A multi-omic analysis of birthweight in newborn cord blood reveals new underlying mechanisms related to cholesterol metabolism

Progress in toxicogenomics to protect human health

Toxicogenomics measures molecular features, such as transcripts, proteins, metabolites and epigenomic modifications, to understand and predict the toxicological effects of environmental and pharmaceutical exposures. Transcriptomics has become an integral tool in contemporary toxicology research owing to innovations in gene expression profiling that can provide mechanistic and quantitative information at scale. These data can be used to predict toxicological hazards through the use of transcriptomic biomarkers, network inference analyses, pattern-matching approaches and artificial intelligence. Furthermore, emerging approaches, such as high-throughput dose-response modelling, can leverage toxicogenomic data for human health protection even in the absence of predicting specific hazards. Finally, single-cell transcriptomics and multi-omics provide detailed insights into toxicological mechanisms. Here, we review the progress since the inception of toxicogenomics in applying transcriptomics towards toxicology testing and highlight advances that are transforming risk assessment.

Association of cord plasma metabolites with birth weight: results from metabolomic and lipidomic studies of discovery and validation cohorts

OBJECTIVE

Birth weight is a good predictor of fetal intrauterine growth and long-term health, and several studies have evaluated the relationship between metabolites and birth weight. The aim of this study was to investigate the association of cord blood metabolomics and lipidomics with birth weight, using a two-stage discovery and validation approach.

METHODS

Firstly, a pseudotargeted metabolomics approach was applied to detect metabolites in 504 cord blood samples in the discovery set enrolled from the Wuhan Healthy Baby Cohort, China. Metabolome-wide association scan analysis and pathway enrichment were applied to identify metabolites and metabolic pathways that were significantly associated with birth weight adjusted for gestational age Z-score (BW Z-score). Logistic regression models were used to analyze the association of metabolites in the most significantly associated pathways with small-for-gestational age (SGA) at delivery and low birth weight (LBW). Subsequently, 350 cord blood samples in a validation cohort were subjected to targeted analysis to validate the metabolites identified by screening in the discovery cohort.

RESULTS

In the discovery set, of 2566 metabolites detected, 2418 metabolites were retained for subsequent analysis after data preprocessing. Of these, 513 metabolites were significantly associated with BW Z-score (P-value adjusted for false discovery rate (PFDR) < 0.05), of which 298 Kyoto Encyclopedia of Genes and Genomes (KEGG)-annotated metabolites were included in the pathway analysis. The primary bile acid biosynthesis pathway was the most relevant metabolic pathway associated with BW Z-score. Elevated cord plasma primary bile acids were associated with lower BW Z-score and higher risk of SGA or LBW in the discovery and validation cohorts. In the validation set, a 2-fold increase in taurochenodeoxycholic acid (TCDCA) and in taurocholic acid (TCA) was associated with a decrease in BW Z-score (estimated β coefficient, -0.10 (95% CI, -0.20 to 0.00) and -0.18 (95% CI, -0.31 to -0.04), respectively), after adjusting for covariates. In addition, a 2-fold increase in cord plasma TCDCA and of cord plasma TCA was associated with an increased risk of SGA (adjusted odds ratio (OR), 1.52 (95% CI, 1.00-2.30) and 1.77 (95% CI, 1.05-2.98), respectively). The adjusted OR for LBW, for a 2-fold increase in TCDCA and TCA concentration, were 2.39 (95% CI, 1.00-5.71) and 3.21 (95% CI, 0.96-10.74), respectively.

CONCLUSIONS

These results indicate a significant association of elevated primary bile acids, particularly TCDCA and TCA, in cord blood with lower BW Z-score, as well as increased risk of SGA and LBW. Abnormalities of primary bile acid metabolism may play an important role in restricted fetal development. © 2024 International Society of Ultrasound in Obstetrics and Gynecology.

Optimal transport for automatic alignment of untargeted metabolomic data

Untargeted metabolomic profiling through liquid chromatography-mass spectrometry (LC-MS) measures a vast array of metabolites within biospecimens, advancing drug development, disease diagnosis, and risk prediction. However, the low throughput of LC-MS poses a major challenge for biomarker discovery, annotation, and experimental comparison, necessitating the merging of multiple datasets. Current data pooling methods encounter practical limitations due to their vulnerability to data variations and hyperparameter dependence. Here, we introduce GromovMatcher, a flexible and user-friendly algorithm that automatically combines LC-MS datasets using optimal transport. By capitalizing on feature intensity correlation structures, GromovMatcher delivers superior alignment accuracy and robustness compared to existing approaches. This algorithm scales to thousands of features requiring minimal hyperparameter tuning. Manually curated datasets for validating alignment algorithms are limited in the field of untargeted metabolomics, and hence we develop a dataset split procedure to generate pairs of validation datasets to test the alignments produced by GromovMatcher and other methods. Applying our method to experimental patient studies of liver and pancreatic cancer, we discover shared metabolic features related to patient alcohol intake, demonstrating how GromovMatcher facilitates the search for biomarkers associated with lifestyle risk factors linked to several cancer types.

