Human Microbiome Mixture Analysis Using Weighted Quantile Sum Regression

: Studies of the health effects of the microbiome often measure overall associations by using diversity metrics, and individual taxa associations in separate analyses, but do not consider the correlated relationships between taxa in the microbiome. In this study, we applied random subset weighted quantile sum regression with repeated holdouts (WQSRSRH), a mixture method successfully applied to 'omic data to account for relationships between many predictors, to processed amplicon sequencing data from the Human Microbiome Project. We simulated a binary variable associated with 20 operational taxonomic units (OTUs). WQSRSRH was used to test for the association between the microbiome and the simulated variable, adjusted for sex, and sensitivity and specificity were calculated. The WQSRSRH method was also compared to other standard methods for microbiome analysis. The method was further illustrated using real data from the Growth and Obesity Cohort in Chile to assess the association between the gut microbiome and body mass index. In the analysis with simulated data, WQSRSRH predicted the correct directionality of association between the microbiome and the simulated variable, with an average sensitivity and specificity of 75% and 70%, respectively, in identifying the 20 associated OTUs. WQSRSRH performed better than all other comparison methods. In the illustration analysis of the gut microbiome and obesity, the WQSRSRH analysis identified an inverse association between body mass index and the gut microbe mixture, identifying Bacteroides, Clostridium, Prevotella, and Ruminococcus as important genera in the negative association. The application of WQSRSRH to the microbiome allows for analysis of the mixture effect of all the taxa in the microbiome, while simultaneously identifying the most important to the mixture, and allowing for covariate adjustment. It outperformed other methods when using simulated data, and in analysis with real data found results consistent with other study findings

Human Microbiome Mixture Analysis Using Weighted Quantile Sum Regression

Studies of the health effects of the microbiome often measure overall associations by using diversity metrics, and individual taxa associations in separate analyses, but do not consider the correlated relationships between taxa in the microbiome. In this study, we applied random subset weighted quantile sum regression with repeated holdouts (WQSRSRH), a mixture method successfully applied to ‘omic data to account for relationships between many predictors, to processed amplicon sequencing data from the Human Microbiome Project. We simulated a binary variable associated with 20 operational taxonomic units (OTUs). WQSRSRH was used to test for the association between the microbiome and the simulated variable, adjusted for sex, and sensitivity and specificity were calculated. The WQSRSRH method was also compared to other standard methods for microbiome analysis. The method was further illustrated using real data from the Growth and Obesity Cohort in Chile to assess the association between the gut microbiome and body mass index. In the analysis with simulated data, WQSRSRH predicted the correct directionality of association between the microbiome and the simulated variable, with an average sensitivity and specificity of 75% and 70%, respectively, in identifying the 20 associated OTUs. WQSRSRH performed better than all other comparison methods. In the illustration analysis of the gut microbiome and obesity, the WQSRSRH analysis identified an inverse association between body mass index and the gut microbe mixture, identifying Bacteroides, Clostridium, Prevotella, and Ruminococcus as important genera in the negative association. The application of WQSRSRH to the microbiome allows for analysis of the mixture effect of all the taxa in the microbiome, while simultaneously identifying the most important to the mixture, and allowing for covariate adjustment. It outperformed other methods when using simulated data, and in analysis with real data found results consistent with other study findings

Prenatal phenol and paraben exposures in relation to child neurodevelopment including autism spectrum disorders in the MARBLES study.

BackgroundEnvironmental phenols and parabens are endocrine disrupting chemicals (EDCs) with the potential to affect child neurodevelopment including autism spectrum disorders (ASD). Our aim was to assess whether exposure to environmental phenols and parabens during pregnancy was associated with an increased risk of clinical ASD or other nontypical development (non-TD).MethodsThis study included mother-child pairs (N = 207) from the Markers of Autism Risks in Babies - Learning Early Signs (MARBLES) Cohort Study with urinary phenol and paraben metabolites analyzed using liquid chromatography-tandem mass spectrometry (LC-MS/MS) from repeated pregnancy urine samples. Because family recurrence risks in siblings are about 20%, MARBLES enrolls pregnant women who already had a child with ASD. Children were clinically assessed at 3 years of age and classified into 3 outcome categories: ASD, non-TD, or typically developing (TD). Single analyte analyses were conducted with trinomial logistic regression and weighted quantile sum (WQS) regression was used to test for mixture effects.ResultsRegression models were adjusted for pre-pregnancy body mass index, prenatal vitamin use (yes/no), homeowner status (yes/no), birth year, and child's sex. In single chemical analyses phenol exposures were not significantly associated with child's diagnosis. Mixture analyses using trinomial WQS regression showed a significantly increased risk of non-TD compared to TD (OR = 1.58, 95% CI: 1.04, 2.04) with overall greater prenatal phenol and paraben metabolites mixture. Results for ASD also showed an increased risk, but it was not significant.DiscussionThis is the first study to provide evidence that pregnancy environmental phenol exposures may increase the risk for non-TD in a high-risk population

Tobacco smoke exposure, the lower airways microbiome and outcomes of ventilated children.

BackgroundTobacco smoke exposure increases the risk and severity of lower respiratory tract infections in children, yet the mechanisms remain unclear. We hypothesized that tobacco smoke exposure would modify the lower airway microbiome.MethodsSecondary analysis of a multicenter cohort of 362 children between ages 31 days and 18 years mechanically ventilated for >72 h. Tracheal aspirates from 298 patients, collected within 24 h of intubation, were evaluated via 16 S ribosomal RNA sequencing. Smoke exposure was determined by creatinine corrected urine cotinine levels ≥30 µg/g.ResultsPatients had a median age of 16 (IQR 568) months. The most common admission diagnosis was lower respiratory tract infection (53%). Seventy-four (20%) patients were smoke exposed and exhibited decreased richness and Shannon diversity. Smoke exposed children had higher relative abundances of Serratia spp., Moraxella spp., Haemophilus spp., and Staphylococcus aureus. Differences were most notable in patients with bacterial and viral respiratory infections. There were no differences in development of acute respiratory distress syndrome, days of mechanical ventilation, ventilator free days at 28 days, length of stay, or mortality.ConclusionAmong critically ill children requiring prolonged mechanical ventilation, tobacco smoke exposure is associated with decreased richness and Shannon diversity and change in microbial communities.ImpactTobacco smoke exposure is associated with changes in the lower airways microbiome but is not associated with clinical outcomes among critically ill pediatric patients requiring prolonged mechanical ventilation. This study is among the first to evaluate the impact of tobacco smoke exposure on the lower airway microbiome in children. This research helps elucidate the relationship between tobacco smoke exposure and the lower airway microbiome and may provide a possible mechanism by which tobacco smoke exposure increases the risk for poor outcomes in children