Prospective association of the infant gut microbiome with social behaviors in the ECHO consortium.

BACKGROUND: Identifying modifiable risk factors of autism spectrum disorders (ASDs) may inform interventions to reduce financial burden. The infant/toddler gut microbiome is one such feature that has been associated with social behaviors, but results vary between cohorts. We aimed to identify consistent overall and sex-specific associations between the early-life gut microbiome and autism-related behaviors. METHODS: Utilizing the Environmental influences on Children Health Outcomes (ECHO) consortium of United States (U.S.) pediatric cohorts, we gathered data on 304 participants with fecal metagenomic sequencing between 6-weeks to 2-years postpartum (481 samples). ASD-related social development was assessed with the Social Responsiveness Scale (SRS-2). Linear regression, PERMANOVA, and Microbiome Multivariable Association with Linear Models (MaAsLin2) were adjusted for sociodemographic factors. Stratified models estimated sex-specific effects. RESULTS: Genes encoding pathways for synthesis of short-chain fatty acids were associated with higher SRS-2 scores, indicative of ASDs. Fecal concentrations of butyrate were also positively associated with ASD-related SRS-2 scores, some of which may be explained by formula use. LIMITATIONS: The distribution of age at outcome assessment differed in the cohorts included, potentially limiting comparability between cohorts. Stool sample collection methods also differed between cohorts. Our study population reflects the general U.S. population, and thus includes few participants who met the criteria for being at high risk of developing ASD. CONCLUSIONS: Our study is among the first multicenter studies in the U.S. to describe prospective microbiome development from infancy in relation to neurodevelopment associated with ASDs. Our work contributes to clarifying which microbial features associate with subsequent diagnosis of neuropsychiatric outcomes. This will allow for future interventional research targeting the microbiome to change neurodevelopmental trajectories.

Patterns of infant fecal metabolite concentrations and social behavioral development in toddlers.

BACKGROUND: Gut-derived metabolites, products of microbial and host co-metabolism, may inform mechanisms underlying children's neurodevelopment. We investigated whether infant fecal metabolites were related to toddler social behavior. METHODS: Stool samples collected from 6-week-olds (n = 86) and 1-year-olds (n = 209) in the New Hampshire Birth Cohort Study (NHBCS) were analyzed using nuclear magnetic resonance spectroscopy metabolomics. Autism-related behavior in 3-year-olds was assessed by caregivers using the Social Responsiveness Scale (SRS-2). To assess the association between metabolites and SRS-2 scores, we used a traditional single-metabolite approach, quantitative metabolite set enrichment (QEA), and self-organizing maps (SOMs). RESULTS: Using a single-metabolite approach and QEA, no individual fecal metabolite or metabolite set at either age was associated with SRS-2 scores. Using the SOM method, fecal metabolites of six-week-olds organized into four profiles, which were unrelated to SRS-2 scores. In 1-year-olds, one of twelve fecal metabolite profiles was associated with fewer autism-related behaviors, with SRS-2 scores 3.4 (95%CI: -7, 0.2) points lower than the referent group. This profile had higher concentrations of lactate and lower concentrations of short chain fatty acids than the reference. CONCLUSIONS: We uncovered metabolic profiles in infant stool associated with subsequent social behavior, highlighting one potential mechanism by which gut bacteria may influence neurobehavior. IMPACT: Differences in host and microbial metabolism may explain variability in neurobehavioral phenotypes, but prior studies do not have consistent results. We applied three statistical techniques to explore fecal metabolite differences related to social behavior, including self-organizing maps (SOMs), a novel machine learning algorithm. A 1-year-old fecal metabolite pattern characterized by high lactate and low short-chain fatty acid concentrations, identified using SOMs, was associated with social behavior less indicative of autism spectrum disorder. Our findings suggest that social behavior may be related to metabolite profiles and that future studies may uncover novel findings by applying the SOM algorithm.