Epigenetic Responses to Nonchemical Stressors: Potential Molecular Links to Perinatal Health Outcomes

PURPOSE OF REVIEW

We summarize the recent literature investigating exposure to four nonchemical stressors (financial stress, racism, psychosocial stress, and trauma) and DNA methylation, miRNA expression, and mRNA expression. We also highlight the relationships between these epigenetic changes and six critical perinatal outcomes (preterm birth, low birth weight, preeclampsia, gestational diabetes, childhood allergic disease, and childhood neurocognition).

RECENT FINDINGS

Multiple studies have found financial stress, psychosocial stress, and trauma to be associated with DNA methylation and/or miRNA and mRNA expression. Fewer studies have investigated the effects of racism. The majority of studies assessed epigenetic or genomic changes in maternal blood, cord blood, or placenta. Several studies included multi-OMIC assessments in which DNA methylation and/or miRNA expression were associated with gene expression. There is strong evidence for the role of epigenetics in driving the health outcomes considered. A total of 22 biomarkers, including numerous HPA axis genes, were identified to be epigenetically altered by both stressors and outcomes. Epigenetic changes related to inflammation, the immune and endocrine systems, and cell growth and survival were highlighted across numerous studies. Maternal exposure to nonchemical stressors is associated with epigenetic and/or genomic changes in a tissue-specific manner among inflammatory, immune, endocrine, and cell growth-related pathways, which may act as mediating pathways to perinatal health outcomes. Future research can test the mediating role of the specific biomarkers identified as linked with both stressors and outcomes. Understanding underlying epigenetic mechanisms altered by nonchemical stressors can provide a better understanding of how chemical and nonchemical exposures interact.

Associations of birth weight, plasma metabolome in adulthood and risk of type 2 diabetes

AIMS

We aimed to examine the longitudinal associations of birth weight with plasma metabolites in adulthood, and further quantify the proportions of the links between birth weight and incident adult type 2 diabetes (T2D) that were mediated by plasma metabolites.

MATERIALS AND METHODS

A total of 62,033 participants with complete nuclear magnetic resonance metabolomics and birth weight data from the UK Biobank were included in this study. Linear regression was used to assess the associations between birth weight and metabolites. Cox regression was used to estimate hazard ratios for T2D associated with metabolites. We further performed mediation analyses to estimate the extent to which metabolites might mediate the association between birth weight and T2D risk.

RESULTS

Low birth weight was associated with the adverse metabolic responses across multiple metabolic pathways, including lipoprotein subclasses, amino acids, fatty acids (FA), and inflammation. Metabolites associated with higher birth weight tended to be associated with a lower risk of T2D (Pearson correlation coefficient: -0.85). A total of 62 metabolites showed statistically significant mediation effects in the protective association of higher birth weight and T2D risk, including large-sized very low-density lipoprotein particles and triglyceride concentrations as well as saturated, and monounsaturated FA and glycoprotein acetyls.

CONCLUSIONS

We identified a range of metabolites that reflect the adult metabolic response to birth weight, some of which might lie on the pathway between birth weight and adult T2D risk.

Methylation of serotonin regulating genes in cord blood cells: association with maternal metabolic parameters and correlation with methylation in peripheral blood cells during childhood and adolescence

BACKGROUND

Serotonin (5-hydroxytryptamine, 5-HT) signaling is involved in neurodevelopment, mood regulation, energy metabolism, and other physiological processes. DNA methylation plays a significant role in modulating the expression of genes responsible for maintaining 5-HT balance, such as 5-HT transporter (SLC6A4), monoamine oxidase A (MAOA), and 5-HT receptor type 2A (HTR2A). Maternal metabolic health can influence long-term outcomes in offspring, with DNA methylation mediating these effects. We investigated associations between maternal metabolic parameters-pre-pregnancy body mass index (pBMI), gestational weight gain (GWG), and glucose tolerance status (GTS), i.e., gestational diabetes mellitus (GDM) versus normal glucose tolerance (NGT)-and cord blood methylation of SLC6A4, MAOA, and HTR2A in participants from our PlaNS birth cohort. CpG sites (15, 9, and 2 in each gene, respectively) were selected based on literature and in silico data. Methylation levels were quantified by bisulfite pyrosequencing. We also examined the stability of methylation patterns in these genes in circulating blood cells from birth to adolescence using longitudinal DNA methylation data from the ARIES database.

