The application of metabolomics analysis in the research of gestational diabetes mellitus and preeclampsia

Paradoxes: Cholesterol and Hypoxia in Preeclampsia

Preeclampsia, a hypertensive disease of pregnancy of unknown etiology, is intensely studied as a model of cardiovascular disease (CVD) not only due to multiple shared pathologic elements but also because changes that develop over decades in CVD appear and resolve within days in preeclampsia. Those affected by preeclampsia and their offspring experience increased lifetime risks of CVD. At the systemic level, preeclampsia is characterized by increased cellular, membrane, and blood levels of cholesterol; however, cholesterol-dependent signaling, such as canonical Wnt/βcatenin, Hedgehog, and endothelial nitric oxide synthase, is downregulated indicating a cholesterol deficit with the upregulation of cholesterol synthesis and efflux. Hypoxia-related signaling in preeclampsia also appears to be paradoxical with increased Hypoxia-Inducible Factors in the placenta but measurably increased oxygen in maternal blood in placental villous spaces. This review addresses the molecular mechanisms by which excessive systemic cholesterol and deficient cholesterol-dependent signaling may arise from the effects of dietary lipid variance and environmental membrane modifiers causing the cellular hypoxia that characterizes preeclampsia.

Palmitate and group B Streptococcus synergistically and differentially induce IL-1β from human gestational membranes

Introduction

Rupture of the gestational membranes often precedes major pregnancy complications, including preterm labor and preterm birth. One major cause of inflammation in the gestational membranes, chorioamnionitis (CAM) is often a result of bacterial infection. The commensal bacterium Streptococcus agalactiae, or Group B Streptococcus (GBS) is a leading infectious cause of CAM. Obesity is on the rise worldwide and roughly 1 in 4 pregnancy complications is related to obesity, and individuals with obesity are also more likely to be colonized by GBS. The gestational membranes are comprised of several distinct cell layers which are, from outermost to innermost: maternally-derived decidual stromal cells (DSCs), fetal cytotrophoblasts (CTBs), fetal mesenchymal cells, and fetal amnion epithelial cells (AECs). In addition, the gestational membranes have several immune cell populations; macrophages are the most common phagocyte. Here we characterize the effects of palmitate, the most common long-chain saturated fatty acid, on the inflammatory response of each layer of the gestational membranes when infected with GBS, using human cell lines and primary human tissue.

Results

Palmitate itself slightly but significantly augments GBS proliferation. Palmitate and GBS co-stimulation synergized to induce many inflammatory proteins and cytokines, particularly IL-1β and matrix metalloproteinase 9 from DSCs, CTBs, and macrophages, but not from AECs. Many of these findings are recapitulated when treating cells with palmitate and a TLR2 or TLR4 agonist, suggesting broad applicability of palmitate-pathogen synergy. Co-culture of macrophages with DSCs or CTBs, upon co-stimulation with GBS and palmitate, resulted in increased inflammatory responses, contrary to previous work in the absence of palmitate. In whole gestational membrane biopsies, the amnion layer appeared to dampen immune responses from the DSC and CTB layers (the choriodecidua) to GBS and palmitate co-stimulation. Addition of the monounsaturated fatty acid oleate, the most abundant monounsaturated fatty acid in circulation, dampened the proinflammatory effect of palmitate.

Discussion

These studies reveal a complex interplay between the immunological response of the distinct layers of the gestational membrane to GBS infection and that such responses can be altered by exposure to long-chain saturated fatty acids. These data provide insight into how metabolic syndromes such as obesity might contribute to an increased risk for GBS disease during pregnancy.

Potential Causal Association between Plasma Metabolites, Immunophenotypes, and Female Reproductive Disorders: A Two-Sample Mendelian Randomization Analysis

BACKGROUND

While extensive research highlighted the involvement of metabolism and immune cells in female reproductive diseases, causality remains unestablished.

METHODS

Instrumental variables for 486 circulating metabolites (N = 7824) and 731 immunophenotypes (N = 3757) were derived from a genome-wide association study (GWAS) meta-analysis. FinnGen contributed data on 14 female reproductive disorders. A bidirectional two-sample Mendelian randomization study was performed to determine the relationships between exposures and outcomes. The robustness of results, potential heterogeneity, and horizontal pleiotropy were examined through sensitivity analysis.