Maternal-Infant Factors in Relation to Extracellular Vesicle and Particle miRNA in Prenatal Plasma and in Postpartum Human Milk.

MicroRNAs (miRNA) in extracellular vesicles and particles (EVPs) in maternal circulation during pregnancy and in human milk postpartum are hypothesized to facilitate maternal-offspring communication via epigenetic regulation. However, factors influencing maternal EVP miRNA profiles during these two critical developmental windows remain largely unknown. In a pilot study of 54 mother-child dyads in the New Hampshire Birth Cohort Study, we profiled 798 EVP miRNAs, using the NanoString nCounter platform, in paired maternal second-trimester plasma and mature (6-week) milk samples. In adjusted models, total EVP miRNA counts were lower for plasma samples collected in the afternoon compared with the morning (p = 0.024). Infant age at sample collection was inversely associated with total miRNA counts in human milk EVPs (p = 0.040). Milk EVP miRNA counts were also lower among participants who were multiparous after delivery (p = 0.047), had a pre-pregnancy BMI > 25 kg/m2 (p = 0.037), or delivered their baby via cesarean section (p = 0.021). In post hoc analyses, we also identified 22 specific EVP miRNA that were lower among participants who delivered their baby via cesarean section (Q < 0.05). Target genes of delivery mode-associated miRNAs were over-represented in pathways related to satiety signaling in infants (e.g., CCKR signaling) and mammary gland development and lactation (e.g., FGF signaling, EGF receptor signaling). In conclusion, we identified several key factors that may influence maternal EVP miRNA composition during two critical developmental windows, which should be considered in future studies investigating EVP miRNA roles in maternal and child health.

Metabolomic data presents challenges for epidemiological meta-analysis: a case study of childhood body mass index from the ECHO consortium.

INTRODUCTION: Meta-analyses across diverse independent studies provide improved confidence in results. However, within the context of metabolomic epidemiology, meta-analysis investigations are complicated by differences in study design, data acquisition, and other factors that may impact reproducibility. OBJECTIVE: The objective of this study was to identify maternal blood metabolites during pregnancy (> 24 gestational weeks) related to offspring body mass index (BMI) at age two years through a meta-analysis framework. METHODS: We used adjusted linear regression summary statistics from three cohorts (total N = 1012 mother-child pairs) participating in the NIH Environmental influences on Child Health Outcomes (ECHO) Program. We applied a random-effects meta-analysis framework to regression results and adjusted by false discovery rate (FDR) using the Benjamini-Hochberg procedure. RESULTS: Only 20 metabolites were detected in all three cohorts, with an additional 127 metabolites detected in two of three cohorts. Of these 147, 6 maternal metabolites were nominally associated (P < 0.05) with offspring BMI z-scores at age 2 years in a meta-analytic framework including at least two studies: arabinose (Coefmeta = 0.40 [95% CI 0.10,0.70], Pmeta = 9.7 × 10-3), guanidinoacetate (Coefmeta = - 0.28 [- 0.54, - 0.02], Pmeta = 0.033), 3-ureidopropionate (Coefmeta = 0.22 [0.017,0.41], Pmeta = 0.033), 1-methylhistidine (Coefmeta = - 0.18 [- 0.33, - 0.04], Pmeta = 0.011), serine (Coefmeta = - 0.18 [- 0.36, - 0.01], Pmeta = 0.034), and lysine (Coefmeta = - 0.16 [- 0.32, - 0.01], Pmeta = 0.044). No associations were robust to multiple testing correction. CONCLUSIONS: Despite including three cohorts with large sample sizes (N > 100), we failed to identify significant metabolite associations after FDR correction. Our investigation demonstrates difficulties in applying epidemiological meta-analysis to clinical metabolomics, emphasizes challenges to reproducibility, and highlights the need for standardized best practices in metabolomic epidemiology.

Periconceptional and Prenatal Exposure to Metals and Extracellular Vesicle and Particle miRNAs in Human Milk: A Pilot Study.

