Maternal lipidomic signatures in relation to spontaneous preterm birth and large-for-gestational age neonates

Maternal Acylcarnitine Disruption as a Potential Predictor of Preterm Birth in Primigravida: A Preliminary Investigation

Preterm birth, defined as any birth before 37 weeks of completed gestation, poses adverse health risks to both mothers and infants. Despite preterm birth being associated with several risk factors, its relationship to maternal metabolism remains unclear, especially in first-time mothers. Aims of the present study were to identify maternal metabolic disruptions associated with preterm birth and to evaluate their predictive potentials. Blood was collected, and the serum harvested from the mothers of 24 preterm and 42 term births at 28-32 weeks gestation (onset of the 3rd trimester). Serum samples were assayed by untargeted metabolomic analyses via liquid chromatography/mass spectrometry (QTOF-LC/MS). Metabolites were annotated by inputting the observed mass-to-charge ratio into the Human Metabolome Database (HMDB). Analysis of 181 identified metabolites by PLS-DA modeling using SIMCA (v17) showed reasonable separation between the two groups (CV-ANOVA, p = 0.02). Further statistical analysis revealed lower serum levels of various acyl carnitines and amino acid metabolites in preterm mothers. Butenylcarnitine (C4:1), a short-chain acylcarnitine, was found to be the most predictive of preterm birth (AUROC = 0.73, [CI] 0.60-0.86). These observations, in conjuncture with past literature, reveal disruptions in fatty acid oxidation and energy metabolism in preterm primigravida. While these findings require validation, they reflect altered metabolic pathways that may be predictive of preterm delivery in primigravida.

First Trimester Prediction of Preterm Birth in Patient Plasma with Machine-Learning-Guided Raman Spectroscopy and Metabolomics

Preterm birth (PTB) is the leading cause of infant deaths globally. Current clinical measures often fail to identify women who may deliver preterm. Therefore, accurate screening tools are imperative for early prediction of PTB. Here, we show that Raman spectroscopy is a promising tool for studying biological interfaces, and we examine differences in the maternal metabolome of the first trimester plasma of PTB patients and those that delivered at term (healthy). We identified fifteen statistically significant metabolites that are predictive of the onset of PTB. Mass spectrometry metabolomics validates the Raman findings identifying key metabolic pathways that are enriched in PTB. We also show that patient clinical information alone and protein quantification of standard inflammatory cytokines both fail to identify PTB patients. We show for the first time that synergistic integration of Raman and clinical data guided with machine learning results in an unprecedented 85.1% accuracy of risk stratification of PTB in the first trimester that is currently not possible clinically. Correlations between metabolites and clinical features highlight the body mass index and maternal age as contributors of metabolic rewiring. Our findings show that Raman spectral screening may complement current prenatal care for early prediction of PTB, and our approach can be translated to other patient-specific biological interfaces.

Small molecule metabolites: discovery of biomarkers and therapeutic targets

Metabolic abnormalities lead to the dysfunction of metabolic pathways and metabolite accumulation or deficiency which is well-recognized hallmarks of diseases. Metabolite signatures that have close proximity to subject's phenotypic informative dimension, are useful for predicting diagnosis and prognosis of diseases as well as monitoring treatments. The lack of early biomarkers could lead to poor diagnosis and serious outcomes. Therefore, noninvasive diagnosis and monitoring methods with high specificity and selectivity are desperately needed. Small molecule metabolites-based metabolomics has become a specialized tool for metabolic biomarker and pathway analysis, for revealing possible mechanisms of human various diseases and deciphering therapeutic potentials. It could help identify functional biomarkers related to phenotypic variation and delineate biochemical pathways changes as early indicators of pathological dysfunction and damage prior to disease development. Recently, scientists have established a large number of metabolic profiles to reveal the underlying mechanisms and metabolic networks for therapeutic target exploration in biomedicine. This review summarized the metabolic analysis on the potential value of small-molecule candidate metabolites as biomarkers with clinical events, which may lead to better diagnosis, prognosis, drug screening and treatment. We also discuss challenges that need to be addressed to fuel the next wave of breakthroughs.

