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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.

The Environmental influences on Child Health Outcomes (ECHO)-wide Cohort Study (EWC), a collaborative research design comprising 69 cohorts in 31 consortia, was funded by the National Institutes of Health (NIH) in 2016 to improve children’s health in the United States. The EWC harmonizes extant data and collects new data using a standardized protocol, the ECHO-wide Cohort data Collection Protocol (EWCP). EWCP visits occur at least once per life stage, but the frequency and timing of the visits vary across cohorts. As of March 4, 2022, the EWC cohorts contributed data from 60,553 children and consented 29,622 children for new EWCP data and biospecimen collection. The median (interquartile range) age of EWCP-enrolled children was 7.5 years (3.7-11.1). Surveys, interviews, standardized examinations, laboratory analyses, and medical record abstraction are used to obtain information in five main outcome areas: pre-, peri-, and post-natal outcomes; neurodevelopment; obesity; airways; and positive health. Exposures include place- (e.g., air pollution, neighborhood socioeconomic status), family- (e.g., parental mental health), and individual-level (e.g., diet, genomics) factors.

BACKGROUND: A link between the gut microbiome and behavior is hypothesized, but most previous studies are cross-sectional or in animal models. The modifying role of host sex is poorly characterized. We aimed to identify sex-specific prospective associations between the early-life gut microbiome and preschool-age neurobehavior. METHODS: In a prospective cohort, gut microbiome diversity and taxa were estimated with 16S rRNA sequencing at six weeks, one year, and two years. Species and gene pathways were inferred from metagenomic sequencing at six weeks and one year. When subjects were three years old, parents completed the Behavioral Assessment System for Children, 2nd edition (BASC-2). 260 children contributed 523 16S rRNA and 234 metagenomics samples to analysis. Models adjusted for sociodemographic characteristics. RESULTS: Higher diversity at six weeks was associated with better Internalizing Problems among boys, but not girls [β(Boys)=−1.86 points/SD Shannon diversity; 95% CI (−3.29,−0.42), p(Boys)=0.01, β(Girls)=0.22 (−1.43,1.87), p(Girls)=0.8, p(interaction)=0.06]. Among other taxa-specific associations, Bifidobacterium at six weeks was associated with Adaptive Skills scores in a sex-specific manner. We observed relationships between functional features and BASC-2 scores, including vitamin B6 biosynthesis pathways and better Depression scores. CONCLUSIONS: This study advances our understanding of microbe-host interactions with implications for childhood behavioral health.

The gut-lung axis describes the interaction between microbiota in the gut and health status of the airway, whereby there is a bidirectional relationship facilitated by systemic transport of microbially-derived metabolites and immune factors. Cystic fibrosis (CF) is a genetic disease that is associated with dysbiosis of the gut microbiota. Recent literature has shown that the microbial dysbiosis in the CF gut can alter the hosts’ inflammatory status and that there are distinct microbial compositions in children with CF who have low versus high intestinal inflammation. These distinct microbial profiles can be used as signatures in children with CF (cwCF) to predict health outcomes. Here, we use supervised machine learning to train a random forest model on the distinct microbial composition of cwCF to predict: age (as a validation of the method), frequency of upper respiratory infection (URIfreq), and neutrophil to lymphocyte ratio (NLR), a clinical marker for systemic inflammation that negatively correlates with lung function. We find that the out of bag error, a measure of model accuracy, is lower when predicting age for cwCF compared to children without CF, consistent with previous data. We are able to predict high URIfreq with only 16% error and high NLR with 27% error. This machine learning pipeline may allow physicians and microbiome researchers to use the stool microbiota of cwCF as a tool for identifying individuals with the more negative airway clinical outcomes from this population, and potentially allow for early intervention.ImportanceChildren with CF (cwCF) often experience chronic respiratory infections, leading to progressive, irreversible lung function decline and significant morbidity and premature mortality. Modulator therapy has revolutionized the treatment of eligible adults with CF. cwCF as young as age 6 are now eligible to receive modulator therapy, although by early childhood many children have already experienced pulmonary exacerbations. Here we show that for cwCF, stool microbiota composition is associated with higher upper respiratory infection frequency and increased systemic inflammation. Our findings may aid in developing diagnostic tools that can allow physicians further understanding of which intestinal microbiota profiles are associated with health outcomes and to identify targets for preventative treatment for cwCF.

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.