Human milk is a rich source of microRNAs (miRNAs), which can be transported by extracellular vesicles and particles (EVPs) and are hypothesized to contribute to maternal-offspring communication and child development. Environmental contaminant impacts on EVP miRNAs in human milk are largely unknown. In a pilot study of 54 mother-child pairs from the New Hampshire Birth Cohort Study, we examined relationships between five metals (arsenic, lead, manganese, mercury, and selenium) measured in maternal toenail clippings, reflecting exposures during the periconceptional and prenatal periods, and EVP miRNA levels in human milk. 798 miRNAs were profiled using the NanoString nCounter platform; 200 miRNAs were widely detectable and retained for downstream analyses. Metal-miRNA associations were evaluated using covariate-adjusted robust linear regression models. Arsenic exposure during the periconceptional and prenatal periods was associated with lower total miRNA content in human milk EVPs (PBonferroni < 0.05). When evaluating miRNAs individually, 13 miRNAs were inversely associated with arsenic exposure, two in the periconceptional period and 11 in the prenatal period (PBonferroni < 0.05). Other metal-miRNA associations were not statistically significant after multiple testing correction (PBonferroni ≥ 0.05). Many of the arsenic-associated miRNAs are involved in lactation and have anti-inflammatory properties in the intestine and tumor suppressive functions in breast cells. Our findings raise the possibility that periconceptional and prenatal arsenic exposure may reduce levels of multiple miRNAs in human milk EVPs. However, larger confirmatory studies, which can apply environmental mixture approaches, evaluate potential effect modifiers of these relationships, and examine possible downstream consequences for maternal and child health and breastfeeding outcomes, are needed.

Machine-learning analysis of cross-study samples according to the gut microbiome in 12 infant cohorts.

IMPORTANCE: There are challenges in merging microbiome data from diverse research groups due to the intricate and multifaceted nature of such data. To address this, we utilized a combination of machine-learning (ML) models to analyze 16S sequencing data from a substantial set of gut microbiome samples, sourced from 12 distinct infant cohorts that were gathered prospectively. Our initial focus was on the mode of delivery due to its prior association with changes in infant gut microbiomes. Through ML analysis, we demonstrated the effective merging and comparison of various gut microbiome data sets, facilitating the identification of robust microbiome biomarkers applicable across varied study populations.

Human milk-associated bacterial communities associate with the infant gut microbiome over the first year of life.

Introduction: Microbial communities inhabiting the human infant gut are important for immune system development and lifelong health. One critical exposure affecting the bacterial colonization of the infant gut is consumption of human milk, which contains diverse microbial communities and prebiotics. We hypothesized that human milk-associated microbial profiles are associated with those of the infant gut. Methods: Maternal-infant dyads enrolled in the New Hampshire Birth Cohort Study (n = 189 dyads) contributed breast milk and infant stool samples collected approximately at 6 weeks, 4 months, 6 months, 9 months, and 12 months postpartum (n = 572 samples). Microbial DNA was extracted from milk and stool and the V4-V5 region of the bacterial 16S rRNA gene was sequenced. Results: Clustering analysis identified three breast milk microbiome types (BMTs), characterized by differences in Streptococcus, Staphylococcus, Pseudomonas, Acinetobacter, and microbial diversity. Four 6-week infant gut microbiome types (6wIGMTs) were identified, differing in abundances of Bifidobacterium, Bacteroides, Clostridium, Streptococcus, and Escherichia/Shigella, while two 12-month IGMTs (12mIGMTs) differed primarily by Bacteroides presence. At 6 weeks, BMT was associated with 6wIGMT (Fisher's exact test value of p = 0.039); this association was strongest among infants delivered by Cesarean section (Fisher's exact test value of p = 0.0028). The strongest correlations between overall breast milk and infant stool microbial community structures were observed when comparing breast milk samples to infant stool samples collected at a subsequent time point, e.g., the 6-week breast milk microbiome associated with the 6-month infant gut microbiome (Mantel test Z-statistic = 0.53, value of p = 0.001). Streptoccous and Veillonella species abundance were correlated in 6-week milk and infant stool, and 4- and 6-month milk Pantoea species were associated with infant stool Lachnospiraceae genera at 9 and 12 months. Discussion: We identified clusters of human milk and infant stool microbial communities that were associated in maternal-infant dyads at 6 weeks of life and found that milk microbial communities were more strongly associated with infant gut microbial communities in infants delivered operatively and after a lag period. These results suggest that milk microbial communities have a long-term effect on the infant gut microbiome both through sharing of microbes and other molecular mechanisms.