Lysophosphatidic acid production from lysophosphatidylcholine by lysophospholipase D activity of autotaxin in plasma of women with normal and adverse pregnancies

To identify biomarker lipids causing preterm delivery, we focused on lysophosphatidylcholine (LPC) and lysophosphatidic acid (LPA). The results of liquid chromatography-tandem mass spectrometry revealed that plasma levels of LPCs and LPAs were higher in the first and third (T3) trimesters of human normal and adverse pregnancies than in the second trimester, suggesting the direct metabolic conversion of LPC to LPA by lysophospholipase D (lysoPLD) activity of autotaxin. The elevated LPC and LPA levels in women with preterm deliveries in T3 were higher than in women with term deliveries under normal pregnancy in T3. We measured lysoPLD activity of diluted sera of pregnant women by quantification of choline released from exogenous LPC, and found progressive increases of lysoPLD activities in women with normal and adverse pregnancies. Ratios of lysoPLD activities for linoleoyl LPC to that for palmitoyl LPC were found to be decreased in pregnant women compared to that in non-pregnant women. These results may be due to the altered patterns of endogenous modulators for autotaxin and the profiles of the bound metal ion.

Maternal urinary phthalate metabolites are associated with lipidomic signatures among pregnant women in Puerto Rico

BACKGROUND

Phthalates have been reported to alter circulating lipid concentrations in animals, and investigation of these associations in humans will provide greater understanding of potential mechanisms for health outcomes.

OBJECTIVE

To explore associations between phthalate metabolite biomarkers and lipidomic profiles among pregnant women (n = 99) in the Puerto Rico PROTECT cohort.

METHODS

We measured 19 urinary phthalate metabolites during 24-28 weeks of pregnancy. Lipidomic profiles were identified from plasma samples by liquid chromatography-mass spectrometry-based shotgun lipidomics. Relationships between phthalate metabolites and lipid profiles were estimated using compound-by-compound comparisons in multiple linear regression and dimension reduction techniques. We derived sums for each lipid class and sub-class (saturated, mono-unsaturated, polyunsaturated) which were then regressed on phthalate metabolites. Associations were adjusted for false discovery.

RESULTS

After controlling for multiple comparisons, 33 phthalate-lipid associations were identified (False discovery rate adjusted p value < 0.05), and diacylglycerol 40:7 and plasmenyl-phosphatidylcholine 35:1 were the most strongly associated with multiple phthalate metabolites. Metabolites of di-2-ethylhexyl phthalate, bis(2-ethylhexyl) phthalate, dibutyl phthalates, and diisobutyl phthalate were associated with increased ceramides, lysophosphatidylcholines, lysophosphatidylethanolamines, and triacylglycerols, particularly those containing saturated and mono-unsaturated fatty acid chains.

SIGNIFICANCE

Characterization of associations between lipidomic markers and phthalate metabolites during pregnancy will yield mechanistic insight for maternal and child health outcomes.

IMPACT

This study leverages emerging technology to evaluate lipidome-wide signatures of phthalate exposure during pregnancy. The greatest lipid signatures of phthalate exposure were observed for diacylglycerol 40:7 and plasmenyl-phosphatidylcholine 35:1. Polymerized glycerides are important for energy production and regulated through hormone signaling, while plasmenyl-phosphatidylcholines have been implicated in membrane dynamics and important for cell-to-cell signaling. Characterization of these mechanisms are relevant for informing the etiology of maternal and children's health outcomes.

Maternal Metals/Metalloid Blood Levels Are Associated With Lipidomic Profiles Among Pregnant Women in Puerto Rico

Background/Aim: The association between heavy metal exposure and adverse birth outcomes is well-established. However, there is a paucity of research identifying biomarker profiles that may improve the early detection of heavy metal-induced adverse birth outcomes. Because lipids are abundant in our body and associated with important signaling pathways, we assessed associations between maternal metals/metalloid blood levels with lipidomic profiles among 83 pregnant women in the Puerto Rico PROTECT birth cohort.