RESULTS

None of the 203 PlaNS mothers included in this study had preexisting diabetes, 99 were diagnosed with GDM, and 104 had NGT; all neonates were born at full term by planned Cesarean section. Methylation at most CpG sites differed between male and female newborns. SLC6A4 methylation correlated inversely with maternal pBMI and GWG, while methylation at HTR2A site -1665 correlated positively with GWG. None of the maternal metabolic parameters statistically associated with MAOA methylation. DNA methylation data in cord blood and peripheral blood at ages 7 and 15 years were available for 808 participants from the ARIES database; 4 CpG sites (2 in SLC6A4 and 2 in HTR2A) overlapped between the PlaNS and ARIES cohorts. A positive correlation between methylation levels in cord blood and peripheral blood at 7 and 15 years of age was observed for both SLC6A4 and HTR2A CpG sites.

CONCLUSIONS

Methylation of 5-HT regulating genes in cord blood cells is influenced by neonatal sex, with maternal metabolism playing an additional role. Inter-individual variations present in circulating blood cells at birth are still pronounced in childhood and adolescence.

Targeting cholesterol metabolism in Cancer: From molecular mechanisms to therapeutic implications

Cholesterol is an essential component of cell membranes and helps to maintain their structure and function. Abnormal cholesterol metabolism has been linked to the development and progression of tumors. Changes in cholesterol metabolism triggered by internal or external stimuli can promote tumor growth. During metastasis, tumor cells require large amounts of cholesterol to support their growth and colonization of new organs. Recent research has shown that cholesterol metabolism is reprogrammed during tumor development, and this can also affect the anti-tumor activity of immune cells in the surrounding environment. However, identifying the specific targets in cholesterol metabolism that regulate cancer progression and the tumor microenvironment is still a challenge. Additionally, exploring the potential of combining statin drugs with other therapies for different types of cancer could be a worthwhile avenue for future drug development. In this review, we focus on the molecular mechanisms of cholesterol and its derivatives in cell metabolism and the tumor microenvironment, and discuss specific targets and relevant therapeutic agents that inhibit aspects of cholesterol homeostasis.

On the use of cross-validation for the calibration of the adaptive lasso

Cross-validation is the standard method for hyperparameter tuning, or calibration, of machine learning algorithms. The adaptive lasso is a popular class of penalized approaches based on weighted L1 -norm penalties, with weights derived from an initial estimate of the model parameter. Although it violates the paramount principle of cross-validation, according to which no information from the hold-out test set should be used when constructing the model on the training set, a "naive" cross-validation scheme is often implemented for the calibration of the adaptive lasso. The unsuitability of this naive cross-validation scheme in this context has not been well documented in the literature. In this work, we recall why the naive scheme is theoretically unsuitable and how proper cross-validation should be implemented in this particular context. Using both synthetic and real-world examples and considering several versions of the adaptive lasso, we illustrate the flaws of the naive scheme in practice. In particular, we show that it can lead to the selection of adaptive lasso estimates that perform substantially worse than those selected via a proper scheme in terms of both support recovery and prediction error. In other words, our results show that the theoretical unsuitability of the naive scheme translates into suboptimality in practice, and call for abandoning it.

The contribution to policies of an exposome-based approach to childhood obesity

Childhood obesity is an increasingly severe public health problem, with a prospective impact on health. We propose an exposome approach to identify actionable risk factors for this condition. Our assumption is that relationships between external exposures and outcomes such as rapid growth, overweight, or obesity in children can be better understood through a "meet-in-the-middle" model. This is based on a combination of external and internal exposome-based approaches, that is, the study of multiple exposures (in our case, dietary patterns) and molecular pathways (metabolomics and epigenetics). This may strengthen causal reasoning by identifying intermediate markers that are associated with both exposures and outcomes. Our biomarker-based studies in the STOP consortium suggest (in several ways, including mediation analysis) that branched-chain amino acids (BCAAs) could be mediators of the effect of dietary risk factors on childhood overweight/obesity. This is consistent with intervention and animal studies showing that higher intake of BCAAs has a positive impact on body composition, glycemia, and satiety. Concerning food, of particular concern is the trend of increasing intake of ultra-processed food (UPF), including among children. Several mechanisms have been proposed to explain the impact of UPF on obesity and overweight, including nutrient intake (particularly proteins), changes in appetite, or the role of additives. Research from the Avon Longitudinal Study of Parents and Children cohort has shown a relationship between UPF intake and trajectories in childhood adiposity, while UPF was related to lower blood levels of BCAAs. We suggest that an exposome-based approach can help strengthening causal reasoning and support policies. Intake of UPF in children should be restricted to prevent obesity.