Umbilical cord blood immune cell profiles in relation to the infant gut microbiome.

During infancy, the interplay between the developing immune system and the microbiome is critical. We examined whether blood immune cell composition at birth in the umbilical cord (inferred by DNA methylation profiling) related to the early infant gut microbiome (assessed by 16S rRNA gene sequencing) among 73 infants in the New Hampshire Birth Cohort Study. We used generalized estimating equations and controlled for false discovery rate to select microbial taxa associated with immune cells. We found associations between the infant gut microbiome and immune cells, including a positive association between B cells and Enterobacter, a negative association between natural killer cells and Bifidobacterium, and a positive association between granulocytes and Bifidobacterium. Our findings give clues that immune profiles at the time of birth as measured in umbilical cord blood are associated with the development of the gut microbiome in early life.

Associations of maternal plasma and umbilical cord plasma metabolomics profiles with birth anthropometric measures.

BACKGROUND: The metabolomics profiles of maternal plasma during pregnancy and cord plasma at birth might influence fetal growth and birth anthropometry. The objective was to examine how maternal plasma and umbilical cord plasma metabolites are associated with newborn anthropometric measures, a known predictor of future health outcomes. METHODS: Pregnant women between 24 and 28 weeks of gestation were recruited as part of a prospective cohort study. Blood samples from 413 women at enrollment and 787 infant cord blood samples were analyzed using the Biocrates AbsoluteIDQ® p180 kit. Multivariable linear regression models were used to examine associations of cord and maternal metabolites with infant anthropometry at birth. RESULTS: In cord blood samples from this rural cohort from New Hampshire of largely white residents, 13 metabolites showed negative associations, and 10 metabolites showed positive associations with birth weight Z-score. Acylcarnitine C5 showed negative association, and 4 lysophosphatidylcholines showed positive associations with birth length Z-score. Maternal blood metabolites did not significantly correlate with birth weight and length Z-scores. CONCLUSIONS: Consistent findings were observed for several acylcarnitines that play a role in utilization of energy sources, and a lysophosphatidylcholine that is part of oxidative stress and inflammatory response pathways in cord plasma samples. IMPACT: The metabolomics profiles of maternal plasma during pregnancy and cord plasma at birth may influence fetal growth and birth anthropometry. This study examines the independent effects of maternal gestational and infant cord blood metabolomes across different classes of metabolites on birth anthropometry. Acylcarnitine species were negatively associated and glycerophospholipids species were positively associated with weight and length Z-scores at birth in the cord plasma samples, but not in the maternal plasma samples. This study identifies lipid metabolites in infants that possibly may affect early growth.

The association between early life antibiotic exposure and the gut resistome of young children: a systematic review.