The target of inference in microbiome analyses is usually relative abundance (RA) because RA in a sample (e.g., stool) can be considered as an approximation of RA in an entire ecosystem (e.g., gut). However, inference on RA suffers from the fact that RA are calculated by dividing absolute abundances (AA) over the common denominator (CD), the summation of all AA (i.e., library size). Because of that, perturbation in one taxon will result in a change in the CD and thus cause false changes in RA of all other taxa, and those false changes could lead to false positive/negative findings. We propose a novel analysis approach (IFAA) to make robust inference on AA of an ecosystem that can circumvent the issues induced by the CD problem and compositional structure of RA. IFAA can also address the confounding effect of library size and handle zero-inflated data structures. IFAA identifies microbial taxa associated with the covariates in Phase one and estimates the association parameters by employing an independent reference taxon in Phase two. Two real data applications are presented and extensive simulations show that IFAA outperforms other established existing approaches by a big margin in the presence of confounding effect of library size., Corresponding email: zhigang.li@ufl.edu

Cystic Fibrosis is a heritable disease that causes altered physiology at mucosal sites; these changes result in chronic infection in the lung, significant gastrointestinal complications as well as dysbiosis of the gut microbiome, although the latter has been less well explored. Here, we describe the longitudinal development of the gut microbiome in a cohort of children with cystic fibrosis (cwCF) from birth through early childhood (0-4 years of age) using 16S rRNA gene amplicon sequencing of stool samples as a surrogate for the gut microbiota. Similar to healthy populations, alpha diversity of the gut microbiome increases significantly with age, but diversity plateaus ∼2 years of age for this CF cohort. Several taxa that have been associated with dysbiosis in CF change with age towards a more healthy-like composition; notable exceptions include Akkermansia, which decreases with age, and Blautia, which increases with age. We also examined the relative abundance and prevalence of nine taxa associated with CF lung disease, several of which persist across early life, highlighting the possibility of the lung being seeded directly from the gut early in life. Finally, we applied the Crohn’s dysbiosis index to each sample, and found that high Crohn’s-associated dysbiosis early in life (Bacteroides in samples collected from 2-4 years of age. Together, these data indicate a persisting dysbiosis in the gut microbiota as well as markers associated with inflammatory bowel disease early in life for cwCF.IMPORTANCECystic Fibrosis is a heritable disease that disrupts ion transport at mucosal surfaces, causing a buildup of mucus and dysregulation of microbial communities in both the lungs and the intestines. Persons with CF are known to have dysbiotic gut microbial communities, but the development of these communities over time beginning at birth have not been thoroughly studied. Here, we describe the development of the gut microbiome of cwCF throughout the first four years of life, during the critical window of both gut microbiome and immune development. Our findings indicate a persisting dysbiosis, the possibility of the gut microbiota as a reservoir of airway pathogens and a surprisingly early indication of a microbiota associated with inflammatory bowel disease.

Persons with cystic fibrosis, starting in early life, have intestinal microbiome dysbiosis characterized in part by a decreased relative abundance of the genus Bacteroides. Bacteroides is a major producer of the intestinal short chain fatty acid (SCFA) propionate. We demonstrate here that CFTR-/- Caco-2 intestinal epithelial cells are responsive to the anti-inflammatory effects of propionate. Furthermore, Bacteroides isolates inhibit the IL-1β-induced inflammatory response of CFTR-/- Caco-2 intestinal epithelial cells and do so in a propionate-dependent manner. Bacteroides isolates also produce low levels of butyrate; this SCFA is positively correlated with inhibition of the inflammatory response. Finally, the introduction of Bacteroides-supplemented stool from infants with CF into the gut of CftrF508del mice results in an increase in propionate in the stool as well as the reduction in several systemic pro-inflammatory cytokines. Bacteroides supplementation also reduced the fecal relative abundance of E. coli, indicating a potential interaction between these two microbes, consistent with previous clinical studies. Together, our data indicate the important role of Bacteroides and Bacteroides-derived propionate in the context of the developing microbiome in infants and children with CF, which could help explain the observed gut-lung axis in CF.