Prenatal Exposure to Metabolism-Disrupting Chemicals, Cord Blood Transcriptome Perturbations, and Birth Weight in a Belgian Birth Cohort

Prenatal exposure to metabolism-disrupting chemicals (MDCs) has been linked to birth weight, but the molecular mechanisms remain largely unknown. In this study, we investigated gene expressions and biological pathways underlying the associations between MDCs and birth weight, using microarray transcriptomics, in a Belgian birth cohort. Whole cord blood measurements of dichlorodiphenyldichloroethylene (p,p'-DDE), polychlorinated biphenyls 153 (PCB-153), perfluorooctanoic acid (PFOA), perfluorooctane sulfonic acid (PFOS), and transcriptome profiling were conducted in 192 mother-child pairs. A workflow including a transcriptome-wide association study, pathway enrichment analysis with a meet-in-the-middle approach, and mediation analysis was performed to characterize the biological pathways and intermediate gene expressions of the MDC-birth weight relationship. Among 26,170 transcriptomic features, we successfully annotated five overlapping metabolism-related gene expressions associated with both an MDC and birth weight, comprising BCAT2, IVD, SLC25a16, HAS3, and MBOAT2. We found 11 overlapping pathways, and they are mostly related to genetic information processing. We found no evidence of any significant mediating effect. In conclusion, this exploratory study provides insights into transcriptome perturbations that may be involved in MDC-induced altered birth weight.

Cord blood epigenome-wide meta-analysis in six European-based child cohorts identifies signatures linked to rapid weight growth

BACKGROUND

Rapid postnatal growth may result from exposure in utero or early life to adverse conditions and has been associated with diseases later in life and, in particular, with childhood obesity. DNA methylation, interfacing early-life exposures and subsequent diseases, is a possible mechanism underlying early-life programming.

METHODS

Here, a meta-analysis of Illumina HumanMethylation 450K/EPIC-array associations of cord blood DNA methylation at single CpG sites and CpG genomic regions with rapid weight growth at 1 year of age (defined with reference to WHO growth charts) was conducted in six European-based child cohorts (ALSPAC, ENVIRONAGE, Generation XXI, INMA, Piccolipiù, and RHEA, N = 2003). The association of gestational age acceleration (calculated using the Bohlin epigenetic clock) with rapid weight growth was also explored via meta-analysis. Follow-up analyses of identified DNA methylation signals included prediction of rapid weight growth, mediation of the effect of conventional risk factors on rapid weight growth, integration with transcriptomics and metabolomics, association with overweight in childhood (between 4 and 8 years), and comparison with previous findings.

RESULTS

Forty-seven CpGs were associated with rapid weight growth at suggestive p-value <1e-05 and, among them, three CpGs (cg14459032, cg25953130 annotated to ARID5B, and cg00049440 annotated to KLF9) passed the genome-wide significance level (p-value <1.25e-07). Sixteen differentially methylated regions (DMRs) were identified as associated with rapid weight growth at false discovery rate (FDR)-adjusted/Siddak p-values < 0.01. Gestational age acceleration was associated with decreasing risk of rapid weight growth (p-value = 9.75e-04). Identified DNA methylation signals slightly increased the prediction of rapid weight growth in addition to conventional risk factors. Among the identified signals, three CpGs partially mediated the effect of gestational age on rapid weight growth. Both CpGs (N=3) and DMRs (N=3) were associated with differential expression of transcripts (N=10 and 7, respectively), including long non-coding RNAs. An AURKC DMR was associated with childhood overweight. We observed enrichment of CpGs previously reported associated with birthweight.

CONCLUSIONS

Our findings provide evidence of the association between cord blood DNA methylation and rapid weight growth and suggest links with prenatal exposures and association with childhood obesity providing opportunities for early prevention.

Epigenome-wide analysis of maternal exposure to green space during gestation and cord blood DNA methylation in the ENVIRONAGE cohort

BACKGROUND

DNA methylation programming is sensitive to prenatal life environmental influences, but the impact of maternal exposure to green space on newborns DNA methylation has not been studied yet.

METHODS

We conducted a meta-epigenome-wide association study (EWAS) of maternal exposure to green space during gestation with cord blood DNA methylation in two subsets of the ENVIRONAGE cohort (N = 538). Cord blood DNA methylation was measured by Illumina HumanMethylation 450K in one subset (N = 189) and EPICarray in another (N = 349). High (vegetation height>3 m (m)), low (vegetation height<3 m) and total (including both) high-resolution green space exposures during pregnancy were estimated within 100 m and 1000 m distance around maternal residence. In each subset, we sought cytosine-phosphate-guanine (CpG) sites via linear mixed models adjusted on newborns' sex, ethnicity, gestational age, season at delivery, sampling day, maternal parity, age, smoking, education, and estimated blood cell proportions. EWASs results were meta-analysed via fixed-effects meta-analyses. Differentially methylated regions (DMRs) were identified via ENmix-combp and DMRcate algorithms. Sensitivity analyses were additionally adjusted on PM_2.5, distance to major roads, urbanicity and neighborhood income. In the 450K subset, cord blood expression of differentially methylated genes was measured by Agilent microarrays and associated with green space.

RESULTS

147 DMRs were identified, 85 of which were still significant upon adjustment for PM_2.5, distance to major roads, urbanicity and neighborhood income, including HLA-DRB5, RPTOR, KCNQ1DN, A1BG-AS1, HTR2A, ZNF274, COL11A1 and PRSS36 DMRs. One CpG reached genome-wide significance, while 54 CpGs were suggestive significant (p-values<1e-05). Among them, a CpG, hypermethylated with 100 m buffer total green space, was annotated to PAQR9, whose expression decreased with 1000 m buffer low green space (p-value = 1.45e-05).