RESULTS

High levels of mannose were found to be causally associated with increased risks of gestational diabetes (GDM) (OR [95% CI], 6.02 [2.85-12.73], p = 2.55 × 10-6). A genetically predicted elevation in the relative count of circulating CD28-CD25++CD8+ T cells was causally related to increased female infertility risk (OR [95% CI], 1.26 [1.14-1.40], p = 1.07 × 10-5), whereas a high absolute count of NKT cells reduced the risk of ectopic pregnancy (OR [95% CI], 0.87 [0.82-0.93], p = 5.94 × 10-6). These results remained consistent in sensitivity analyses.

CONCLUSIONS

Our study supports mannose as a promising GDM biomarker and intervention target by integrating metabolomics and genomics.

Preterm preeclampsia screening using biomarkers: combining phenotypic classifiers into robust prediction models

BACKGROUND

Preeclampsia screening is a critical component of antenatal care worldwide. Currently, the most developed screening test for preeclampsia at 11 to 13 weeks' gestation integrates maternal demographic characteristics and medical history with 3 biomarkers-serum placental growth factor, mean arterial pressure, and uterine artery pulsatility index-to identify approximately 75% of women who develop preterm preeclampsia with delivery before 37 weeks of gestation. It is generally accepted that further improvements to preeclampsia screening require the use of additional biomarkers. We recently reported that the levels of specific metabolites and metabolite ratios are associated with preterm preeclampsia. Notably, for several of these markers, preterm preeclampsia prediction varied according to maternal body mass index class. These findings motivated us to study whether patient classification allowed for combining metabolites with the current biomarkers more effectively to improve prediction of preterm preeclampsia.

OBJECTIVE

This study aimed to investigate whether metabolite biomarkers can improve biomarker-based preterm preeclampsia prediction in 3 screening resource scenarios according to the availability of: (1) placental growth factor, (2) placental growth factor+mean arterial pressure, and (3) placental growth factor+mean arterial pressure+uterine artery pulsatility index.

STUDY DESIGN

This was an observational case-control study, drawn from a large prospective screening study at 11 to 13 weeks' gestation on the prediction of pregnancy complications, conducted at King's College Hospital, London, United Kingdom. Maternal blood samples were also collected for subsequent research studies. We used liquid chromatography-mass spectrometry to quantify levels of 50 metabolites previously associated with pregnancy complications in plasma samples from singleton pregnancies. Biomarker data, normalized using multiples of medians, on 1635 control and 106 preterm preeclampsia pregnancies were available for model development. Modeling was performed using a methodology that generated a prediction model for preterm preeclampsia in 4 consecutive steps: (1) z-normalization of predictors, (2) combinatorial modeling of so-called (weak) classifiers in the unstratified patient set and in discrete patient strata based on body mass index and/or race, (3) selection of classifiers, and (4) aggregation of the selected classifiers (ie, bagging) into the final prediction model. The prediction performance of models was evaluated using the area under the receiver operating characteristic curve, and detection rate at 10% false-positive rate.

RESULTS

First, the predictor development methodology itself was evaluated. The patient set was split into a training set (2/3) and a test set (1/3) for predictor model development and internal validation. A prediction model was developed for each of the 3 different predictor panels, that is, placental growth factor+metabolites, placental growth factor+mean arterial pressure+metabolites, and placental growth factor+mean arterial pressure+uterine artery pulsatility index+metabolites. For all 3 models, the area under the receiver operating characteristic curve in the test set did not differ significantly from that of the training set. Next, a prediction model was developed using the complete data set for the 3 predictor panels. Among the 50 metabolites available for modeling, 26 were selected across the 3 prediction models; 21 contributed to at least 2 out of the 3 prediction models developed. Each time, area under the receiver operating characteristic curve and detection rate were significantly higher with the new prediction model than with the reference model. Markedly, the estimated detection rate with the placental growth factor+mean arterial pressure+metabolites prediction model in all patients was 0.58 (95% confidence interval, 0.49-0.70), a 15% increase (P<.001) over the detection rate of 0.43 (95% confidence interval, 0.33-0.55) estimated for the reference placental growth factor+mean arterial pressure. The same prediction model significantly improved detection in Black (14%) and White (19%) patients, and in the normal-weight group (18.5≤body mass index<25) and the obese group (body mass index≥30), with respectively 19% and 20% more cases detected, but not in the overweight group, when compared with the reference model. Similar improvement patterns in detection rates were found in the other 2 scenarios, but with smaller improvement amplitudes.