Antimicrobial resistance is a growing public health burden, but little is known about the effects of antibiotic exposure on the gut resistome. As childhood (0-5 years) represents a sensitive window of microbiome development and a time of relatively high antibiotic use, the aims of this systematic review were to evaluate the effects of antibiotic exposure on the gut resistome of young children and identify knowledge gaps. We searched PubMed, Scopus, Web of Science, and the Cochrane Central Register of Controlled Trials. A PICO framework was developed to determine eligibility criteria. Our main outcomes were the mean or median difference in overall resistance gene load and resistome alpha diversity by antibiotic exposure groups. Bias assessment was completed using RoB 2 and ROBINS-I with quality of evidence assessed via the GRADE criteria. From 4885 records identified, 14 studies (3 randomized controlled trials and 11 observational studies) were included in the qualitative review. Eight studies that included information on antibiotic exposure and overall resistance gene load reported no or positive associations. Inconsistent associations were identified for the nine studies that assessed resistome alpha diversity. We identified three main groups of studies based on study design, location, participants, antibiotic exposures, and indication for antibiotics. Overall, the quality of evidence for our main outcomes was rated low or very low, mainly due to potential bias from the selective of reporting results and confounding. We found evidence that antibiotic exposure is associated with changes to the overall gut resistance gene load of children and may influence the diversity of antimicrobial resistance genes. Given the overall quality of the studies, more research is needed to assess how antibiotics impact the resistome of other populations. Nonetheless, this evidence indicates that the gut resistome is worthwhile to consider for antibiotic prescribing practices.

The relationship between the gut microbiome and the risk of respiratory infections among newborns.

Background: Emerging evidence points to a critical role of the developing gut microbiome in immune maturation and infant health; however, prospective studies are lacking. Methods: We examined the occurrence of infections and associated symptoms during the first year of life in relation to the infant gut microbiome at six weeks of age using bacterial 16S rRNA V4-V5 gene sequencing (N = 465) and shotgun metagenomics (N = 185). We used generalized estimating equations to assess the associations between longitudinal outcomes and 16S alpha diversity and metagenomics species. Results: Here we show higher infant gut microbiota alpha diversity was associated with an increased risk of infections or respiratory symptoms treated with a prescription medicine, and specifically upper respiratory tract infections. Among vaginally delivered infants, a higher alpha diversity was associated with an increased risk of all-cause wheezing treated with a prescription medicine and diarrhea involving a visit to a health care provider. Positive associations were specifically observed with Veillonella species among all deliveries and Haemophilus influenzae among cesarean-delivered infants. Conclusion: Our findings suggest that intestinal microbial diversity and the relative abundance of key taxa in early infancy may influence susceptibility to respiratory infection, wheezing, and diarrhea.

Metagenomic analysis reveals associations between salivary microbiota and body composition in early childhood.

Several studies have shown that body mass index is strongly associated with differences in gut microbiota, but the relationship between body weight and oral microbiota is less clear especially in young children. We aimed to evaluate if there is an association between child growth and the saliva microbiome. We hypothesized that associations between growth and the saliva microbiome would be moderate, similarly to the association between growth and the gut microbiome. For 236 toddlers participating in the New Hampshire Birth Cohort Study, we characterized the association between multiple longitudinal anthropometric measures of body height, body weight and body mass. Body Mass Index (BMI) z-scores were calculated, and dual-energy x-ray absorptiometry (DXA) was used to estimate body composition. Shotgun metagenomic sequencing of saliva samples was performed to taxonomically and functionally profile the oral microbiome. We found that within-sample diversity was inversely related to body mass measurements while community composition was not associated. Although the magnitude of associations were small, some taxa were consistently associated with growth and modified by sex. Certain taxa were associated with decreased weight or growth (including Actinomyces odontolyticus and Prevotella melaninogenica) or increased growth (such as Streptococcus mitis and Corynebacterium matruchotii) across anthropometric measures. Further exploration of the functional significance of this relationship will enhance our understanding of the intersection between weight gain, microbiota, and energy metabolism and the potential role of these relationships on the onset of obesity-associated diseases in later life.

MarZIC: A Marginal Mediation Model for Zero-Inflated Compositional Mediators with Applications to Microbiome Data.

