We aimed to determine if the newborn gut microbiota is an underlying determinant of early life growth trajectories. 132 Hispanic infants were recruited at 1-month postpartum. The infant gut microbiome was characterized using 16S rRNA amplicon sequencing. Rapid infant growth was defined as a weight-for-age z-score (WAZ) change greater than 0.67 between birth and 12-months of age. Measures of infant growth included change in WAZ, weight-for-length z-score (WLZ), and body mass index (BMI) z-scores from birth to 12-months and infant anthropometrics at 12-months (weight, skinfold thickness). Of the 132 infants, 40% had rapid growth in the first year of life. Multiple metrics of alpha-diversity predicted rapid infant growth, including a higher Shannon diversity (OR = 1.83; 95% CI: 1.07–3.29; p = .03), Faith’s phylogenic diversity (OR = 1.41, 95% CI: 1.05–1.94; p = .03), and richness (OR = 1.04, 95% CI: 1.01–1.08; p = .02). Many of these alpha-diversity metrics were also positively associated with increases in WAZ, WLZ, and BMI z-scores from birth to 12-months (p all <0.05). Importantly, we identified subsets of microbial consortia whose abundance were correlated with these same measures of infant growth. We also found that rapid growers were enriched in multiple taxa belonging to genera such as Acinetobacter, Collinsella, Enterococcus, Neisseria , and Parabacteroides . Moreover, measures of the newborn gut microbiota explained up to an additional 5% of the variance in rapid growth beyond known clinical predictors (R 2 = 0.37 vs. 0.32, p < .01). These findings indicate that a more mature gut microbiota, characterized by increased alpha-diversity, at as early as 1-month of age, may influence infant growth trajectories in the first year of life.

Background New technologies like next-generation sequencing have led to a proliferation of studies investigating the role of the gut microbiome in human health, particularly population-based studies that rely upon participant self-collection of samples. However, the impact of methodological differences in sample shipping, storage, and processing are not well-characterized for these types of studies, especially when transit times may exceed 24 h. The aim of this study was to experimentally assess microbiota stability in stool samples stored at 4 °C for durations of 6, 24, 48, 72, and 96 h with no additives to better understand effects of variable shipping times in population-based studies. These data were compared to a baseline sample that was immediately stored at − 80 °C after stool production. Results Compared to the baseline sample, we found that the alpha-diversity metrics Shannon’s and Inverse Simpson’s had excellent intra-class correlations (ICC) for all storage durations. Chao1 richness had good to excellent ICC. We found that the relative abundances of bacteria in the phyla Verrucomicrobia, Actinobacteria, and Proteobacteria had excellent ICC with baseline for all storage durations, while Firmicutes and Bacteroidetes ranged from moderate to good. We interpreted the ICCs as follows: poor: ICC < 0.50, moderate: 0.50 < ICC < 0.75, good: 0.75 < ICC < 0.90, and excellent: ICC > 0.90. Using the Bray–Curtis dissimilarity index, we found that the greatest change in community composition occurred between 0 and 24 h of storage, while community composition remained relatively stable for subsequent storage durations. Samples showed strong clustering by individual, indicating that inter-individual variability was greater than the variability associated with storage time. Conclusions The results of this analysis suggest that several measures of alpha diversity, relative abundance, and overall community composition are robust to storage at 4 °C for up to 96 h. We found that the overall community richness was influenced by storage duration in addition to the relative abundances of sequences within the Firmicutes and Bacteroidetes phyla. Finally, we demonstrate that inter-individual variability in microbiota composition was greater than the variability due to changing storage durations.

Breast milk contains thousands of bioactive compounds including extracellular vesicle microRNAs (EV-miRNAs), which may regulate pathways such as infant immune system development and metabolism. We examined the associations between the expression of EV-miRNAs and laboratory variables (i.e., batch effects, sample characteristics), sequencing quality indicators, and maternal-infant characteristics. The study included 109 Latino mother-infant dyads from the Southern California Mother’s Milk Study. Mothers were age 28.0 ± 5.6 and 23-46 days postpartum. We used principal components analysis to evaluate whether EV-miRNA expression was associated with factors of interest. Then, we used linear models to estimate relationships between these factors and specific EV-miRNA counts and analyzed functional pathways associated with those EV-miRNAs. Finally, we explored which maternal-infant characteristics predicted sequencing quality indicators. Sequencing quality indicators, predominant breastfeeding, and breastfeedings/day were associated with EV-miRNA principal components. Maternal body mass index and breast milk collection timing predicted proportion of unmapped reads. Expression of 2 EV-miRNAs were associated with days postpartum, 23 EV-miRNAs were associated with breast milk collection time, 23 EV-miRNAs were associated with predominant breastfeeding, and 38 EV-miRNAs were associated with breastfeedings/day. These EV-miRNAs were associated with pathways including Hippo signaling pathway and ECM-receptor interaction, among others. This study identifies several important factors that may contribute to breast milk EV-miRNA expression. Future studies should consider these findings in the design and analysis of breast milk miRNA research.

Epidemiological studies in adults have shown that exposure to ambient air pollution (AAP) is associated with the composition of the adult gut microbiome, but these relationships have not been examined in infancy. We aimed to determine if 6-month postnatal AAP exposure was associated with the infant gut microbiota at 6 months of age in a cohort of Latino mother-infant dyads from the Southern California Mother’s Milk Study (n = 103). We estimated particulate matter (PM 2.5 and PM 10 ) and nitrogen dioxide (NO 2 ) exposure from birth to 6-months based on residential address histories. We characterized the infant gut microbiota using 16S rRNA amplicon sequencing at 6-months of age. At 6-months, the gut microbiota was dominated by the phyla Bacteroidetes, Firmicutes, Proteobacteria, and Actinobacteria. Our results show that, after adjusting for important confounders, postnatal AAP exposure was associated with the composition of the gut microbiota. As an example, PM 10 exposure was positively associated with Dialister, Dorea, Acinetobacter , and Campylobacter while PM 2.5 was positively associated with Actinomyces . Further, exposure to PM 10 and PM 2.5 was inversely associated with Alistipes and NO 2 exposure was positively associated with Actinomyces, Enterococcus, Clostridium , and Eubacterium . Several of these taxa have previously been linked with systemic inflammation, including the genera Dialister and Dorea . This study provides the first evidence of significant associations between exposure to AAP and the composition of the infant gut microbiota, which may have important implications for future infant health and development.

Children’s sleep quality and duration are important to overall development, health, and wellbeing. However, measuring children’s sleep is challenging – especially in situations where objective assessment is impractical. This study aimed to assess age and proxy effects in comparing subjective sleep duration with objective measures, in a community-based sample of Wisconsin children (age 6-17 years), recruited from 2014-2017. The sample mean age was 11.4 years (SD: 3.3 years) and was 52% male. We used linear mixed effects models to test for age effects in proxy- and self-report groups separately, and a quasi-experimental regression discontinuity approach to compare subjective sleep duration with objective actigraphy estimates across proxy- and self-report groups. We found evidence of systematic overestimation of sleep duration when using subjective measurements but did not find evidence of age effects in either group. Based on these analyses, we found evidence of differential overestimation by proxy- or self-report condition; proxy-reporters overestimated sleep duration by 2.3 hours (95% CI: 2.2, 2.4), compared to 1.0 hour (95% CI: 0.7, 1.2) for self-reporters. These findings suggest that proxy- versus self-reporting conditions are an important consideration when designing a study, and that it may be beneficial to reduce the age at which children self-report.