The human gastrointestinal tract is an environment that hosts an ecosystem of microorganisms essential to human health. Vital biological processes emerge from fundamental inter- and intra-species molecular interactions that influence the assembly and composition of the gut microbiota ecology. Here we quantify the complexity of the ecological relationships within the human infant gut microbiota ecosystem as a function of the information contained in the nonlinear associations of a sequence of increasingly-specified maximum entropy representations of the system. Our paradigm frames the ecological state, in terms of the presence or absence of individual microbial ecological units that are identified by amplicon sequence variants (ASV) in the gut microenvironment, as a function of both the ecological states of its neighboring units and, in a departure from standard graphical model representations, the associations among the units within its neighborhood. We characterize the order of the system based on the relative quantity of statistical information encoded by high-order statistical associations of the infant gut microbiota.

The microbial interactions within the human microbiome are complex and temporally dynamic, but few methods are available to model this system within a longitudinal network framework. Based on general longitudinal 16S rRNA sequence data, we propose a stationary Gaussian graphical model (SGGM) for microbial interaction networks (MIN) which can accommodate the possible correlations between the high-dimensional observations. For SGGM, an EM-type algorithm is devised to compute theL1-penalized maximum likelihood estimate of MIN which employs the classic graphical LASSO algorithm as the building block and can therefore be implemented easily. Simulation studies show that the proposed algorithms can significantly outperform the conventional algorithms when the correlations between measurements grow large. The algorithms are then applied to a real 16S rRNA gene sequence data set for gut microbiome. With the estimated MIN in hand, module-preserving permutation test is proposed to test the independence of the MIN and the corresponding phylogenetic tree. The results demonstrate strong evidence of an association between the MIN and the phylogenetic tree which indicates that the genetically related taxa tend to have more/stronger interactions. The proposed algorithms are implemented in R packagelglassoathttps://CRAN.R-project.org/package=lglasso.

The volume and breadth of research on the role of the microbiome in neurodevelopmental and neuropsychiatric disorders has expanded greatly over the last decade, opening doors to new models of mechanisms of the gut-brain axis and therapeutic interventions to reduce the burden of these outcomes. Studies have highlighted the window of birth to 3 years as an especially sensitive window when interventions may be the most effective. Harnessing the powerful gut-brain axis during this critical developmental window clarifies important investigations into the microbe-human connection and the developing brain, affording opportunities to prevent rather than treat neurodevelopmental disorders and neuropsychiatric illness. In this review, we present an overview of the developing intestinal microbiome in the critical window of birth to age 3; and its prospective relationship with neurodevelopment, with particular emphasis on immunological mechanisms. Next, the role of the microbiome in neurobehavioral outcomes (such as autism, anxiety, and attention-deficit hyperactivity disorder) as well as cognitive development are described. In these sections, we highlight the importance of pairing mechanistic studies in murine models with large scale epidemiological studies that aim to clarify the typical health promoting microbiome in early life across varied populations in comparison to dysbiosis. The microbiome is an important focus in human studies because it is so readily alterable with simple interventions, and we briefly outline what is known about microbiome targeted interventions in neurodevelopmental outcomes. More novel examinations of known environmental chemicals that adversely impact neurodevelopmental outcomes and the potential role of the microbiome as a mediator or modifier are discussed. Finally, we look to the future and emphasize the need for additional research to identify populations that are sensitive to alterations in their gut microbiome and clarify how interventions might correct and optimize neurodevelopmental outcomes.

BackgroundYoung children are frequently exposed to antibiotics for otitis media and respiratory infections, with the potential for collateral consequences on the gut microbiome. The impact of antibiotic exposures to off-target microbes (i.e., bacteria not targeted by antibiotic treatment) and antibiotic resistance genes (ARGs) is unknown.MethodsWe 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 off-target microbes and ARGs while adjusting for covariates.ResultsBy 1 year, the relative 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 suggesting differential antibiotic selection. Overall, antibiotic exposure was associated with a dose-dependent decrease in alpha diversity of off-target microbes. ARGs associated with antibiotic 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.ConclusionFurther quantifying impacts to off-target microbes and ARGs has implications for antibiotic stewardshipImpactInfants are frequently exposed to antibiotics for respiratory illnesses, but the extent of impact to off-target gut microbiota and antibiotic resistance genes is unknown.We quantified these impacts in 2 cohort studies using a difference-in-differences approach. Novel to the microbiome space, this enabled us to use pre/post antibiotic data to emulate a randomized controlled trial.Compared to infants unexposed to antibiotics between six weeks and 1 year, the relative abundance of multiple off-target species and antibiotic resistance genes was altered.This research highlights the need to consider the microbiome in antibiotic stewardship and offers a new framework for quantifying change.

