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.

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.

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.

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.

The infant gut resistome is associated with E. coli and early-life exposures.

BACKGROUND: 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. 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 were 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. The overall relative abundance of the resistome was strongly correlated with Proteobacteria (Spearman correlation = 78.9%) and specifically Escherichia 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 trend towards a higher risk of harboring unique ARGs [relative risk = 1.12 (95% CI: 0.97-1.29)] as well as 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. 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 risk of antibiotic resistant infections.

Retail store customer flow and COVID-19 transmission.

We examine how operational changes in customer flows in retail stores affect the rate of COVID-19 transmission. We combine a model of customer movement with two models of disease transmission: direct exposure when two customers are in close proximity and wake exposure when one customer is in the airflow behind another customer. We find that the effectiveness of some operational interventions is sensitive to the primary mode of transmission. Restricting customer flow to one-way movement is highly effective if direct exposure is the dominant mode of transmission. In particular, the rate of direct transmission under full compliance with one-way movement is less than one-third the rate under two-way movement. Directing customers to follow one-way flow, however, is not effective if wake exposure dominates. We find that two other interventions-reducing the speed variance of customers and throughput control-can be effective whether direct or wake transmission is dominant. We also examine the trade-off between customer throughput and the risk of infection to customers, and we show how the optimal throughput rate drops rapidly as the population prevalence rises.

Maternal serum perfluoroalkyl substance mixtures and thyroid hormone concentrations in maternal and cord sera: The HOME Study.

BACKGROUND: Per- and polyfluoroalkyl substances (PFAS) are ubiquitous. Previous studies have found associations between PFAS and thyroid hormones in maternal and cord sera, but the results are inconsistent. To further address this research question, we used mixture modeling to assess the associations with individual PFAS, interactions among PFAS chemicals, and the overall mixture. METHODS: We collected data through the Health Outcomes and Measures of the Environment (HOME) Study, a prospective cohort study that between 2003 and 2006 enrolled 468 pregnant women and their children in the greater Cincinnati, Ohio region. We assessed the associations of maternal serum PFAS concentrations measured during pregnancy with maternal (n = 185) and cord (n = 256) sera thyroid stimulating hormone (TSH), total thyroxine (TT4), total triiodothyronine (TT3), free thyroxine (FT4), and free triiodothyronine (FT3) using two mixture modeling approaches (Bayesian kernel machine regression (BKMR) and quantile g-computation) and multivariable linear regression. Additional models considered thyroid autoantibodies, other non-PFAS chemicals, and iodine deficiency as potential confounders or effect measure modifiers. RESULTS: PFAS, considered individually or as mixtures, were generally not associated with any thyroid hormones. A doubling of perfluorooctanesulfonic acid (PFOS) had a positive association with cord serum TSH in BKMR models but the 95% Credible Interval included the null (ß = 0.09; 95% CrI: -0.08, 0.27). Using BKMR and multivariable models, we found that among children born to mothers with higher thyroid peroxidase antibody (TPOAb), perfluorooctanoic acid (PFOA), PFOS, and perfluorohexanesulfonic acid (PFHxS) were associated with decreased cord FT4 suggesting modification by maternal TPOAb status. CONCLUSIONS: These findings suggest that maternal serum PFAS concentrations measured in the second trimester of pregnancy are not strongly associated with thyroid hormones in maternal and cord sera. Further analyses using robust mixture models in other cohorts are required to corroborate these findings.