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

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.

First-trimester nuclear magnetic resonance-based metabolomic profiling increases the prediction of gestational diabetes mellitus

BACKGROUND

Current strategies for predicting gestational diabetes mellitus demonstrate suboptimal performance.

OBJECTIVE

To investigate whether nuclear magnetic resonance-based metabolomic profiling of maternal blood can be used for first-trimester prediction of gestational diabetes mellitus.

STUDY DESIGN

This was a prospective study of 20,000 women attending routine pregnancy care visits at 11 to 13 weeks' gestation. Metabolic profiles were assessed using a high-throughput nuclear magnetic resonance metabolomics platform. To inform translational applications, we focused on a panel of 34 clinically validated biomarkers for detailed analysis and risk modeling. All biomarkers were used to generate a multivariable logistic regression model to predict gestational diabetes mellitus. Data were split using a random seed into a 70% training set and a 30% validation set. Performance of the multivariable models was measured by receiver operating characteristic curve analysis and detection rates at fixed 10% and 20% false positive rates. Calibration for the combined risk model for all gestational diabetes mellitus was assessed visually through a figure showing the observed incidence against the predicted risk for gestational diabetes mellitus. A sensitivity analysis was conducted excluding the 64 women in our cohort who were diagnosed with gestational diabetes mellitus before 20 weeks' gestation.

RESULTS

The concentrations of several metabolomic biomarkers, including cholesterol, triglycerides, fatty acids, and amino acids, differed between women who developed gestational diabetes mellitus and those who did not. Addition of biomarker profile improved the prediction of gestational diabetes mellitus provided by maternal demographic characteristics and elements of medical history alone (before addition: area under the receiver operating characteristic curve, 0.790; detection rate, 50% [95% confidence interval, 44.3%-55.7%] at 10% false positive rate; and detection rate, 63% [95% confidence interval, 57.4%-68.3%] at 20% false positive rate; after addition: 0.840; 56% [50.3%-61.6%]; and 73% [67.7%-77.8%]; respectively). The performance of combined testing was better for gestational diabetes mellitus treated by insulin (area under the receiver operating characteristic curve, 0.905; detection rate, 76% [95% confidence interval, 67.5%-83.2%] at 10% false positive rate; and detection rate, 85% [95% confidence interval, 77.4%-90.9%] at 20% false positive rate) than gestational diabetes mellitus treated by diet alone (area under the receiver operating characteristic curve, 0.762; detection rate, 47% [95% confidence interval, 37.7%-56.5%] at 10% false positive rate; and detection rate, 64% [95% confidence interval, 54.5%-72.7%] at 20% false positive rate). The calibration plot showed good agreement between the observed incidence of gestational diabetes mellitus and the incidence predicted by the combined risk model. In the sensitivity analysis excluding the women diagnosed with gestational diabetes mellitus before 20 weeks' gestation, there was a negligible difference in the area under the receiver operating characteristic curve compared with the results from the entire cohort combined.

CONCLUSION

Addition of nuclear magnetic resonance-based metabolomic profiling to risk factors can provide first-trimester prediction of gestational diabetes mellitus.

Gestational diabetes mellitus (GDM): diagnosis using biochemical parameters and anthropometric measurements during the first trimester in the Indian population

OBJECTIVES

The objective of the study was to use anthropometric measurements (age, BMI, and subcutaneous fat) in conjunction with biochemical parameters (sex hormone-binding globulin (SHBG), homeostasis model assessment-insulin resistance (HOMA-IR), fasting glucose, serum insulin, and total cholesterol) to predict the probability of gestational diabetes mellitus (GDM) in the first trimester.

METHODS

The study enrolled 48 pregnant women with GDM and 64 high-risk pregnant women without GDM. During the first-trimester examination, maternal blood samples were collected to measure SHBG, fasting blood glucose, serum insulin, and total cholesterol levels. Regression model analysis was used to examine the variables that showed statistically significant differences between the groups and were independent predictors of GDM. Receiver operating characteristic (ROC) curve analysis was employed to determine the risk of developing GDM based on cut-off values.

RESULTS

The levels of SHBG, HOMA-IR, serum insulin, fasting glucose, and total cholesterol were identified as significant independent markers for predicting GDM. Meanwhile, age, body mass index, and subcutaneous fat values were found to be non-independent predictors of GDM. The areas under the ROC curve were calculated to determine the predictive accuracy of total cholesterol, HOMA-IR, SHBG, and subcutaneous fat for developing into GDM, and were 0.869, 0.977, 0.868, and 0.822 respectively. The sensitivities for a false positive rate of 5 % for predicting GDM were 68.7 , 91.67, 91.7, and 97.9 % for total cholesterol, HOMA-IR, SHBG, and subcutaneous fat, respectively.

