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

First-Trimester Prediction of Gestational Diabetes Mellitus Based on Maternal Risk Factors

OBJECTIVE

To develop and validate a new first-trimester model for the prediction of gestational diabetes mellitus (GDM) based on maternal demographic characteristics and elements of medical history.

DESIGN

Prospective cohort study.

SETTING

Inner-city hospital.

POPULATION

41 587 women with singleton pregnancies at 11+0-13+6 weeks' gestation, including 4231 (10.2%) who subsequently developed GDM.

METHODS

Logistic regression model for GDM was developed and fivefold cross-validation was performed to assess the calibration and predictive performance of the model, assessed by the area under the receiver operating characteristic curve (AUROC) and detection rates (DRs) at different screen positive rates (SPRs).

MAIN OUTCOME MEASURE

GDM.

RESULTS

In both parous women with a previous history of GDM and nulliparous women or parous women with no history of GDM, significant contributors to the prediction of GDM were maternal age, weight, height, ethnicity and family history of diabetes mellitus. In parous women with no previous history of GDM, there was a contribution from the birthweight z-score of the previous pregnancy. There was good agreement between the predicted risk and observed incidence of GDM (intercept 0.000, 95% CI: -0.034, 0.034; slope 1.000, 95% CI: 0.967, 1.033). The AUROC curve was 0.757 (95% CI: 0.749, 0.765). The performance was higher for GDM treated with insulin versus metformin or diet alone. At SPR of 40%, the DR of the insulin, metformin and diet alone group was 87.2% (95% CI: 84.9, 89.3), 80.0% (77.8, 82.0) and 61.5% (59.2, 63.7), respectively.

CONCLUSION

Assessment of risk for GDM can be achieved in the first trimester based on maternal risk factors.

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 metabolic profiling of gestational diabetes mellitus: insights into early-onset and late-onset cases compared with healthy controls

Introduction

Gestational diabetes mellitus (GDM) is a global health concern with significant short and long-term complications for both mother and baby. Early prediction of GDM, particularly late-onset, is crucial for implementing timely interventions to mitigate adverse outcomes. In this study, we conducted a comprehensive metabolomic analysis to explore potential biomarkers for early GDM prediction.

Methods

Plasma samples were collected during the first trimester from 60 women: 20 with early-onset GDM, 20 with late-onset GDM, and 20 with normal glucose tolerance. Using advanced analytical techniques, including liquid chromatography-tandem mass spectrometry (LC-MS/MS) and gas chromatography-mass spectrometry (GC-MS), we profiled over 150 lipid species and central carbon metabolism intermediates.

Results

Significant metabolic alterations were observed in both early- and late-onset GDM groups compared to healthy controls, with a specific focus on glycerolipids, fatty acids, and glucose metabolism. Key findings revealed a 4.0-fold increase in TG(44:0), TG(46:0), TG(46:1) with p-values <0.001 and TG(46:2) with 4.7-fold increase and p-value <0.0001 as well as changes in several phospholipids as PC(38:3), PC(40:4) with 1.4-fold increase, p < 0.001 and PE(34:1), PE(34:2) and PE(36:2) with 1.5-fold change, p < 0.001 in late-onset GDM.

Discussion

Observed lipid changes highlight disruptions in energy metabolism and inflammatory pathways. It is suggested that lipid profiles with distinct fatty acid chain lengths and degrees of unsaturation can serve as early biomarkers of GDM risk. These findings underline the importance of integrating metabolomic insights with clinical data to develop predictive models for GDM. Such models could enable early risk stratification, allowing for timely dietary, lifestyle, or medical interventions aimed at optimizing glucose regulation and preventing complications such as preeclampsia, macrosomia, and neonatal metabolic disorders. By focusing on metabolic disruptions evident in the first trimester, this approach addresses a critical window for improving maternal and fetal outcomes. Our study demonstrates the value of metabolomics in understanding the metabolic perturbations associated with GDM. Future research is needed to validate these biomarkers in larger cohorts and assess their integration into clinical workflows for personalized pregnancy care.

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.

Early pregnancy serum PFAS are associated with alterations in the maternal lipidome