CONCLUSION

Metabolite biomarkers can be combined with the established biomarkers of placental growth factor, mean arterial pressure, and uterine artery pulsatility index to improve the biomarker component of early-pregnancy preterm preeclampsia prediction tests. Classification of the pregnant women according to the maternal characteristics of body mass index and/or race proved instrumental in achieving improved prediction. This suggests that maternal phenotyping can have a role in improving the prediction of obstetrical syndromes such as preeclampsia.

Dairy Milk Casein and Whey Proteins Differentially Alter the Postprandial Lipidome in Persons with Prediabetes: A Comparative Lipidomics Study

Dairy milk, likely through its bioactive proteins, has been reported to attenuate postprandial hyperglycemia-induced oxidative stress responses implicated in cardiovascular diseases (CVDs). However, how its major proteins, whey and casein, alter metabolic excursions of the lipidome in persons with prediabetes is unclear. Therefore, the objective of this study was to examine whey or casein protein ingestion on glucose-induced alternations in lipidomic responses in adults (17 males and 6 females) with prediabetes. In this clinical study, participants consumed glucose alone, glucose + nonfat milk (NFM), or glucose with either whey (WHEY) or casein (CASEIN) protein, and plasma samples were collected at multiple time points. Lipidomics data from plasma samples was acquired using an ultra-high-performance liquid chromatography-high-resolution mass spectrometry-based platform. Our results indicated that glucose ingestion alone induced the largest number of changes in plasma lipids. WHEY showed an earlier and stronger impact to maintain the stability of the lipidome compared with CASEIN. WHEY protected against glucose-induced changes in glycerophospholipid and sphingolipid (SP) metabolism, while ether lipid metabolism and SP metabolism were the pathways most greatly impacted in CASEIN. Meanwhile, the decreased acyl carnitines and fatty acid (FA) 16:0 levels could attenuate lipid peroxidation after protein intervention to protect insulin secretory capacity. Diabetes-associated lipids, the increased phosphatidylethanolamine (PE) 34:2 and decreased phosphatidylcholine (PC) 34:3 in the NFM-T90 min, elevated PC 35:4 and decreased CE 18:1 to a CE 18:2 ratio in the WHEY-T180 min, decreased lysophosphatidylcholine (LPC) 22:6 and LPC 22:0/0:0 in the CASEIN-T90 min, and decreased PE 36:1 in the CASEIN-T180 min, indicating a decreased risk for prediabetes. Collectively, our study suggested that dairy milk proteins are responsible for the protective effect of non-fat milk on glucose-induced changes in the lipidome, which may potentially influence long-term CVD risk.

Identification of Biomarkers for Preeclampsia Based on Metabolomics

Background

Preeclampsia (PE) is a significant cause of maternal and neonatal morbidity and mortality worldwide. However, the pathogenesis of PE is unclear and reliable early diagnostic methods are still lacking. The purpose of this review is to summarize potential metabolic biomarkers and pathways of PE, which might facilitate risk prediction and clinical diagnosis, and obtain a better understanding of specific metabolic mechanisms of PE.

Methods

This review included human metabolomics studies related to PE in the PubMed, Google Scholar, and Web of Science databases from January 2000 to November 2021. The reported metabolic biomarkers were systematically examined and compared. Pathway analysis was conducted through the online software MetaboAnalyst 5.0.

Results

Forty-one human studies were included in this systematic review. Several metabolites, such as creatinine, glycine, L-isoleucine, and glucose and biomarkers with consistent trends (decanoylcarnitine, 3-hydroxyisovaleric acid, and octenoylcarnitine), were frequently reported. In addition, eight amino acid metabolism-related, three carbohydrate metabolism-related, one translation-related and one lipid metabolism-related pathways were identified. These biomarkers and pathways, closely related to renal dysfunction, insulin resistance, lipid metabolism disorder, activated inflammation, and impaired nitric oxide production, were very likely to contribute to the progression of PE.

Conclusion

This study summarized several metabolites and metabolic pathways, which may be associated with PE. These high-frequency differential metabolites are promising to be biomarkers of PE for early diagnosis, and the prominent metabolic pathway may provide new insights for the understanding of the pathogenesis of PE.