BACKGROUND: The human microbiome can contribute to pathogeneses of many complex diseases by mediating disease-leading causal pathways. However, standard mediation analysis methods are not adequate to analyze the microbiome as a mediator due to the excessive number of zero-valued sequencing reads in the data and that the relative abundances have to sum to one. The two main challenges raised by the zero-inflated data structure are: (a) disentangling the mediation effect induced by the point mass at zero; and (b) identifying the observed zero-valued data points that are not zero (i.e., false zeros). METHODS: We develop a novel marginal mediation analysis method under the potential-outcomes framework to address the issues. We also show that the marginal model can account for the compositional structure of microbiome data. RESULTS: The mediation effect can be decomposed into two components that are inherent to the two-part nature of zero-inflated distributions. With probabilistic models to account for observing zeros, we also address the challenge with false zeros. A comprehensive simulation study and the application in a real microbiome study showcase our approach in comparison with existing approaches. CONCLUSIONS: When analyzing the zero-inflated microbiome composition as the mediators, MarZIC approach has better performance than standard causal mediation analysis approaches and existing competing approach.

Bronchiolitis hospitalizations in rural New England: clues to disease prevention.

Background: An improved understanding of the clinico-epidemiology of bronchiolitis hospitalizations, a clinical surrogate of respiratory syncytial virus (RSV) disease, is critical to inform public health strategies for mitigating the in-patient burden of bronchiolitis in early life. Methods: A retrospective chart review was conducted of all bronchiolitis first admissions (N = 295) to the Children's Hospital at Dartmouth-Hitchcock, CHaD, between 1 November 2010 and 31 October 2017 using the relevant International Classification of Diseases (ICD)-9 and ICD-10 codes for this illness. Abstracted data included laboratory confirmation of RSV infection, severity of illness, duration of hospitalization, age at admission in days, weight at admission, prematurity, siblings, and relevant medical pre-existing conditions. Results: Admissions for bronchiolitis were strongly associated with age of the child, the calendar month of an infant's birth, and the presence of older children in the family. Medical risk factors associated with admission included premature birth and underlying cardiopulmonary disease. Conclusion: The very early age of hospitalization emphasizes the high penetration of RSV in the community, by implication the limited protection afforded by maternal antibody, and the complexity of protecting infants from this infection. Plain Language Summary: Although risks for respiratory syncytial virus (RSV)/bronchiolitis hospitalization are well described, few studies have examined, with precision, the age-related frequency and severity of RSV/bronchiolitis. We also explore the implications of RSV clinico-epidemiology for our understanding of the pathogenesis of the disease and development of optimal approaches to prevention.

CBEA: Competitive balances for taxonomic enrichment analysis.

Research in human-associated microbiomes often involves the analysis of taxonomic count tables generated via high-throughput sequencing. It is difficult to apply statistical tools as the data is high-dimensional, sparse, and compositional. An approachable way to alleviate high-dimensionality and sparsity is to aggregate variables into pre-defined sets. Set-based analysis is ubiquitous in the genomics literature and has demonstrable impact on improving interpretability and power of downstream analysis. Unfortunately, there is a lack of sophisticated set-based analysis methods specific to microbiome taxonomic data, where current practice often employs abundance summation as a technique for aggregation. This approach prevents comparison across sets of different sizes, does not preserve inter-sample distances, and amplifies protocol bias. Here, we attempt to fill this gap with a new single-sample taxon enrichment method that uses a novel log-ratio formulation based on the competitive null hypothesis commonly used in the enrichment analysis literature. Our approach, titled competitive balances for taxonomic enrichment analysis (CBEA), generates sample-specific enrichment scores as the scaled log-ratio of the subcomposition defined by taxa within a set and the subcomposition defined by its complement. We provide sample-level significance testing by estimating an empirical null distribution of our test statistic with valid p-values. Herein, we demonstrate, using both real data applications and simulations, that CBEA controls for type I error, even under high sparsity and high inter-taxa correlation scenarios. Additionally, CBEA provides informative scores that can be inputs to downstream analyses such as prediction tasks.

Impact of antibiotics on off-target infant gut microbiota and resistance genes in cohort studies.