Per- and polyfluoroalkyl substances (PFAS) have been detected in the blood of humans and animals worldwide. Exposure to some PFAS are associated with multiple adverse pregnancy outcomes. Existing literature has identified a strong association with PFAS exposure and metabolic dysfunction in humans, including modification of lipid metabolism. Using a subset of the Michigan Mother-Infant Pairs cohort (n = 95), this study investigated associations between first trimester plasma levels of PFAS and maternal lipids and metabolites in the first trimester (T1), at the time of delivery (T3), and in the infant cord blood (CB) using untargeted shotgun lipidomics and metabolomics. Identifying PFAS-induced alterations in the maternal lipid- or metabolome at specific timepoints may help elucidate windows of susceptibility to adverse pregnancy outcomes. Out of 9 PFAS measured, 7 were detected in at least 20% of samples and were used for further analyses. PFOS and PFHxS were measured at the highest concentrations with medians of 5.76 ng/mL and 3.33 ng/mL, respectively. PFOA, PFNA, and PFDA had lower measured values with medians of <1.2 ng/mL. PFHxS concentrations were positively associated with monounsaturated sphingomyelins (SMs) in T1 maternal plasma in adjusted models, determined by an adjusted p-value (q) < 0.1. PFHxS was positively associated with saturated and polyunsaturated SMs and inversely associated with saturated diacylglycerols in T1. Following metabolite-specific analysis, two mono-unsaturated diacylglycerols with carbon chain lengths of 32 and 35 were inversely associated with PFHxS in T1. In T3, only the association between PFHxS and SMs remained, but was attenuated. In addition, PFDA was associated with an increase in polyunsaturated plasmenyl-phosphatidylethanolamines in T3. No associations were identified between PFAS and infant cord blood lipids. Continued research into PFAS associated disruptions in lipid metabolism at sensitive stages of gestation may provide insight into the mechanisms that lead to adverse birth and pregnancy outcomes.

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.

Second-trimester triglyceride-glucose index to predict adverse outcomes in women with gestational diabetes mellitus: A retrospective multicenter cohort study

AIMS/INTRODUCTION

Women with gestational diabetes mellitus are at high risk for adverse maternal and neonatal outcomes. The study aimed to evaluate the performance of the triglyceride-glucose index in predicting the risk of developing adverse outcomes in women with gestational diabetes mellitus.

MATERIALS AND METHODS

This retrospective multicenter cohort study included 8,808 pregnant women with gestational diabetes mellitus in two grade-A tertiary hospitals in China during 2018-2022. The triglyceride-glucose index was defined as ln [triglyceride (mg/dL) × fasting blood glucose (mg/dL)/2]. Significant adverse gestational diabetes mellitus outcomes were chosen by generalized linear models as the main outcomes. Multivariable logistic regression models evaluated their association with the triglyceride-glucose index. Areas under the receiver operating characteristic curves predicted adverse pregnancy outcomes. The prediction efficiency was validated in the sensitivity analysis dataset and validation cohort.

RESULTS

The triglyceride-glucose index was associated with preeclampsia, severe preeclampsia, preterm birth, placenta accreta spectrum, and macrosomia before and after adjusting for confounding factors (P < 0.05). The predictive performance of the triglyceride-glucose index was relatively moderate. Incorporating the triglyceride-glucose index into the baseline clinical risk model improved the area under curves for the diagnosis of preeclampsia (0.749 [0.714-0.784] vs 0.766 [0.734-0.798], P = 0.033) and macrosomia (0.664 [0.644-0.685] vs 0.676 [0.656-0.697], P = 0.002). These predictive models exhibited good calibration and robustness.

CONCLUSIONS

The triglyceride-glucose index is positively associated with preeclampsia, severe preeclampsia, preterm birth, placenta accreta spectrum, and macrosomia and is useful for the early prediction and prevention of adverse outcomes in women with gestational diabetes mellitus.

Lysophosphatidylcholine Impairs the Mitochondria Homeostasis Leading to Trophoblast Dysfunction in Gestational Diabetes Mellitus

Gestational diabetes mellitus (GDM) is a common pregnancy disorder associated with an increased risk of pre-eclampsia and macrosomia. Recent research has shown that the buildup of excess lipids within the placental trophoblast impairs mitochondrial function. However, the exact lipids that impact the placental trophoblast and the underlying mechanism remain unclear. GDM cases and healthy controls were recruited at Kaohsiung Medical University Hospital. The placenta and cord blood were taken during birth. Confocal and electron microscopy were utilized to examine the morphology of the placenta and mitochondria. We determined the lipid composition using liquid chromatography-mass spectrometry in data-independent analysis mode (LC/MSE). In vitro studies were carried out on choriocarcinoma cells (JEG3) to investigate the mechanism of trophoblast mitochondrial dysfunction. Results showed that the GDM placenta was distinguished by increased syncytial knots, chorangiosis, lectin-like oxidized low-density lipoprotein (LDL) receptor-1 (LOX-1) overexpression, and mitochondrial dysfunction. Lysophosphatidylcholine (LPC) 16:0 was significantly elevated in the cord blood LDL of GDM patients. In vitro, we demonstrated that LPC dose-dependently disrupts mitochondrial function by increasing reactive oxygen species (ROS) levels and HIF-1α signaling. In conclusion, highly elevated LPC in cord blood plays a pivotal role in GDM, contributing to trophoblast impairment and pregnancy complications.