Effect of Carbohydrate-Restricted Dietary Pattern on Insulin Treatment Rate, Lipid Metabolism and Nutritional Status in Pregnant Women with Gestational Diabetes in Beijing, China

Carbohydrates play an important role in blood glucose control in pregnant women with GDM. Carbohydrate-restricted dietary (CRD) pattern for gestational diabetes mellitus (GDM) has been widely used in clinics, but the change in insulin utilization rate beyond CRD intervention in GDM remains unclear. The aim of the present study was to explore the application of insulin in pregnancy with GDM, as well as the influence of CRD pattern on lipid metabolism and nutritional state. A retrospective study of 265 women with GDM who delivered in Peking University People’s Hospital from July 2018 to January 2020 was conducted using a questionnaire survey. Women were divided into a CRD group or a control group according to whether they had received CRD intervention during pregnancy. There was no statistically significant difference in the rate of insulin therapy between the two groups (p > 0.05), the initial gestational week of the CRD group combined with insulin treatment was significantly higher than that of the control group (p < 0.05), and the risk of insulin therapy was positively correlated with fasting plasma glucose (FPG) in early pregnancy (p < 0.05). The incidence of abnormal low-density lipoprotein cholesterol levels in the CRD group was significantly lower than that in the control group (p < 0.05). There were no significant differences in nutritional indexes between the two groups. The results indicate that CRD intervention may be effective in delaying the use of insulin and improving the blood lipids metabolism during GDM pregnancy, while nutritional status may not be significantly affected under CRD intervention, and a high FPG in early pregnancy with GDM may be a risk factor for combined insulin therapy with CRD intervention.

Metabolomics Applied to Cord Serum in Preeclampsia Newborns: Implications for Neonatal Outcomes

Preeclampsia (PE) is one of the leading causes of maternal and perinatal morbidity and mortality. However, it is still uncertain how PE affects neonate metabolism. We conducted an untargeted metabolomics analysis of cord blood to explore the metabolic changes in PE neonates. Umbilical cord serum samples from neonates with preeclampsia (n = 29) and non-preeclampsia (non-PE) (n = 32) pregnancies were analyzed using the UHPLC-QE-MS metabolomic platform. Different metabolites were screened, and pathway analysis was conducted. A subgroup analysis was performed among PE neonates to compare the metabolome between appropriate-for-gestational-age infants (n = 21) and small-for-gestational-age (SGA) infants (n = 8). A total of 159 different metabolites were detected in PE and non-PE neonates. Creatinine, N4-acetylcytidine, sphingomyelin (D18:1/16:0), pseudouridine, uric acid, and indolelactic acid were the most significant differential metabolites in the cord serum of PE neonates. Differential metabolite levels were elevated in PE neonates and were involved in the following metabolic pathways: glycine, serine, and threonine metabolism; sphingolipid, glyoxylate, and dicarboxylate metabolism; and arginine biosynthesis. In PE neonates, SGA neonates showed increased levels of hexacosanoyl carnitine and decreased abundance of 3-hydroxybutyric acid and 3-sulfinoalanine. Taurine-related metabolism and ketone body-related pathways were mainly affected. Based on the UHPLC-QE-MS metabolomics analysis, we identified the metabolic profiles of PE and SGA neonates. The abundance of metabolites related to certain amino acid, sphingolipid, and energy metabolism increased in the umbilical cord serum of PE neonates.

Application of Metabolomics in Various Types of Diabetes

Metabolomics is the analysis of numerous small molecules known as metabolites. Over the past few years, with the continuous development in metabolomics, it has been widely used in the detection, diagnosis, and treatment of diabetes and has demonstrated great benefits. At the same time, studies on diabetes and its complications have discovered the metabolic markers that are characteristic of diabetes. However, the pathogenesis of diabetes has yet to be clarified, as well as no complete cure. The mechanism of diabetes has not been completely elucidated, and its eradication treatment is not available. Thus, prevention of the onset of the disease and its treatment have become very important. In this review, we focused on the recent progress in the use of metabolites in diabetes and their complications, as well as understanding the impact of diabetes metabolites.

Placental metabolic profiling in gestational diabetes mellitus: An important role of fatty acids

Aim

Gestational diabetes mellitus (GDM) is the most common metabolic disorder during pregnancy. Accumulating studies have reported metabolites that are significantly associated with the development of GDM. However, studies on the metabolism of placenta, the most important organ of maternal‐fetal energy and material transport, are extremely rare. This study aimed to identify and discuss the relationship between differentially expressed metabolites (DEM) and clinical parameters of the mothers and newborns.

Methods

In this study, metabolites from 63 placenta tissues (32 GDM and 31 normal controls) were assayed by ultra‐performance liquid chromatography‐high resolution mass spectrometry (UPLC‐HRMS).