BACKGROUND: Young children are frequently exposed to antibiotics, with the potential for collateral consequences to the gut microbiome. The impact of antibiotic exposures to off-target microbes (i.e., bacteria not targeted by treatment) and antibiotic resistance genes (ARGs) is poorly understood. METHODS: We used metagenomic sequencing data from paired stool samples collected prior to antibiotic exposure and at 1 year from over 200 infants and a difference-in-differences approach to assess the relationship between subsequent exposures and the abundance or compositional diversity of microbes and ARGs while adjusting for covariates. RESULTS: By 1 year, the abundance of multiple species and ARGs differed by antibiotic exposure. Compared to infants never exposed to antibiotics, Bacteroides vulgatus relative abundance increased by 1.72% (95% CI: 0.19, 3.24) while Bacteroides fragilis decreased by 1.56% (95% CI: -4.32, 1.21). Bifidobacterium species also exhibited opposing trends. ARGs associated with exposure included class A beta-lactamase gene CfxA6. Among infants attending day care, Escherichia coli and ARG abundance were both positively associated with antibiotic use. CONCLUSION: Novel findings, including the importance of day care attendance, were identified through considering microbiome data at baseline and post-intervention. Thus, our study design and approach have important implications for future studies evaluating the unintended impacts of antibiotics. IMPACT: The impact of antibiotic exposure to off-target microbes and antibiotic resistance genes in the gut is poorly defined. We quantified these impacts in two cohort studies using a difference-in-differences approach. Novel to microbiome studies, we used pre/post-antibiotic data to emulate a randomized controlled trial. Compared to infants unexposed to antibiotics between baseline and 1 year, the relative abundance of multiple off-target species and antibiotic resistance genes was altered. Infants who attended day care and were exposed to antibiotics within the first year had a higher abundance of Escherichia coli and antibiotic resistance genes; a novel finding warranting further investigation.

Chemical exposures assessed via silicone wristbands and endogenous plasma metabolomics during pregnancy.

BACKGROUND: Metabolomics is a promising method to investigate physiological effects of chemical exposures during pregnancy, with the potential to clarify toxicological mechanisms, suggest sensitive endpoints, and identify novel biomarkers of exposures. OBJECTIVE: Investigate the influence of chemical exposures on the maternal plasma metabolome during pregnancy. METHODS: Data were obtained from participants (n = 177) in the New Hampshire Birth Cohort Study, a prospective pregnancy cohort. Chemical exposures were assessed via silicone wristbands worn for one week at ~13 gestational weeks. Metabolomic features were assessed in plasma samples obtained at ~24-28 gestational weeks via the Biocrates AbsoluteIDQ® p180 kit and nuclear magnetic resonance (NMR) spectroscopy. Associations between chemical exposures and plasma metabolomics were investigated using multivariate modeling. RESULTS: Chemical exposures predicted 11 (of 226) and 23 (of 125) metabolomic features in Biocrates and NMR, respectively. The joint chemical exposures did not significantly predict pathway enrichment, though some individual chemicals were associated with certain amino acids and related metabolic pathways. For example, N,N-diethyl-m-toluamide was associated with the amino acids glycine, L-glutamic acid, L-asparagine, and L-aspartic acid and enrichment of the ammonia recycling pathway. SIGNIFICANCE: This study contributes evidence to the potential effects of chemical exposures during pregnancy upon the endogenous maternal plasma metabolome.

Information enhanced model selection for Gaussian graphical model with application to metabolomic data.