The association between plasma angiopoietin-like protein 4, glucose and lipid metabolism during pregnancy, placental function, and risk of delivering large-for-gestational-age neonates

BACKGROUND

Large-for-gestational-age (LGA) neonates have increased risk of adverse pregnancy outcomes and adult metabolic diseases. We aimed to investigate the relationship between plasma angiopoietin-like protein 4 (ANGPTL4), a protein involved in lipid and glucose metabolism during pregnancy, placental function, growth factors, and the risk of LGA.

METHODS

We conducted a prospective cohort study and recruited women with singleton pregnancies at the National Taiwan University Hospital between 2013 and 2018. First trimester maternal plasma ANGPTL4 concentrations were measured.

RESULTS

Among 353 pregnant women recruited, the LGA group had higher first trimester plasma ANGPTL4 concentrations than the appropriate-for-gestational-age group. Plasma ANGPTL4 was associated with hemoglobin A1c, post-load plasma glucose, plasma triglyceride, plasma free fatty acid concentrations, plasma growth hormone variant (GH-V), and birth weight, but was not associated with cord blood growth factors. After adjusting for age, body mass index, hemoglobin A1c, and plasma triglyceride concentrations, plasma ANGPTL4 concentrations were significantly associated with LGA risk, and its predictive performance, as measured by the area under the receiver operating characteristic curve, outperformed traditional risk factors for LGA.

CONCLUSIONS

Plasma ANGPTL4 is associated with glucose and lipid metabolism during pregnancy, plasma GH-V, and birth weight, and is an early biomarker for predicting the risk of LGA.

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.

Investigation of the levels of essential and non-essential metals in women with and without abortion history: A study based on the Persian population of the Shahrekord cohort

Spontaneous abortion is a serious threat to the mothers' physical and mental well-being. The cause of spontaneous abortion is multifactorial disease. Prenatal non-essential metal exposure, particularly heavy metals, has been suggested to be associated with adverse pregnancy and birth outcomes. The purpose of this study was to investigate the relationship between the concentration of essential and non-essential metals including Pb, As, Zn, and Se and the risk of spontaneous abortion. In this case-control study the levels of Pb, As, Zn, and Se in the whole blood of 60 women with spontaneous abortion (case group) and also 60 women without spontaneous abortion (control group) were measured by atomic absorption spectrophotometry. Results revealed statistically significant reductions (P < 0.001) in whole blood levels of Zn and Se as well as the levels of As and Pb had a substantial elevation (P < 0.001) in cases compared to controls. According to the findings, repeated spontaneous abortion may be influenced by increasing whole blood levels of heavy metals such as As (OR = 17.53, P = 0.001) and Pb (OR = 15.58, P = 0.001) as well as decreasing levels of vital micronutrients Zn (OR = 0.20, P = 0.001) and Se (OR = 0.14, P = 0.001). The results of this study support the idea that limiting intake of non-essential metals during pregnancy can decrease the risk of spontaneous abortion. Overall, the information presented is expected to help plan future fundamental and applied investigations on the spontaneous abortion.

Combining HbA1c and insulin resistance to assess the risk of gestational diabetes mellitus: a prospective cohort study

OBJECTIVE

To investigate the association of glycated hemoglobin (HbA1c) and homeostasis model assessment insulin resistance (HOMA-IR) with gestational diabetes mellitus (GDM) risk.

METHODS

Data for this study were from a prospective cohort in Hangzhou, China. We included pregnant women with HbA1c, fasting insulin, and fasting glucose (FG) measured at 15-20 weeks of gestation and underwent oral glucose tolerance test (OGTT) at 24-28 weeks. Based on HbA1c and HOMA-IR, participants were divided into four groups. We estimated the odds ratios (OR) with 95% confidence intervals (CI) to assess the associations of HbA1c and HOMA-IR with GDM occurrence. Finally, we the potential additive interaction between HbA1c and HOMA-IR by calculating relative excess risk due to interaction (RERI) and the attributable proportion due to interaction (AP).

RESULT

462 pregnant women were included, of whom 136 (29.44%) developed GDM. Based on HbA1c and HOMA-IR, the study population was divided into four groups, with the percentages of each group being 51.30%, 15.58%, 20.56%, and 12.55%, respectively. The incidence of GDM increased with the increase of HOMA-IR and HbA1c, respectively, and the risk of GDM was significantly increased when both HOMA-IR and HbA1c were elevated. However, no such risk was observed in pregnant women < 35 years. Finally, we found significantly higher FG at 24-28 weeks in the high HOMA-IR and HbA1c group among GDM-positive pregnant women.

CONCLUSIONS

The incidence of GDM increased with increasing HbA1c and HOMA-IR, and the risk of GDM was significantly increased when both HbA1c and HOMA-IR were elevated. This finding may help to identify high-risk women for GDM early in pregnancy and provide timely interventions.