Results

A total of 1297 annotated metabolites were detected, of which 87 significantly different in GDM placenta. Lipids and lipid‐like molecules accounted for 62.1% of DEM as they were significantly enriched via the “biosynthesis of unsaturated fatty acids” and “fatty acid biosynthesis” pathways. Linoleic acid and α‐linolenic acid appeared to be good biomarkers for the prediction and diagnosis of GDM. In addition, the level of PC(14:0/18:0) was negatively correlated with neonatal weight. 14 metabolites significantly different in male and female offspring, with the most increase in female newborns.

Conclusion

Even if maternal blood glucose level is well controlled, there are still metabolic abnormalities in GDM. Lipids and lipid‐like molecules were the main differential metabolites, especially unsaturated fatty acids.

Serum and Amniotic Fluid Metabolic Profile Changes in Response to Gestational Diabetes Mellitus and the Association with Maternal-Fetal Outcomes

This study was designed to identify serum and amniotic fluid (AF) metabolic profile changes in response to gestational diabetes mellitus (GDM) and explore the association with maternal-fetal outcomes. We established the GDM rat models by combining a high-fat diet (HFD) with an injection of low-dose streptozotocin (STZ), detected the fasting plasma glucose (FPG) of pregnant rats in the second and third trimester, and collected AF and fetal rats by cesarean section on gestational day 19 (GD19), as well as measuring the weight and crown-rump length (CRL) of fetal rats. We applied liquid chromatography-tandem mass spectrometry (LC-MS/MS) for the untargeted metabolomics analyses of serum and AF samples and then explored their correlation with maternal-fetal outcomes via the co-occurrence network. The results showed that 91 and 68 metabolites were upregulated and 125 and 78 metabolites were downregulated in serum and AF samples exposed to GDM, respectively. In maternal serum, the obvious alterations emerged in lipids and lipid-like molecules, while there were great changes in carbohydrate and carbohydrate conjugates, followed by amino acids, peptides, and analogs in amniotic fluid. The altered pathways both in serum and AF samples were amino acid, lipid, nucleotide, and vitamin metabolism pathways. In response to GDM, changes in the steroid hormone metabolic pathway occurred in serum, and an altered carbohydrate metabolism pathway was found in AF samples. Among differential metabolites in two kinds of samples, there were 34 common biochemicals shared by serum and AF samples, and a mutual significant association existed. These shared differential metabolites were implicated in several metabolism pathways, including choline, tryptophan, histidine, and nicotinate and nicotinamide metabolism, and among them, N1-methyl-4-pyridone-3-carboxamide, 5'-methylthioadenosine, and kynurenic acid were significantly associated with both maternal FPG and fetal growth. In conclusion, serum and AF metabolic profiles were remarkably altered in response to GDM. N1-Methyl-4-pyridone-3-carboxamide, 5'-methylthioadenosine, and kynurenic acid have the potential to be taken as biomarkers for maternal-fetal health status of GDM. The common and inter-related differential metabolites both in the serum and AF implied the feasibility of predicting fetal health outcomes via detecting the metabolites in maternal serum exposed to GDM.

Unique Biomarker Characteristics in Gestational Diabetes Mellitus Identified by LC-MS-Based Metabolic Profiling

BACKGROUND

Gestational diabetes mellitus (GDM) is a type of glucose intolerance disorder that first occurs during women's pregnancy. The main diagnostic method for GDM is based on the midpregnancy oral glucose tolerance test. The rise of metabolomics has expanded the opportunity to better identify early diagnostic biomarkers and explore possible pathogenesis.

METHODS

We collected blood serum from 34 GDM patients and 34 normal controls for a LC-MS-based metabolomics study.

RESULTS

184 metabolites were increased and 86 metabolites were decreased in the positive ion mode, and 65 metabolites were increased and 71 were decreased in the negative ion mode. Also, it was found that the unsaturated fatty acid metabolism was disordered in GDM. Ten metabolites with the most significant differences were selected for follow-up studies. Since the diagnostic specificity and sensitivity of a single differential metabolite are not definitive, we combined these metabolites to prepare a ROC curve. We found a set of metabolite combination with the highest sensitivity and specificity, which included eicosapentaenoic acid, docosahexaenoic acid, docosapentaenoic acid, arachidonic acid, citric acid, α-ketoglutaric acid, and genistein. The area under the curves (AUC) value of those metabolites was 0.984 between the GDM and control group.

CONCLUSIONS

Our results provide a direction for the mechanism of GDM research and demonstrate the feasibility of developing a diagnostic test that can distinguish between GDM and normal controls clearly. Our findings were helpful to develop novel biomarkers for precision or personalized diagnosis for GDM. In addition, we provide a critical insight into the pathological and biological mechanisms for GDM.