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.

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.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.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.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.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.

Prenatal arsenic exposure is associated with an increased risk of lung cancer along with multiple non-carcinogenic outcomes, including respiratory diseases in arsenic-contaminated areas. Limited epidemiologic data exist on whether in utero arsenic exposure influences lung development and subsequent respiratory health. We investigated the association between gestational arsenic exposure and childhood lung function in the New Hampshire Birth Cohort Study. Urinary arsenic speciation including inorganic arsenic (iAs), monomethylarsonic acid (MMA), dimethylarsinic acid (DMA) and arsenobetaine was measured in maternal urine samples collected during pregnancy and spirometry was performed in offspring at a median age of 7.4 years. Forced vital capacity (FVC), forced expiratory volume in the first second of exhalation (FEV1), and forced expiratory flow between 25% and 75% of FVC (FEF25-75) standardized z-scores were assessed in linear models as dependent variables with the log2-transformed summation of urinary arsenic species (ΣAs = iAs + MMA + DMA) corrected for specific gravity as an independent variable and with adjustment for maternal smoking status, children’s age, sex and height. Among the 358 children in the study, a doubling of ΣAs was associated with a −0.08 (s) decrease in FVC z-scores (95% confidence interval (CI) from −0.14 to −0.01) and −0.10 (s) (95% CI from −0.18 to −0.02) decrease in FEV1 z-scores. The inverse association appeared stronger among those mothers with lower secondary methylation index (urinary DMA/MMA), especially among girls. No association was observed for FEF25-75 z-scores. Our results suggest that gestation arsenic exposure at levels relevant to the general US population during the vulnerable period of lung formation may adversely affect lung function in childhood.

Background Mechanistic studies support the potential for mercury (Hg) to alter immunity, including via in utero exposure. As yet, there are few prospective studies of in utero Hg exposure and subsequent immune-related outcomes, especially in infancy. Objectives We investigated the association of biomarkers of prenatal Hg exposure and maternal silver-mercury dental amalgams with the occurrence of infant allergy, respiratory infection, and respiratory symptoms in the first year of life. Methods The New Hampshire Birth Cohort Study (NHBCS) ascertained information on infant allergies, infections and symptoms through telephone interviews at 4, 8 and 12 months postpartum and measured total Hg in maternal toenails collected at ~28–30 weeks gestation. Information on maternal fish consumption and presence of dental amalgams was obtained from a questionnaire administered at study enrollment at 24–28 weeks. A total of 1321 NHBCS mother-infant pairs had at least one Hg exposure measure (toenail Hg or information on dental amalgams) and information on dietary fish intake. Generalized linear models and generalized estimating equation models with Poisson regression adjusted for potential confounders (maternal age, level of education, parity, smoking, alternative Healthy Eating Index-2010, infant sex, gestational age, feeding mode, and day care attendance) were used to assess the association between infant outcomes and prenatal toenail Hg levels. We subsetted this analysis on mothers who consumed fish (n = 706) as a measure of in utero methylmercury (MeHg) exposure. Associations between infant outcomes and dental amalgams as a measure of in utero inorganic Hg exposure were assessed among mothers who did not consume fish (n = 218). Results Among women who ate fish during pregnancy, higher maternal toenail Hg concentrations were associated with an increased risk of lower respiratory infections and respiratory symptoms requiring a doctor visit among infants age 9–12 months (relative risk (RR) 1.4 (95% CI: 1.1, 1.9) and 1.2 (95% CI: 1.0, 1.4) respectively), whereas a reduced risk of lower respiratory infections was observed among infants 0–4 months of age (RR = 0.7 (95% CI: 0.5, 1.0). We found little to no evidence of associations of toenail Hg with upper respiratory infections, allergy or eczema at any age to one year. Among infants of mothers who did not consume fish, we found an elevated risk of upper respiratory infections requiring a doctor visit in relation to having dental amalgams during pregnancy (RR = 1.5 (95% CI: 1.1, 2.1)). Overall, weaker associations were observed with lower respiratory infections, respiratory symptoms, and medically confirmed allergies, and there was no association with eczema. Conclusions Our analyses of a US birth cohort, along with prior mechanistic work, raise the possibility that gestational Hg exposure through fish/seafood consumption and dental amalgams may alter respiratory infections and respiratory symptoms in infants.