CONCLUSIONS

The independent predictors for the subsequent development of GDM in high-risk pregnancies are HOMA-IR, SHBG, Total cholesterol, and subcutaneous fat (SC) levels. These parameters can be used to create a regression model to predict the occurrence of 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.

Gestational testosterone excess early to mid-pregnancy disrupts maternal lipid homeostasis and activates biosynthesis of phosphoinositides and phosphatidylethanolamines in sheep

Gestational hyperandrogenism is a risk factor for adverse maternal and offspring outcomes with effects likely mediated in part via disruptions in maternal lipid homeostasis. Using a translationally relevant sheep model of gestational testosterone (T) excess that manifests maternal hyperinsulinemia, intrauterine growth restriction (IUGR), and adverse offspring cardiometabolic outcomes, we tested if gestational T excess disrupts maternal lipidome. Dimensionality reduction models following shotgun lipidomics of gestational day 127.1 ± 5.3 (term 147 days) plasma revealed clear differences between control and T-treated sheep. Lipid signatures of gestational T-treated sheep included higher phosphoinositides (PI 36:2, 39:4) and lower acylcarnitines (CAR 16:0, 18:0, 18:1), phosphatidylcholines (PC 38:4, 40:5) and fatty acids (linoleic, arachidonic, Oleic). Gestational T excess activated phosphatidylethanolamines (PE) and PI biosynthesis. The reduction in key fatty acids may underlie IUGR and activated PI for the maternal hyperinsulinemia evidenced in this model. Maternal circulatory lipids contributing to adverse cardiometabolic outcomes are modifiable by dietary interventions.

Multigenerational diabetes mellitus

Gestational diabetes (GDM) changes the maternal metabolic and uterine environment, thus increasing the risk of short- and long-term adverse outcomes for both mother and child. Children of mothers who have GDM during their pregnancy are more likely to develop Type 2 Diabetes (T2D), early-onset cardiovascular disease and GDM when they themselves become pregnant, perpetuating a multigenerational increased risk of metabolic disease. The negative effect of GDM is exacerbated by maternal obesity, which induces a greater derangement of fetal adipogenesis and growth. Multiple factors, including genetic, epigenetic and metabolic, which interact with lifestyle factors and the environment, are likely to contribute to the development of GDM. Genetic factors are particularly important, with 30% of women with GDM having at least one parent with T2D. Fetal epigenetic modifications occur in response to maternal GDM, and may mediate both multi- and transgenerational risk. Changes to the maternal metabolome in GDM are primarily related to fatty acid oxidation, inflammation and insulin resistance. These might be effective early biomarkers allowing the identification of women at risk of GDM prior to the development of hyperglycaemia. The impact of the intra-uterine environment on the developing fetus, "developmental programming", has a multisystem effect, but its influence on adipogenesis is particularly important as it will determine baseline insulin sensitivity, and the response to future metabolic challenges. Identifying the critical window of metabolic development and developing effective interventions are key to our ability to improve population metabolic health.

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.

The role of dietary fiber on preventing gestational diabetes mellitus in an at-risk group of high triglyceride-glucose index women: a randomized controlled trial

BACKGROUND

Pregnant women with a high triglyceride-glucose (TyG) index during early pregnancy may increase the risk of gestational diabetes mellitus (GDM), and dietary fiber could play an important role in glucose and lipid metabolism. However, no trials have tested the effects of dietary fiber on preventing GDM in women with a high TyG index. This study aims to investigate whether GDM can be prevented by dietary fiber supplementation in women with a TyG index ≥8.5 during early pregnancy (<20 weeks).

METHODS

A randomized clinical trial was performed among 295 women with a TyG index ≥8.5 before 20 weeks of gestation, divided into a fiber group (24 g dietary fiber powder/day) or a control group (usual care). The intervention was conducted from 20 to 24+6 gestational weeks, and both groups received guidance on exercise and diet. The primary outcomes were the incidence of GDM diagnosed by a 75 g oral glucose tolerance test at 25-28 gestational weeks, and levels of maternal blood glucose, lipids. Secondary outcomes include gestational hypertension, postpartum hemorrhage, preterm birth, and other maternal and neonatal complications.