CONCLUSIONS

Our results demonstrate that maternal exposure to green space during pregnancy is associated with cord blood DNA methylation, mainly at loci organized in regions, in genes playing important roles in neurological development (e.g., HTR2A).

IDSL.UFA Assigns High-Confidence Molecular Formula Annotations for Untargeted LC/HRMS Data Sets in Metabolomics and Exposomics

Untargeted liquid chromatography/high-resolution mass spectrometry (LC/HRMS) assays in metabolomics and exposomics aim to characterize the small molecule chemical space in a biospecimen. To gain maximum biological insights from these data sets, LC/HRMS peaks should be annotated with chemical and functional information including molecular formula, structure, chemical class, and metabolic pathways. Among these, molecular formulas may be assigned to LC/HRMS peaks through matching theoretical and observed isotopic profiles (MS1) of the underlying ionized compound. For this, we have developed the Integrated Data Science Laboratory for Metabolomics and Exposomics-United Formula Annotation (IDSL.UFA) R package. In the untargeted metabolomics validation tests, IDSL.UFA assigned 54.31-85.51% molecular formula for true positive annotations as the top hit and 90.58-100% within the top five hits. Molecular formula annotations were also supported by tandem mass spectrometry data. We have implemented new strategies to (1) generate formula sources and their theoretical isotopic profiles, (2) optimize the formula hits ranking for the individual and aligned peak lists, and (3) scale IDSL.UFA-based workflows for studies with larger sample sizes. Annotating the raw data for a publicly available pregnancy metabolome study using IDSL.UFA highlighted hundreds of new pregnancy-related compounds and also suggested the presence of chlorinated perfluorotriether alcohols (Cl-PFTrEAs) in human specimens. IDSL.UFA is useful for human metabolomics and exposomics studies where we need to minimize the loss of biological insights in untargeted LC/HRMS data sets. The IDSL.UFA package is available in the R CRAN repository https://cran.r-project.org/package=IDSL.UFA. Detailed documentation and tutorials are also provided at www.ufa.idsl.me.

Cord blood metabolites and rapid postnatal growth as multiple mediators in the prenatal propensity to childhood overweight

BACKGROUND

The mechanisms underlying childhood overweight and obesity are poorly known. Here, we investigated the direct and indirect effects of different prenatal exposures on offspring rapid postnatal growth and overweight in childhood, mediated through cord blood metabolites. Additionally, rapid postnatal growth was considered a potential mediator on childhood overweight, alone and sequentially to each metabolite.

METHODS

Within four European birth-cohorts (N = 375 mother-child dyads), information on seven prenatal exposures (maternal education, pre-pregnancy BMI, weight gain and tobacco smoke during pregnancy, age at delivery, parity, and child gestational age), selected as obesogenic according to a-priori knowledge, was collected. Cord blood levels of 31 metabolites, associated with rapid postnatal growth and/or childhood overweight in a previous study, were measured via liquid-chromatography-quadrupole-time-of-flight-mass-spectrometry. Rapid growth at 12 months and childhood overweight (including obesity) between four and eight years were defined with reference to WHO growth charts. Single mediation analysis was performed using the imputation approach and multiple mediation analysis using the extended-imputation approach.

RESULTS

Single mediation suggested that the effect of maternal education, pregnancy weight gain, parity, and gestational age on rapid postnatal growth but not on childhood overweight was partly mediated by seven metabolites, including cholestenone, decenoylcarnitine(C10:1), phosphatidylcholine(C34:3), progesterone and three unidentified metabolites; and the effect of gestational age on childhood overweight was mainly mediated by rapid postnatal growth. Multiple mediation suggested that the effect of gestational age on childhood overweight was mainly mediated by rapid postnatal growth and that the mediating role of the metabolites was marginal.

CONCLUSION

Our findings provide evidence of the involvement of in utero metabolism in the propensity to rapid postnatal growth and of rapid postnatal growth in the propensity to childhood overweight. We did not find evidence supporting a mediating role of the studied metabolites alone between the studied prenatal exposures and the propensity to childhood overweight.