BACKGROUND AND AIM: Prenatal environmental exposure to metals and metalloids (referred to as “metals”) has been associated with childhood lung development, but limited data exist on metal mixtures....

Introduction: The prevalence of overweight and obesity in children ages 2-5 years continues to rise in the US. Experimental germ-free animal models indicate gut microbiota can cause excess weight gain. Observational human studies, mostly cross-sectional, also suggest gut microbiota is associated with obesity, but these studies have largely been conducted in older children and adults. Infants have a unique gut microbiota composition and function, under strong influence by human milk. Bifidobacteria, in particular, is hypothesized to be beneficial in the presence of human milk oligosaccharides. To our knowledge, no longitudinal studies have examined the association of the infant gut microbiota with childhood BMI, taking into account intake of human milk. Hypothesis: We hypothesized that infant gut microbiota composition, in particular relative abundance of Bifidobacteria, in the first year is prospectively associated with differences in child BMI from ages 2-5 years and associations are modified by duration of human milk feeding. Methods: We examined longitudinal data from mother-child dyads in the New Hampshire Birth Cohort, which began enrolling pregnant women from New Hampshire in 2009. We measured the infant gut microbiota using 16S rRNA sequencing at 6 weeks and 12 months of age. We estimated alpha diversity using the Shannon diversity index. Child BMI z scores (BMI-z) at 2-5 years of age were calculated using sex- and age-specific WHO growth charts. We used unadjusted and multivariable adjusted linear mixed models. We adjusted for pre-pregnancy BMI, birth weight, delivery mode, and infant BMI-z at 12 months (in 12-month microbiota models). We considered effect measure modification by breastfeeding duration. Results: Our analytic sample comprised 148 and 146 infants with microbiota data at 6 weeks and 12 months, respectively, and at least 1 BMI-z from ages 2-5 years. Shannon diversity at 6 weeks and 12 months of age, and top genera at 6 weeks were not significantly associated with child BMI-z. Abundance of 12-month Bifidobacterium was associated with lower BMI-z (-0.12; 95% CI (-0.25, 0.006)) and interacted with breastfeeding duration (p interaction Bifidobacterium was associated with a 0.22 (95% CI: 0.06, 0.37) lower child BMI-z. Lower abundance of 12-month Prevotella , a bacteria linked to obesity in adults, was suggestively associated with lower child BMI-z and this association was also modified by breastfeeding duration (p interaction=0.01), such that it was only significant among infants breastfed < 6 months (0.34; 95% CI: 0.09, 0.60). Both interactions were consistent when breastfeeding duration was dichotomized at 12 months. Conclusion: Higher percent Bifidobacterium and lower Prevotella at 12 months was prospectively associated with lower childhood BMI-z, and both associations were modified by breastfeeding duration.

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.

Summary 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.

Background/Aim: In our previous analysis in the New Hampshire Birth Cohort Study, we identified associations between nutrient-toxic element mixtures and the early-life microbiome. The role of nutri...

The hypothesized link between gut bacteria and autism spectrum disorder (ASD) has been explored through animal models and human studies with microbiome assessment after ASD presentation. We aimed to prospectively characterize the association between the infant/toddler gut microbiome and ASD-related social behaviors at age 3 years. As part of an ongoing birth cohort gut bacterial diversity, structure, taxa, and function at 6 weeks (n = 166), 1 year (n = 158), 2 years (n = 129), and 3 years (n = 140) were quantified with 16S rRNA gene and shotgun metagenomic sequencing (n = 101 six weeks, n = 103 one year). ASD-related social behavior was assessed at age 3 years using Social Responsiveness Scale (SRS-2) T-scores. Covariate-adjusted linear and permutation-based models were implemented. Microbiome structure at 1 year was associated with SRS-2 total T-scores (p = 0.01). Several taxa at 1, 2, and 3 years were associated with SRS-2 performance, including many in the Lachnospiraceae family. Higher relative abundance of Adlercreutzia equolifaciens and Ruminococcus torques at 1 year related to poorer SRS-2 performance. Two functional pathways, l-ornithine and vitamin B6 biosynthesis, were associated with better social skills at 3 years. Our results support potential associations between early-childhood gut microbiome and social behaviors. Future mechanistic studies are warranted to pinpoint sensitive targets for intervention.