RESULTS

GDM occurred at 11.2% (10 of 89) in the fiber group, which was significantly lower than 23.7 (44 of 186) in the control group (P = 0.015). The mean gestational weeks increased dramatically in the fiber group compared with the control group (39.07 ± 1.08 vs. 38.58 ± 1.44 weeks, P = 0.006). The incidence of preterm birth was 2.3% (2 of 86) of women randomized to the fiber group compared with 9.4% (17 of 181) in the control group (P = 0.032). The concentrations of 2 h postprandial blood glucose showed statistically higher in the control group compared with the intervention group (6.69 ± 1.65 vs. 6.45 ± 1.25 mmol/L, P = 0.026). There were no other significant differences between groups in lipid profile values, or other secondary outcomes.

CONCLUSION

An intervention with dietary fiber supplementation during pregnancy may prevent GDM and preterm birth in women with a TyG index ≥8.5 before 20 weeks of gestation.

Longitudinal lipidomic profiles during pregnancy and associations with neonatal anthropometry: findings from a multiracial cohort

BACKGROUND

Maternal lipidomic profiling offers promise for characterizing lipid metabolites during pregnancy, but longitudinal data are limited. This study aimed to examine associations of longitudinal lipidomic profiles during pregnancy with multiple neonatal anthropometry using data from a multiracial cohort.

METHODS

We measured untargeted plasma lipidome profiles among 321 pregnant women from the NICHD Fetal Growth Study-Singletons using plasma samples collected longitudinally during four study visits at gestational weeks (GW) 10-14, 15-26, 23-31, and 33-39, respectively. We evaluated individual lipidomic metabolites at each study visit in association with neonatal anthropometry. We also evaluated the associations longitudinally by constructing lipid networks using weighted correlation network analysis and common networks using consensus network analysis across four visits using linear mixed-effects models with the adjustment of false discover rate.

FINDINGS

Multiple triglycerides (TG) were positively associated with birth weight (BW), BW Z-score, length and head circumference, while some cholesteryl ester (CE), phosphatidylcholine (PC), sphingomyelines (SM), phosphatidylethanolamines (PE), and lysophosphatidylcholines (LPC 20:3) families were inversely associated with BW, length, abdominal and head circumference at different GWs. Longitudinal trajectories of TG, PC, and glucosylcermides (GlcCer) were associated with BW, and CE (18:2) with BW z-score, length, and sum of skinfolds (SS), while some PC and PE were significantly associated with abdominal and head circumference. Modules of TG at GW 10-14 and 15-26 mainly were associated with BW. At GW 33-39, two networks of LPC (20:3) and of PC, TG, and CE, showed inverse associations with abdominal circumference. Distinct trajectories within two consensus modules with changes in TG, CE, PC, and LPC showed significant differences in BW and length.

INTERPRETATION

The results demonstrated that longitudinal changes of TGs during early- and mid-pregnancy and changes of PC, LPC, and CE during late-pregnancy were significantly associated with neonatal anthropometry.

FUNDING

National Institute of Child Health and Human Development intramural funding.

Maternal diet quality and associations with plasma lipid profiles and pregnancy-related cardiometabolic health

PURPOSE

To assess the relationship of early pregnancy maternal diet quality (DQ) with maternal plasma lipids and indicators of cardiometabolic health, including blood pressure (BP), gestational diabetes mellitus (GDM) and gestational weight gain (GWG).

METHODS

Women (n = 215) aged 18-40 years with singleton pregnancies were recruited at 10-20 weeks gestation. Diet quality was assessed by the Dietary Guideline Index, calculated at early ([mean ± SD]) (15 ± 3 weeks) and late (35 ± 2 weeks) pregnancy. Lipidomic analysis was performed, and 698 species across 37 lipid classes were measured from plasma blood samples collected at early (15 ± 3 weeks) and mid (27 ± 3 weeks)-pregnancy. Clinical measures (BP, GDM diagnosis, weight) and blood samples were collected across pregnancy. Multiple linear and logistic regression models assessed associations of early pregnancy DQ with plasma lipids at early and mid-pregnancy, BP at three antenatal visits, GDM diagnosis and total GWG.

RESULTS

Maternal DQ scores ([mean ± SD]) decreased significantly from early (70.7 ± 11.4) to late pregnancy (66.5 ± 12.6) (p < 0.0005). At a false discovery rate of 0.2, early pregnancy DQ was significantly associated with 13 plasma lipids at mid-pregnancy, including negative associations with six triglycerides (TGs); TG(54:0)[NL-18:0] (neutral loss), TG(50:1)[NL-14:0], TG(48:0)[NL-18:0], TG(52:1)[NL-18:0], TG(54:1)[NL-18:1], TG(50:0)[NL-18:0]. No statistically significant associations were found between early pregnancy DQ and BP, GDM or GWG.