IDSL.IPA Characterizes the Organic Chemical Space in Untargeted LC/HRMS Data Sets

Generating comprehensive and high-fidelity metabolomics data matrices from LC/HRMS data remains to be extremely challenging for population-scale large studies (n > 200). Here, we present a new data processing pipeline, the Intrinsic Peak Analysis (IDSL.IPA) R package (https://ipa.idsl.me), to generate such data matrices specifically for organic compounds. The IDSL.IPA pipeline incorporates (1) identifying potential ¹²C and ¹³C ion pairs in individual mass spectra; (2) detecting and characterizing chromatographic peaks using a new sensitive and versatile approach to perform mass correction, peak smoothing, baseline development for local noise measurement, and peak quality determination; (3) correcting retention time and cross-referencing peaks from multiple samples by a dynamic retention index marker approach; (4) annotating peaks using a reference database of m/z and retention time; and (5) accelerating data processing using a parallel computation of the peak detection and alignment steps for larger studies. This pipeline has been successfully evaluated for studies ranging from 200 to 1600 samples. By specifically isolating high quality and reliable signals pertaining to carbon-containing compounds in untargeted LC/HRMS data sets from larger studies, IDSL.IPA opens new opportunities for discovering new biological insights in the population-scale metabolomics and exposomics projects. The package is available in the R CRAN repository at https://cran.r-project.org/package=IDSL.IPA.

Strengthening Causal Inference in Exposomics Research: Application of Genetic Data and Methods

Advances in technologies to measure a broad set of exposures have led to a range of exposome research efforts. Yet, these efforts have insufficiently integrated methods that incorporate genetic data to strengthen causal inference, despite evidence that many exposome-associated phenotypes are heritable. Objective: We demonstrate how integration of methods and study designs that incorporate genetic data can strengthen causal inference in exposomics research by helping address six challenges: reverse causation and unmeasured confounding, comprehensive examination of phenotypic effects, low efficiency, replication, multilevel data integration, and characterization of tissue-specific effects. Examples are drawn from studies of biomarkers and health behaviors, exposure domains where the causal inference methods we describe are most often applied. Discussion: Technological, computational, and statistical advances in genotyping, imputation, and analysis, combined with broad data sharing and cross-study collaborations, offer multiple opportunities to strengthen causal inference in exposomics research. Full application of these opportunities will require an expanded understanding of genetic variants that predict exposome phenotypes as well as an appreciation that the utility of genetic variants for causal inference will vary by exposure and may depend on large sample sizes. However, several of these challenges can be addressed through international scientific collaborations that prioritize data sharing. Ultimately, we anticipate that efforts to better integrate methods that incorporate genetic data will extend the reach of exposomics research by helping address the challenges of comprehensively measuring the exposome and its health effects across studies, the life course, and in varied contexts and diverse populations. https://doi.org/10.1289/EHP9098.

Associations of maternal and infant metabolite profiles with foetal growth and the odds of adverse birth outcomes

BACKGROUND

Adaptations in maternal and foetal metabolic pathways may predispose to altered foetal growth and adverse birth outcomes.

OBJECTIVE

To assess the associations of maternal early-pregnancy metabolite profiles and infant metabolite profiles at birth with foetal growth from first trimester onwards and the odds of adverse birth outcomes.

METHODS

In a prospective population-based cohort among 976 Dutch pregnant women and their children, serum concentrations of amino acids, non-esterified fatty acids (NEFA), phospholipids (PL) and carnitines in maternal early-pregnancy blood and in cord blood were obtained by liquid-chromatography tandem mass spectrometry. Information on foetal growth was available from first trimester onwards.

RESULTS

After false discovery rate correction for multiple testing, higher infant total and individual NEFA concentrations were associated with a lower weight, length, and head circumference at birth. Higher infant total and individual acyl-lysophosphatidylcholine (lyso.PC.a) and alkyl-lysophosphatidylcholine concentrations were associated with higher weight and head circumference (lyso.PC.a only) at birth, higher odds of LGA and lower odds of SGA. Few individual maternal metabolites were associated with foetal growth measures in third trimester and at birth, but not with the odds of adverse birth outcomes.

CONCLUSIONS

Our results suggest that infant metabolite profiles, particularly total and individual lyso.PC.a and NEFA concentrations, were strongly related to growth measures at birth and the odds of adverse birth outcomes. Few individual maternal early-pregnancy metabolites, but not total metabolite concentrations, are associated with foetal growth measures in third trimester and at birth.

A systematic review of metabolomic studies of childhood obesity: State of the evidence for metabolic determinants and consequences

Childhood obesity has become a global epidemic and carries significant long-term consequences to physical and mental health. Metabolomics, the global profiling of small molecules or metabolites, may reveal the mechanisms of development of childhood obesity and clarify links between obesity and metabolic disease. A systematic review of metabolomic studies of childhood obesity was conducted, following Preferred Reporting Items for Systematic Reviews (PRISMA) guidelines, searching across Scopus, Ovid, Web of Science and PubMed databases for articles published from January 1, 2005 to July 8, 2020, retrieving 1271 different records and retaining 41 articles for qualitative synthesis. Study quality was assessed using a modified Newcastle-Ottawa Scale. Thirty-three studies were conducted on blood, six on urine, three on umbilical cord blood, and one on saliva. Thirty studies were primarily cross-sectional, five studies were primarily longitudinal, and seven studies examined effects of weight-loss following a life-style intervention. A consistent metabolic profile of childhood obesity was observed including amino acids (particularly branched chain and aromatic), carnitines, lipids, and steroids. Although the use of metabolomics in childhood obesity research is still developing, the identified metabolites have provided additional insight into the pathogenesis of many obesity-related diseases. Further longitudinal research is needed into the role of metabolic profiles and child obesity risk.