BackgroundThe infant intestinal microbiome plays an important role in metabolism and immune development with impacts on lifelong health. The linkage between the taxonomic composition of the microbiome and its metabolic phenotype is undefined and complicated by redundancies in the taxon-function relationships within microbial communities. To inform a more mechanistic understanding of the relationship between the microbiome and health, we performed an integrative statistical and machine learning-based analysis of microbe taxonomic structure and metabolic function in order to characterize the taxa-function relationship in early life.ResultsStool samples collected from infants enrolled in the New Hampshire Birth Cohort Study (NHBCS) at approximately 6-weeks (n = 168) and 12-months (n = 282) of age were profiled using targeted and untargeted nuclear magnetic resonance (NMR) spectroscopy as well as DNA sequencing of the V4-V5 hypervariable region from the bacterial 16S rRNA gene. There was significant inter-omic concordance based on Procrustes analysis of sample distances (6 weeks: p = 0.056; 12 months: p = 0.001), however this association was no longer significant when accounting for phylogenetic relationships using generalized UniFrac distance metric (6 weeks: p = 0.376; 12 months: p = 0.069). Sparse canonical correlation analysis showed significant correlation, as well as identifying sets of microbe/metabolites driving microbiome-metabolome relatedness. Performance of machine learning models varied across different metabolites, with support vector machines (radial basis function kernel) being the consistently top ranked model. However, predictive R2 values demonstrated poor predictive performance across all models assessed (avg. R2: -5.06% -- 6 weeks; -3.7% -- 12 months). Conversely, the Spearman correlation metric was higher (avg. correlation: 0.344 – 6 weeks; 0.265 – 12 months). This demonstrated that taxonomic relative abundance was not predictive of metabolite concentrations. ConclusionsOur results suggest a degree of overall association between taxonomic profiles and metabolite concentrations. However, lack of predictive capacity for stool metabolic signatures reflects, in part, the possible role of functional redundancy in defining the taxa-function relationship in early life as well as the bidirectional nature of the microbiome-metabolome association. Our results provide evidence in favor of a multi-omic approach for microbiome studies, especially those focused on health outcomes.

Background: Antibiotic resistance is an increasing threat to human health. The human gut microbiome harbors a collection of bacterial antimicrobial resistance genes (ARGs) known as the resistome. The factors associated with establishment of the resistome in early life are not well understood and clarifying these factors would inform strategies to decrease antibiotic resistance. We investigated the early-life exposures and taxonomic signatures associated with resistome development over the first year of life in a large, prospective cohort in the United States. Shotgun metagenomic sequencing was used to profile both microbial composition and ARGs in stool samples collected at 6 weeks and 1 year of age from infants enrolled in the New Hampshire Birth Cohort Study. Negative binomial regression and statistical modeling was used to examine infant factors such as sex, delivery mode, feeding method, gestational age, antibiotic exposure, and infant gut microbiome composition in relation to the diversity and relative abundance of ARGs.Results: Metagenomic sequencing was performed on paired samples from 195 full term (at least 37 weeks’ gestation) and 15 late preterm (33-36 weeks’ gestation) infants. 6-week samples compared to 1-year samples had 4.37 times (95% CI: 3.54-5.39) the rate of harboring ARGs. The majority of ARGs that were at a greater relative abundance at 6 weeks (chi-squared p < 0.01) worked through the mechanism of antibiotic efflux (i.e., by pumping antibiotics out of the cell). The overall relative abundance of the resistome was strongly correlated with Proteobacteria (Spearman correlation = 78.9%) and specifically E. coli (62.2%) relative abundance in the gut microbiome. Among infant characteristics, delivery mode was most strongly associated with the diversity and relative abundance of ARGs. Infants born via cesarean delivery had a higher risk of harboring unique ARGs [relative risk = 1.12 (95% CI: 0.97 – 1.29)] as well as a having an increased risk for overall ARG relative abundance [relative risk = 1.43 (95% CI: 1.12 – 1.84)] at 1 year compared to infants born vaginally. Additionally, 6 specific ARGs were at a greater relative abundance in infants delivered by cesarean section compared to vaginally delivered infants across both time points. Conclusions: Our findings suggest that the developing infant gut resistome may be alterable by early-life exposures. Establishing the extent to which infant characteristics and early-life exposures impact the resistome can ultimately lead to interventions that decrease the transmission of ARGs and thus the possibility of antibiotic resistant life threatening infections.