CONCLUSION

Maternal diet did not adhere to Australian Dietary Guidelines. Diet quality was inversely associated with multiple plasma TGs. This study provides novel insights into the relationship between DQ, lipid biomarkers and cardiometabolic health during pregnancy.

Effects of Pregnancy on Plasma Sphingolipids Using a Metabolomic and Quantitative Analysis Approach

Changes in the maternal metabolome, and specifically the maternal lipidome, that occur during pregnancy are relatively unknown. The objective of this investigation was to evaluate the effects of pregnancy on sphingolipid levels using metabolomics analysis followed by confirmational, targeted quantitative analysis. We focused on three subclasses of sphingolipids: ceramides, sphingomyelins, and sphingosines. Forty-seven pregnant women aged 18 to 50 years old participated in this study. Blood samples were collected on two study days for metabolomics analysis. The pregnancy samples were collected between 25 and 28 weeks of gestation and the postpartum study day samples were collected ≥3 months postpartum. Each participant served as their own control. These samples were analyzed using a Ultra-performance liquid chromatography/mass spectroscopy/mass spectroscopy (UPLC/MS/MS) assay that yielded semi-quantitative peak area values that were used to compare sphingolipid levels between pregnancy and postpartum. Following this lipidomic analysis, quantitative LC/MS/MS targeted/confirmatory analysis was performed on the same study samples. In the metabolomic analysis, 43 sphingolipid metabolites were identified and their levels were assessed using relative peak area values. These profiled sphingolipids fell into three categories: ceramides, sphingomyelins, and sphingosines. Of the 43 analytes measured, 35 were significantly different during pregnancy (p < 0.05) (including seven ceramides, 26 sphingomyelins, and two sphingosines) and 32 were significantly higher during pregnancy compared to postpartum. Following metabolomics, a separate quantitative analysis was performed and yielded quantified concentration values for 23 different sphingolipids, four of which were also detected in the metabolomics study. Quantitative analysis supported the metabolomics results with 17 of the 23 analytes measured found to be significantly different during pregnancy including 11 ceramides, four sphingomyelins, and two sphingosines. Fourteen of these were significantly higher during pregnancy. Our data suggest an overall increase in plasma sphingolipid concentrations with possible implications in endothelial function, gestational diabetes mellitus (GDM), intrahepatic cholestasis of pregnancy, and fetal development. This study provides evidence for alterations in maternal sphingolipid metabolism during pregnancy.

Obesity and Dyslipidemia: A Review of Current Evidence

PURPOSE OF REVIEW

Obesity is accompanied by atherogenic dyslipidemia, a specific lipid disorder characterized by both quantitative and qualitative changes of plasma lipoproteins. The main alterations in the lipid profile include hypertriglyceridemia, reduced high-density lipoprotein (HDL) cholesterol level, and elevated small dense low-density lipoprotein (LDL) particles. Epidemiological data show that obesity is more common in women and is a frequent risk factor for reproductive disorders, metabolic complications in pregnancy, and cardiometabolic disease later in life. The aim of this narrative review is to discuss recent advances in the research of dyslipidemia in obesity, with an emphasis on female-specific disorders and cardiometabolic risk.

RECENT FINDINGS

The focus of current research on dyslipidemia in obesity is moving toward structurally and functionally modified plasma lipoproteins. Special attention is paid to the pro-atherogenic role of triglyceride-rich lipoproteins and their remnants. Introduction of advanced analytical techniques enabled identification of novel lipid biomarkers with potential clinical applications. In particular, proteomic and lipidomic studies have provided significant progress in the comprehensive research of HDL's alterations in obesity. Obesity-related dyslipidemia is a widespread metabolic disturbance in polycystic ovary syndrome patients and high-risk pregnancies, but is seldom evaluated with respect to its impact on future cardiometabolic health. Obesity and associated cardiometabolic diseases require a more depth insight into the quality of lipoprotein particles. Further application of omics-based techniques would enable a more comprehensive evaluation of dyslipidemia in order to reduce an excessive cardiovascular risk attributable to increased body weight. However, more studies on obesity-related female reproductive disorders are needed for this approach to be adopted in daily clinical practice.