Perspectives and challenges of epigenetic determinants of childhood obesity: A systematic review

The tremendous increase in childhood obesity prevalence over the last few decades cannot merely be explained by genetics and evolutionary changes in the genome, implying that gene-environment interactions, such as epigenetic modifications, likely play a major role. This systematic review aims to summarize the evidence of the association between epigenetics and childhood obesity. A literature search was performed via PubMed and Scopus engines using a combination of terms related to epigenetics and pediatric obesity. Articles studying the association between epigenetic mechanisms (including DNA methylation and hydroxymethylation, non-coding RNAs, and chromatin and histones modification) and obesity and/or overweight (or any related anthropometric parameters) in children (0-18 years) were included. The risk of bias was assessed with a modified Newcastle-Ottawa scale for non-randomized studies. One hundred twenty-one studies explored epigenetic changes related to childhood obesity. DNA methylation was the most widely investigated mechanism (N = 101 studies), followed by non-coding RNAs (N = 19 studies) with evidence suggestive of an association with childhood obesity for DNA methylation of specific genes and microRNAs (miRNAs). One study, focusing on histones modification, was identified. Heterogeneity of findings may have hindered more insights into the epigenetic changes related to childhood obesity. Gaps and challenges that future research should face are herein described.

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.

Cord blood metabolic signatures predictive of childhood overweight and rapid growth

INTRODUCTION

Metabolomics may identify biological pathways predisposing children to the risk of overweight and obesity. In this study, we have investigated the cord blood metabolic signatures of rapid growth in infancy and overweight in early childhood in four European birth cohorts.

METHODS

Untargeted liquid chromatography-mass spectrometry metabolomic profiles were measured in cord blood from 399 newborns from four European cohorts (ENVIRONAGE, Rhea, INMA and Piccolipiu). Rapid growth in the first year of life and overweight in childhood was defined with reference to WHO growth charts. Metabolome-wide association scans for rapid growth and overweight on over 4500 metabolic features were performed using multiple adjusted logistic mixed-effect models and controlling the false discovery rate (FDR) at 5%. In addition, we performed a look-up analysis of 43 pre-annotated metabolites, previously associated with birthweight or rapid growth.

RESULTS

In the Metabolome-Wide Association Study analysis, we identified three and eight metabolites associated with rapid growth and overweight, respectively, after FDR correction. Higher levels of cholestenone, a cholesterol derivative produced by microbial catabolism, were predictive of rapid growth (p = 1.6 × 10-3). Lower levels of the branched-chain amino acid (BCAA) valine (p = 8.6 × 10-6) were predictive of overweight in childhood. The area under the receiver operator curve for multivariate prediction models including these metabolites and traditional risk factors was 0.77 for rapid growth and 0.82 for overweight, compared with 0.69 and 0.69, respectively, for models using traditional risk factors alone. Among the 43 pre-annotated metabolites, seven and five metabolites were nominally associated (P < 0.05) with rapid growth and overweight, respectively. The BCAA leucine, remained associated (1.6 × 10-3) with overweight after FDR correction.

CONCLUSION

The metabolites identified here may assist in the identification of children at risk of developing obesity and improve understanding of mechanisms involved in postnatal growth. Cholestenone and BCAAs are suggestive of a role of the gut microbiome and nutrient signalling respectively in child growth trajectories.

Multi 'omic data integration: A review of concepts, considerations, and approaches

The application of 'omic techniques including, but not limited to genomics/metagenomics, transcriptomics/meta-transcriptomics, proteomics/meta-proteomics, and metabolomics to generate multiple datasets from a single sample have facilitated hypothesis generation leading to the identification of biological, molecular and ecological functions and mechanisms, as well as associations and correlations. Despite their power and promise, a variety of challenges must be considered in the successful design and execution of a multi-omics study. In this review, various 'omic technologies applicable to single- and meta-organisms (i.e., host + microbiome) are described, and considerations for sample collection, storage and processing prior to data generation and analysis, as well as approaches to data storage, dissemination and analysis are discussed. Finally, case studies are included as examples of multi-omic applications providing novel insights and a more holistic understanding of biological processes.

