTS

Xanthophyll carotenoid concentration increases were significantly greater for Formulation 2 compared to Formulation 1 (p < 0.05), and progression of AD was less for this group (p = 0.003), with carers reporting functional benefits in memory, sight, and mood.

CONCLUSION

This preliminary report suggests positive outcomes for patients with AD who consumed a combination of xanthophyll carotenoids plus fish oil, but further study is required to confirm this important observation.

Exploration of the shared pathophysiological mechanisms of gestational diabetes and large for gestational age offspring

Gestational diabetes mellitus (GDM) and large for gestational age (LGA) offspring are two common pregnancy complications. Connections also exist between the two conditions, including mutual maternal risk factors for the conditions and an increased prevalence of LGA offspring amongst pregnancies affected by GDM. Thus, it is important to elucidate potential shared underlying mechanisms of both LGA and GDM. One potential mechanistic link relates to macronutrient metabolism. Indeed, derangement of carbohydrate and lipid metabolism is present in GDM, and maternal biomarkers of glucose and lipid control are associated with LGA neonates in such pregnancies. The aim of this paper is therefore to reflect on the existing nutritional guidelines for GDM in light of our understanding of the pathophysiological mechanisms of GDM and LGA offspring. Lifestyle modification is first line treatment for GDM, and while there is some promise that nutritional interventions may favourably impact outcomes, there is a lack of definitive evidence that changing the macronutrient composition of the diet reduces the incidence of either GDM or LGA offspring. The quality of the available evidence is a major issue, and rigorous trials are needed to inform evidence-based treatment guidelines.

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.

The fecal metabolome is associated with gestational diabetes mellitus

Dysbiosis of gut microbiota has been linked to gestational diabetes mellitus (GDM), and grows as a resource for GDM biomarkers. However, the contributions of gut microbiota to GDM remain incompletely understood. Metabolites are key messengers in the interactions between gut microbiota and the host. Metabolomics is emerging as an essential tool in exploring the contributions of gut microbiota to diseases. In this study, we performed ¹H-NMR based metabolomics on the feces of 62 pregnant women, including 31 women with GDM, and 31 women as the non-diabetes (NDM) control. Using Principle Component Analysis (PCA) and Orthogonal Projection to Latent Structures Discrimination Analysis (OPLS-DA), we observed clear cluster separation of the fecal metabolome between women with GDM and the NDM control. We further applied several feature selection methods to find five fecal metabolites contributing to the cluster separation of the fecal metabolome. These five metabolites, namely dibutyl decanedioate, N-acetylgalactosamine-4-sulphate, homocysteine, l-malic acid, and butanone, were significantly correlated with the clinical indices of GDM. Metabolite enrichment and pathway analysis on the five metabolites suggested that the fecal citrate cycle and sulfur metabolism were correlated with GDM. The results of this study demonstrated that disorders in the fecal metabolome are associated with GDM.

Fecal metabolome could separate women with GDM from the non-diabetic control.

LC-MS-Based Metabolomics and Lipidomics Study of High-Density-Lipoprotein-Modulated Glucose Metabolism with an apoA-I Knockout Mouse Model

Type 2 diabetes mellitus (T2DM) has become a tremendous problem in public health nowadays. High-density lipoprotein (HDL) refers to a group of heterogeneous particles that circulate in blood, and a recent research finds that HDL acts a pivotal part of glucose metabolism. To understand systemic metabolic changes correlated with HDL in glucose metabolism, we applied LC-MS-based metabolomics and lipidomics to detect metabolomic and lipidomic profiles of plasma from apoA-I knockout mice fed a high-fat diet. Multivariate analysis was applied to differentiate apoA-I knockout mice and controls, and potential biomarkers were found. Pathway analysis demonstrated that several metabolic pathways such as aminoacyl-tRNA biosynthesis, arginine and proline metabolism, and phenylalanine, tyrosine, and tryptophan biosynthesis were dysregulated in apoA-I knockout mice. This study may provide a new insight into the underlying pathogenesis in T2DM and prove that LC-MS-based metabolomics and lipidomics are powerful approaches in finding potential biomarkers and disturbed pathways.