Serum ceramides in early pregnancy as predictors of gestational diabetes

Ceramides contribute to the development of type 2 diabetes but it is uncertain whether they predict gestational diabetes (GDM). In this multicentre case-control study including 1040 women with GDM and 958 non-diabetic controls, early pregnancy (mean 10.7 gestational weeks) concentrations of four ceramides-Cer(d18:1/16:0), Cer(d18:1/18:0), Cer(d18:1/24:0) and Cer(d18:1/24:1)-were determined by a validated mass-spectrometric method from biobanked serum samples. Traditional lipids including total cholesterol, LDL, HDL and triglycerides were measured. Logistic and linear regression and the LASSO logistic regression were used to analyse lipids and clinical risk factors in the prediction of GDM. The concentrations of four targeted ceramides and total cholesterol, LDL and triglycerides were higher and HDL was lower among women with subsequent GDM than among controls. After adjustments, Cer(d18:1/24:0), triglycerides and LDL were independent predictors of GDM, women in their highest quartile had 1.44-fold (95% CI 1.07-1.95), 2.17-fold (95% CI 1.57-3.00) and 1.63-fold (95% CI 1.19-2.24) odds for GDM when compared to their lowest quartiles, respectively. In the LASSO regression modelling ceramides did not appear to markedly improve the predictive performance for GDM alongside with clinical risk factors and triglycerides. However, their adverse alterations highlight the extent of metabolic disturbances involved in GDM.

Gestational Diabetes Mellitus, Postpartum Lipidomic Signatures, and Subsequent Risk of Type 2 Diabetes: A Lipidome-Wide Association Study

OBJECTIVE

To identify a postpartum lipidomic signature associated with gestational diabetes mellitus (GDM) and investigate the role of the identified lipids in the progression to type 2 diabetes (T2D).

RESEARCH DESIGN AND METHODS

This prospective cohort study enrolled 1,409 women at 24-72 h after delivery of a singleton baby and followed them prospectively at the Boston Medical Center. The lipidome was profiled by liquid chromatography-tandem mass spectrometry. Diagnoses of GDM and incident T2D were extracted from medical records and verified using plasma glucose levels.

RESULTS

Mean (SD) age of study women at baseline was 28.5 (6.6) years. A total of 219 (16.4%) women developed incident diabetes over a median follow-up of 11.8 (interquartile range 8.2-14.8) years. We identified 33 postpartum lipid species associated with GDM, including 16 inverse associations (primarily cholesterol esters and phosphatidylcholine plasmalogens), and 17 positive associations (primarily diacyglycerols and triacyglycerols). Of these, four were associated with risk of incident T2D and mediated ∼12% of the progression from GDM to T2D. The identified lipid species modestly improved the predictive performance for incident T2D above classical risk factors when the entire follow-up period was considered.

CONCLUSIONS

GDM was associated with a wide range of lipid metabolic alterations at early postpartum, among which some lipid species were also associated with incident T2D and mediated the progression from GDM to T2D. The improvements attained by including lipid species in the prediction of T2D provides new insights regarding the early detection and prevention of progression to T2D.

Liver biomarkers, lipid metabolites, and risk of gestational diabetes mellitus in a prospective study among Chinese pregnant women

BACKGROUND

Liver plays an important role in maintaining glucose homeostasis. We aimed to examine the associations of liver enzymes and hepatic steatosis index (HSI, a reliable biomarker for non-alcoholic fatty liver disease) in early pregnancy with subsequent GDM risk, as well as the potential mediation effects of lipid metabolites on the association between HSI and GDM.

METHODS

In a birth cohort, liver enzymes were measured in early pregnancy (6-15 gestational weeks, mean 10) among 6,860 Chinese women. Multivariable logistic regression was performed to examine the association between liver biomarkers and risk of GDM. Pearson partial correlation and least absolute shrinkage and selection operator (LASSO) regression were conducted to identify lipid metabolites that were significantly associated with HSI in a subset of 948 women. Mediation analyses were performed to estimate the mediating roles of lipid metabolites on the association of HSI with GDM.

RESULTS

Liver enzymes and HSI were associated with higher risks of GDM after adjustment for potential confounders, with ORs ranging from 1.42 to 2.24 for extreme-quartile comparisons (false discovery rate-adjusted P-trend ≤0.005). On the natural log scale, each SD increment of alanine aminotransferase, aspartate aminotransferase, gamma-glutamyl transferase, alkaline phosphatase, and HSI was associated with a 1.15-fold (95% CI: 1.05, 1.26), 1.10-fold (1.01, 1.20), 1.21-fold (1.10, 1.32), 1.15-fold (1.04, 1.27), and 1.33-fold (1.18, 1.51) increased risk of GDM, respectively. Pearson partial correlation and LASSO regression identified 15 specific lipid metabolites in relation to HSI. Up to 52.6% of the association between HSI and GDM risk was attributed to the indirect effect of the HSI-related lipid score composed of lipid metabolites predominantly from phospholipids (e.g., lysophosphatidylcholine and ceramides) and triacylglycerol.