Associations of cord metabolome and biochemical parameters with the neonatal deaths of cloned pigs

Neonatal cloned pigs generated via somatic cell nuclear transfer (SCNT) have high incidences of malformation and mortality. The mechanisms underlying the massive loss of cloned pig neonates remain unclear. We compared the cord serum metabolic profiles and biochemical indexes of SCNT-derived piglets that died within 4 days (SCNT-DW4), SCNT-derived piglets that survived over 4 days (SCNT-SO4) and artificial insemination (AI)-generated piglets that survived over 4 days (AI-SO4) to investigate the associations of serum metabolomics and biochemical indexes in umbilical cord (UC) sera at delivery with the neonatal loss of cloned pigs. Results showed that compared with SCNT-SO4 and AI-SO4 piglets, SCNT-DW4 piglets had lower birth weight, placental indexes, placental vascularization scores, UC scores, vitality scores, serum glucose and levels but higher creatinine, urea nitrogen and uric acid levels in cord sera. Metabolomics analysis revealed alterations in lipid, glucose and purine metabolism in the cord sera of SCNT-DW4 piglets. These results indicated that the disturbance of the cord serum metabolome might be associated with the low birth weight and malformations of cloned neonates. These effects were likely the consequences of the impaired placental morphology and function of SCNT-derived piglets. This study provides helpful information regarding the potential mechanisms responsible for the neonatal death of cloned pigs and also offers an important basis for the design of effective strategies to improve the survival rate of these animals.

Data Processing Thresholds for Abundance and Sparsity and Missed Biological Insights in an Untargeted Chemical Analysis of Blood Specimens for Exposomics

Background: An untargeted chemical analysis of bio-fluids provides semi-quantitative data for thousands of chemicals for expanding our understanding about relationships among metabolic pathways, diseases, phenotypes and exposures. During the processing of mass spectral and chromatography data, various signal thresholds are used to control the number of peaks in the final data matrix that is used for statistical analyses. However, commonly used stringent thresholds generate constrained data matrices which may under-represent the detected chemical space, leading to missed biological insights in the exposome research. Methods: We have re-analyzed a liquid chromatography high resolution mass spectrometry data set for a publicly available epidemiology study (n = 499) of human cord blood samples using the MS-DIAL software with minimally possible thresholds during the data processing steps. Peak list for individual files and the data matrix after alignment and gap-filling steps were summarized for different peak height and detection frequency thresholds. Correlations between birth weight and LC/MS peaks in the newly generated data matrix were computed using the spearman correlation coefficient. Results: MS-DIAL software detected on average 23,156 peaks for individual LC/MS file and 63,393 peaks in the aligned peak table. A combination of peak height and detection frequency thresholds that was used in the original publication at the individual file and the peak alignment levels can reject 90% peaks from the untargeted chemical analysis dataset that was generated by MS-DIAL. Correlation analysis for birth weight data suggested that up to 80% of the significantly associated peaks were rejected by the data processing thresholds that were used in the original publication. The re-analysis with minimum possible thresholds recovered metabolic insights about C19 steroids and hydroxy-acyl-carnitines and their relationships with birth weight. Conclusions: Data processing thresholds for peak height and detection frequencies at individual data file and at the alignment level should be used at minimal possible level or completely avoided for mining untargeted chemical analysis data in the exposome research for discovering new biomarkers and mechanisms.

CANTARE: finding and visualizing network-based multi-omic predictive models

BACKGROUND

One goal of multi-omic studies is to identify interpretable predictive models for outcomes of interest, with analytes drawn from multiple omes. Such findings could support refined biological insight and hypothesis generation. However, standard analytical approaches are not designed to be "ome aware." Thus, some researchers analyze data from one ome at a time, and then combine predictions across omes. Others resort to correlation studies, cataloging pairwise relationships, but lacking an obvious approach for cohesive and interpretable summaries of these catalogs.

METHODS

We present a novel workflow for building predictive regression models from network neighborhoods in multi-omic networks. First, we generate pairwise regression models across all pairs of analytes from all omes, encoding the resulting "top table" of relationships in a network. Then, we build predictive logistic regression models using the analytes in network neighborhoods of interest. We call this method CANTARE (Consolidated Analysis of Network Topology And Regression Elements).

RESULTS

We applied CANTARE to previously published data from healthy controls and patients with inflammatory bowel disease (IBD) consisting of three omes: gut microbiome, metabolomics, and microbial-derived enzymes. We identified 8 unique predictive models with AUC > 0.90. The number of predictors in these models ranged from 3 to 13. We compare the results of CANTARE to random forests and elastic-net penalized regressions, analyzing AUC, predictions, and predictors. CANTARE AUC values were competitive with those generated by random forests and  penalized regressions. The top 3 CANTARE models had a greater dynamic range of predicted probabilities than did random forests and penalized regressions (p-value = 1.35 × 10-5). CANTARE models were significantly more likely to prioritize predictors from multiple omes than were the alternatives (p-value = 0.005). We also showed that predictive models from a network based on pairwise models with an interaction term for IBD have higher AUC than predictive models built from a correlation network (p-value = 0.016). R scripts and a CANTARE User's Guide are available at https://sourceforge.net/projects/cytomelodics/files/CANTARE/ .

CONCLUSION

CANTARE offers a flexible approach for building parsimonious, interpretable multi-omic models. These models yield quantitative and directional effect sizes for predictors and support the generation of hypotheses for follow-up investigation.