Methods: We measured 10 metals/metalloid blood levels during 24–28 weeks of pregnancy. Prenatal plasma lipidomic profiles were identified by liquid chromatography–mass spectrometry-based shotgun lipidomics. We derived sums for each lipid class and sums for each lipid sub-class (saturated, monounsaturated, polyunsaturated), which were then regressed on metals/metalloid. False discovery rate (FDR) adjusted p-values (q-values) were used to account for multiple comparisons.

Results: A total of 587 unique lipids from 19 lipid classes were profiled. When controlling for multiple comparisons, we observed that maternal exposure to manganese and zinc were negatively associated with plasmenyl-phosphatidylethanolamine (PLPE), particularly those containing polyunsaturated fatty acid (PUFA) chains. In contrast to manganese and zinc, arsenic and mercury were positively associated with PLPE and plasmenyl-phosphatidylcholine (PLPC).

Conclusion: Certain metals were significantly associated with lipids that are responsible for the biophysical properties of the cell membrane and antioxidant defense in lipid peroxidation. This study highlighted lipid-metal associations and we anticipate that this study will open up new avenues for developing diagnostic tools.

Preterm birth buccal cell epigenetic biomarkers to facilitate preventative medicine

Preterm birth is the major cause of newborn and infant mortality affecting nearly one in every ten live births. The current study was designed to develop an epigenetic biomarker for susceptibility of preterm birth using buccal cells from the mother, father, and child (triads). An epigenome-wide association study (EWAS) was used to identify differential DNA methylation regions (DMRs) using a comparison of control term birth versus preterm birth triads. Epigenetic DMR associations with preterm birth were identified for both the mother and father that were distinct and suggest potential epigenetic contributions from both parents. The mother (165 DMRs) and female child (136 DMRs) at p < 1e-04 had the highest number of DMRs and were highly similar suggesting potential epigenetic inheritance of the epimutations. The male child had negligible DMR associations. The DMR associated genes for each group involve previously identified preterm birth associated genes. Observations identify a potential paternal germline contribution for preterm birth and identify the potential epigenetic inheritance of preterm birth susceptibility for the female child later in life. Although expanded clinical trials and preconception trials are required to optimize the potential epigenetic biomarkers, such epigenetic biomarkers may allow preventative medicine strategies to reduce the incidence of preterm birth.

Maternal plasma lipids are involved in the pathogenesis of preterm birth

BACKGROUND

Preterm birth is defined by the onset of labor at a gestational age shorter than 37 weeks, and it can lead to premature birth and impose a threat to newborns' health. The Puerto Rico PROTECT cohort is a well-characterized prospective birth cohort that was designed to investigate environmental and social contributors to preterm birth in Puerto Rico, where preterm birth rates have been elevated in recent decades. To elucidate possible relationships between metabolites and preterm birth in this cohort, we conducted a nested case-control study to conduct untargeted metabolomic characterization of maternal plasma of 31 women who experienced preterm birth and 69 controls who underwent full-term labor at 24-28 gestational weeks.

RESULTS

A total of 333 metabolites were identified and annotated with liquid chromatography/mass spectrometry. Subsequent weighted gene correlation network analysis shows that the fatty acid and carene-enriched module has a significant positive association (P = 8e-04, FDR = 0.006) with preterm birth. After controlling for potential clinical confounders, a total of 38 metabolites demonstrated significant changes uniquely associated with preterm birth, where 17 of them were preterm biomarkers. Among 7 machine-learning classifiers, the application of random forest achieved a highly accurate and specific prediction (AUC = 0.92) for preterm birth in testing data, demonstrating their strong potential as biomarkers for preterm births. The 17 preterm biomarkers are involved in cell signaling, lipid metabolism, and lipid peroxidation functions. Additional modeling using only the 19 spontaneous preterm births (sPTB) and controls identifies 16 sPTB markers, with an AUC of 0.89 in testing data. Half of the sPTB overlap with those markers for preterm births. Further causality analysis infers that suberic acid upregulates several fatty acids to promote preterm birth.

CONCLUSIONS

Altogether, this study demonstrates the involvement of lipids, particularly fatty acids, in the pathogenesis of preterm birth.