CONCLUSIONS

Elevated liver enzymes and HSI in early pregnancy, even within a normal range, were associated with higher risks of GDM among Chinese pregnant women. The association of HSI with GDM was largely mediated by altered lipid metabolism.

In utero metabolomic signatures of refined grain intake and risk of gestational diabetes: A metabolome-wide association study

BACKGROUND

Epidemiologic evidence has linked refined grain intake to a higher risk of gestational diabetes (GDM), but the biological underpinnings remain unclear.

OBJECTIVES

We aimed to identify and validate refined grain-related metabolomic biomarkers for GDM risk.

METHODS

In a metabolome-wide association study of 91 cases with GDM and 180 matched controls without GDM (discovery set) nested in the prospective Pregnancy Environment and Lifestyle Study (PETALS), refined grain intake during preconception and early pregnancy and serum untargeted metabolomics were assessed at gestational weeks 10-13. We identified refined grain-related metabolites using multivariable linear regression and examined their prospective associations with GDM risk using conditional logistic regression. We further examined the predictivity of refined grain-related metabolites selected by least absolute shrinkage and selection operator regression in the discovery set and validation set (a random PETALS subsample of 38 individuals with and 336 without GDM).

RESULTS

Among 821 annotated serum (87.4% fasting) metabolites, 42 were associated with refined grain intake, of which 17 (70.6% in glycerolipids, glycerophospholipids, and sphingolipids clusters) were associated with subsequent GDM risk (all false discovery rate-adjusted P values <0.05). Adding 7 of 17 metabolites to a conventional risk factor-based prediction model increased the C-statistic for GDM risk in the discovery set from 0.71 (95% CI: 0.64, 0.77) to 0.77 (95% CI: 0.71, 0.83) and in the validation set from 0.77 (95% CI: 0.69, 0.86) to 0.81 (95% CI: 0.74, 0.89), both with P-for-difference <0.05.

CONCLUSIONS

Clusters of glycerolipids, glycerophospholipids, and sphingolipids may be implicated in the association between refined grain intake and GDM risk, as demonstrated by the significant associations of these metabolites with both refined grains and GDM risk and the incremental predictive value of these metabolites for GDM risk beyond the conventional risk factors. These findings provide evidence on the potential biological underpinnings linking refined grain intake to the risk of GDM and help identify novel disease-related dietary biomarkers to inform diet-related preventive strategies for GDM.

Exploring preconception signatures of metabolites in mothers with gestational diabetes mellitus using a non-targeted approach

BACKGROUND

Metabolomic changes during pregnancy have been suggested to underlie the etiology of gestational diabetes mellitus (GDM). However, research on metabolites during preconception is lacking. Therefore, this study aimed to investigate distinctive metabolites during the preconception phase between GDM and non-GDM controls in a nested case-control study in Singapore.

METHODS

Within a Singapore preconception cohort, we included 33 Chinese pregnant women diagnosed with GDM according to the IADPSG criteria between 24 and 28 weeks of gestation. We then matched them with 33 non-GDM Chinese women by age and pre-pregnancy body mass index (ppBMI) within the same cohort. We performed a non-targeted metabolomics approach using fasting serum samples collected within 12 months prior to conception. We used generalized linear mixed model to identify metabolites associated with GDM at preconception after adjusting for maternal age and ppBMI. After annotation and multiple testing, we explored the additional predictive value of novel signatures of preconception metabolites in terms of GDM diagnosis.

RESULTS

A total of 57 metabolites were significantly associated with GDM, and eight phosphatidylethanolamines were annotated using HMDB. After multiple testing corrections and sensitivity analysis, phosphatidylethanolamines 36:4 (mean difference β: 0.07; 95% CI: 0.02, 0.11) and 38:6 (β: 0.06; 0.004, 0.11) remained significantly higher in GDM subjects, compared with non-GDM controls. With all preconception signals of phosphatidylethanolamines in addition to traditional risk factors (e.g., maternal age and ppBMI), the predictive value measured by area under the curve (AUC) increased from 0.620 to 0.843.

CONCLUSIONS

Our data identified distinctive signatures of GDM-associated preconception phosphatidylethanolamines, which is of potential value to understand the etiology of GDM as early as in the preconception phase. Future studies with larger sample sizes among alternative populations are warranted to validate the associations of these signatures of metabolites and their predictive value in GDM.

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.