In light of the low signal-to-noise nature of many large biological data sets, we propose a novel method to learn the structure of association networks using Gaussian graphical models combined with prior knowledge. Our strategy includes two parts. In the first part, we propose a model selection criterion called structural Bayesian information criterion, in which the prior structure is modeled and incorporated into Bayesian information criterion. It is shown that the popular extended Bayesian information criterion is a special case of structural Bayesian information criterion. In the second part, we propose a two-step algorithm to construct the candidate model pool. The algorithm is data-driven and the prior structure is embedded into the candidate model automatically. Theoretical investigation shows that under some mild conditions structural Bayesian information criterion is a consistent model selection criterion for high-dimensional Gaussian graphical model. Simulation studies validate the superiority of the proposed algorithm over the existing ones and show the robustness to the model misspecification. Application to relative concentration data from infant feces collected from subjects enrolled in a large molecular epidemiological cohort study validates that metabolic pathway involvement is a statistically significant factor for the conditional dependence between metabolites. Furthermore, new relationships among metabolites are discovered which can not be identified by the conventional methods of pathway analysis. Some of them have been widely recognized in biological literature.

Alterations in Microbial-Associated Fecal Metabolites in Relation to Arsenic Exposure Among Infants.

In utero and early life exposure to inorganic arsenic (iAs) alters immune response in experimental animals and is associated with an increased risk of infant infections. iAs exposure is related to differences in the gut microbiota diversity, community structure, and the relative abundance of individual microbial taxa both in laboratory and human studies. Metabolomics permits a direct measure of molecular products of microbial and host metabolic processes. We conducted NMR metabolomics analysis on infant stool samples and quantified the relative concentrations of 34 known microbial-related metabolites. We examined these metabolites in relation to both in utero and infant log2 urinary total arsenic concentrations (utAs, the sum of iAs and iAs metabolites) collected at approximately 6 weeks of age using linear regression models, adjusted for infant sex, age at sample collection, type of delivery (vaginal vs. cesarean section), feeding mode (breast milk vs. any formula), and specific gravity. Increased fecal butyrate (b = 214.24), propionate (b = 518.33), cholate (b = 8.79), tryptophan (b= 14.23), asparagine (b = 28.80), isoleucine (b = 65.58), leucine (b = 95.91), malonate (b = 50.43), and uracil (b = 36.13), concentrations were associated with a doubling of infant utAs concentrations (p< 0.05). These associations were largely among infants who were formula fed. No clear associations were observed with maternal utAs and infant fecal metabolites. Metabolomic analyses of infant stool samples lend further evidence that the infant gut microbiota is sensitive to As exposure, and these effects may have functional consequences.

Using Satellite Images and Deep Learning to Identify Associations Between County-Level Mortality and Residential Neighborhood Features Proximal to Schools: A Cross-Sectional Study.

What is the relationship between mortality and satellite images as elucidated through the use of Convolutional Neural Networks? Background: Following a century of increase, life expectancy in the United States has stagnated and begun to decline in recent decades. Using satellite images and street view images, prior work has demonstrated associations of the built environment with income, education, access to care, and health factors such as obesity. However, assessment of learned image feature relationships with variation in crude mortality rate across the United States has been lacking. Objective: We sought to investigate if county-level mortality rates in the U.S. could be predicted from satellite images. Methods: Satellite images of neighborhoods surrounding schools were extracted with the Google Static Maps application programming interface for 430 counties representing ~68.9% of the US population. A convolutional neural network was trained using crude mortality rates for each county in 2015 to predict mortality. Learned image features were interpreted using Shapley Additive Feature Explanations, clustered, and compared to mortality and its associated covariate predictors. Results: Predicted mortality from satellite images in a held-out test set of counties was strongly correlated to the true crude mortality rate (Pearson r = 0.72). Direct prediction of mortality using a deep learning model across a cross-section of 430 U.S. counties identified key features in the environment (e.g., sidewalks, driveways, and hiking trails) associated with lower mortality. Learned image features were clustered, and we identified 10 clusters that were associated with education, income, geographical region, race, and age. Conclusions: The application of deep learning techniques to remotely-sensed features of the built environment can serve as a useful predictor of mortality in the United States. Although we identified features that were largely associated with demographic information, future modeling approaches that directly identify image features associated with health-related outcomes have the potential to inform targeted public health interventions.