Exploration of Lipid Metabolism Alterations in Children with Active Tuberculosis Using UHPLC-MS/MS

Metabolic profiling using nonsputum samples has demonstrated excellent performance in diagnosing infectious diseases. But little is known about the lipid metabolism alternation in children with tuberculosis (TB). Therefore, the study was performed to explore lipid metabolic changes caused by Mycobacterium tuberculosis infection and identify specific lipids as diagnostic biomarkers in children with TB using UHPLC-MS/MS. Plasma samples obtained from 70 active TB children, 21 non-TB infectious disease children, and 21 healthy controls were analyzed by a partial least-squares discriminant analysis model in the training set, and 12 metabolites were identified that can separate children with TB from non-TB controls. In the independent testing cohort with 49 subjects, three of the markers, PC (15:0/17:1), PC (17:1/18:2), and PE (18:1/20:3), presented with high diagnostic values. The areas under the curve of the three metabolites were 0.904, 0.833, and 0.895, respectively. The levels of the altered lipid metabolites were found to be associated with the severity of the TB disease. Taken together, plasma lipid metabolites are potentially useful for diagnosis of active TB in children and would provide insights into the pathogenesis of the disease.

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.

Metabolomics in gestational diabetes mellitus: A review

Gestational diabetes mellitus (GDM), a common complication of pregnancy, is a type of diabetes that is first detected and diagnosed during pregnancy. The incidence of GDM is increasing annually and is associated with many adverse pregnancy outcomes. Early prediction of the risk of GDM and intervention are thus important to reduce adverse pregnancy outcomes. Studies have revealed a correlation between the levels of amino acids, fatty acids, triglycerides, and other metabolites in early pregnancy and the occurrence of GDM. The development of high-throughput technologies used in metabolomics has enabled the detection of changes in the levels of small-molecule metabolites during early pregnancy, which can help reflect the overall physiological and pathological status of the body and explore the underlying mechanisms of the development of GDM. This review sought to summarize current research in this field and provide data for the development of strategies to manage GDM.

An anchored experimental design and meta-analysis approach to address batch effects in large-scale metabolomics

Untargeted metabolomics studies are unbiased but identifying the same feature across studies is complicated by environmental variation, batch effects, and instrument variability. Ideally, several studies that assay the same set of metabolic features would be used to select recurring features to pursue for identification. Here, we developed an anchored experimental design. This generalizable approach enabled us to integrate three genetic studies consisting of 14 test strains of Caenorhabditis elegans prior to the compound identification process. An anchor strain, PD1074, was included in every sample collection, resulting in a large set of biological replicates of a genetically identical strain that anchored each study. This enables us to estimate treatment effects within each batch and apply straightforward meta-analytic approaches to combine treatment effects across batches without the need for estimation of batch effects and complex normalization strategies. We collected 104 test samples for three genetic studies across six batches to produce five analytical datasets from two complementary technologies commonly used in untargeted metabolomics. Here, we use the model system C. elegans to demonstrate that an augmented design combined with experimental blocks and other metabolomic QC approaches can be used to anchor studies and enable comparisons of stable spectral features across time without the need for compound identification. This approach is generalizable to systems where the same genotype can be assayed in multiple environments and provides biologically relevant features for downstream compound identification efforts. All methods are included in the newest release of the publicly available SECIMTools based on the open-source Galaxy platform.

Population-based plasma lipidomics reveals developmental changes in metabolism and signatures of obesity risk: a mother-offspring cohort study

BACKGROUND

Lipids play a vital role in health and disease, but changes to their circulating levels and the link with obesity remain poorly characterized in expecting mothers and their offspring in early childhood.

METHODS

LC-MS/MS-based quantitation of 480 lipid species was performed on 2491 plasma samples collected at 4 time points in the mother-offspring Asian cohort GUSTO (Growing Up in Singapore Towards healthy Outcomes). These 4 time points constituted samples collected from mothers at 26-28 weeks of gestation (n=752) and 4-5 years postpartum (n=650), and their offspring at birth (n=751) and 6 years of age (n=338). Linear regression models were used to identify the pregnancy and developmental age-specific variations in the plasma lipidomic profiles, and their association with obesity risk. An independent birth cohort (n=1935), the Barwon Infant Study (BIS), comprising mother-offspring dyads of Caucasian origin was used for validation.

RESULTS

Levels of 36% of the profiled lipids were significantly higher (absolute fold change > 1.5 and P_adj < 0.05) in antenatal maternal circulation as compared to the postnatal phase, with phosphatidylethanolamine levels changing the most. Compared to antenatal maternal lipids, cord blood showed lower concentrations of most lipid species (79%) except lysophospholipids and acylcarnitines. Changes in lipid concentrations from birth to 6 years of age were much higher in magnitude (log₂FC=-2.10 to 6.25) than the changes observed between a 6-year-old child and an adult (postnatal mother) (log₂FC=-0.68 to 1.18). Associations of cord blood lipidomic profiles with birth weight displayed distinct trends compared to the lipidomic profiles associated with child BMI at 6 years. Comparison of the results between the child and adult BMI identified similarities in association with consistent trends (R²=0.75). However, large number of lipids were associated with BMI in adults (67%) compared to the children (29%). Pre-pregnancy BMI was specifically associated with decrease in the levels of phospholipids, sphingomyelin, and several triacylglycerol species in pregnancy.

CONCLUSIONS

In summary, our study provides a detailed landscape of the in utero lipid environment provided by the gestating mother to the growing fetus, and the magnitude of changes in plasma lipidomic profiles from birth to early childhood. We identified the effects of adiposity on the circulating lipid levels in pregnant and non-pregnant women as well as offspring at birth and at 6 years of age. Additionally, the pediatric vs maternal overlap of the circulating lipid phenotype of obesity risk provides intergenerational insights and early opportunities to track and intervene the onset of metabolic adversities.

CLINICAL TRIAL REGISTRATION

This birth cohort is a prospective observational study, which was registered on 1 July 2010 under the identifier NCT01174875 .

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.

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.

Predictive values of serum metabolites in early pregnancy and their possible pathways for gestational diabetes: A nested case-control study in Tianjin, China

AIMS

To investigate the associations and predictive values of serum metabolites in early pregnancy for later development of gestational diabetes mellitus (GDM), and further explore their metabolic pathways to GDM.

METHODS

We conducted a 1:1 nested case-control study including 486 pregnant women from Tianjin, China, and collected blood samples at their first registration (median at 10th gestational week). Liquid chromatography-tandem mass spectrometry was used to measure serum metabolites. Orthogonal partial least squares discriminant analysis was used to select specific metabolites associated with GDM, and pathway analysis was used to identify the metabolic pathways related to GDM.

RESULTS

A total of 64 serum metabolites were included in this analysis, 17 of which were identified as specific metabolites associated with GDM. Ten metabolites increased and seven metabolites decreased GDM risk. Inclusion of these specific metabolites to the model of traditional risk factors greatly increased the predictive value from 0.69 (95% confidence interval: 0.64-0.74) to 0.92 (0.90-0.95). In addition, we found that glycerophospholipid metabolism, sphingolipid metabolism and primary bile acid biosynthesis were main metabolic pathways related to GDM.

CONCLUSION

We identified a set of serum metabolites and their metabolic pathways in early pregnancy associated with GDM, which provided a theoretical basis for further research on the molecular pathways to GDM and early identification of GDM.

Metabolites involved in purine degradation, insulin resistance, and fatty acid oxidation are associated with prediction of Gestational diabetes in plasma

INTRODUCTION

Gestational diabetes mellitus (GDM) significantly increases maternal and fetal health risks, but factors predictive of GDM are poorly understood.

OBJECTIVES

Plasma metabolomics analyses were conducted in early pregnancy to identify potential metabolites associated with prediction of GDM.

METHODS

Sixty-eight pregnant women with overweight/obesity from a clinical trial of a lifestyle intervention were included. Participants who developed GDM (n = 34; GDM group) were matched on treatment group, age, body mass index, and ethnicity with those who did not develop GDM (n = 34; Non-GDM group). Blood draws were completed early in pregnancy (10-16 weeks). Plasma samples were analyzed by UPLC-MS using three metabolomics assays.

RESULTS

One hundred thirty moieties were identified. Thirteen metabolites including pyrimidine/purine derivatives involved in uric acid metabolism, carboxylic acids, fatty acylcarnitines, and sphingomyelins (SM) were different when comparing the GDM vs. the Non-GDM groups (p < 0.05). The most significant differences were elevations in the metabolites' hypoxanthine, xanthine and alpha-hydroxybutyrate (p < 0.002, adjusted p < 0.02) in GDM patients. A panel consisting of four metabolites: SM 14:0, hypoxanthine, alpha-hydroxybutyrate, and xanthine presented the highest diagnostic accuracy with an AUC = 0.833 (95% CI: 0.572686-0.893946), classifying as a "very good panel".

CONCLUSION

Plasma metabolites mainly involved in purine degradation, insulin resistance, and fatty acid oxidation, were altered in early pregnancy in connection with subsequent GDM development.