Risk of prostate cancer with increasing years of night shift work: A two-stage dose-response meta-analysis with duration of night shift work as exposure dose.

Introduction: Night shift work could be a modifiable risk factor for prostate cancer. However, the epidemiological evidence is inconsistent. To summarize the existing evidence on this topic, we conducted a two-stage dose-response meta-analysis. Methods: Medical librarians searched PubMed, EMBASE, and the Cochrane Library on December 30, 2022. Seven criteria were used to determine the inclusion of each study in the present analysis. Results: Eleven cohort studies (12 cohort reports; total cases: 9366; and total person-years: 88,238,009) and seven case-control studies (seven case-control reports; total cases: 5593; and total controls: 6876) were included. This study estimated that the risk of prostate cancer increased by 1, 12, 24, and 39% after 1, 10, 20, and 30 years of night shift work exposure, respectively, according to four cohort and five case-control studies. Discussion: Seven inclusion criteria were used to determine which studies were eligible for this analysis. Risk ratios from cohort studies and odds ratios from case-control studies were analyzed separately. However, all odds ratios from the case-control studies were excluded because of a high likelihood of publication bias. Moreover, exposure, measured in years of night shift work, was defined based on the information provided by individual studies. Finally, we utilized a recently reported two-stage dose-response meta-analysis method. This study provides evidence that night shift work contributes to the risk of prostate cancer in a dose-dependent manner.

Air pollution exposure may impact the composition of human milk oligosaccharides.

Human milk oligosaccharides (HMOs) impact neonate immunity and health outcomes. However, the environmental factors influencing HMO composition remain understudied. This study examined the associations between ambient air pollutant (AAP) exposure and HMOs at 1-month postpartum. Human milk samples were collected at 1-month postpartum (n = 185). AAP (PM2.5, PM10, NO2) exposure included the 9-month pregnancy period through 1-month postpartum. Associations between AAP with (1) HMO diversity, (2) the sum of sialylated and fucosylated HMOs, (3) 6 a priori HMOs linked with infant health, and (4) all HMOs were examined using multivariable linear regression and principal component analysis (PCA). Exposure to AAP was associated with lower HMO diversity. PM2.5 and PM10 exposure was positively associated with the HMO 3-fucosyllactose (3FL); PM2.5 exposure was positively associated with the sum of total HMOs, sum of fucosylated HMOs, and the HMO 2'-fucosyllactose (2'FL). PCA indicated the PM2.5, PM10, and NO2 exposures were associated with HMO profiles. Individual models indicated that AAP exposure was associated with five additional HMOs (LNFP I, LNFP II, DFLNT, LNH). This is the first study to demonstrate associations between AAP and breast milk HMOs. Future longitudinal studies will help determine the long-term impact of AAP on human milk composition.

Limitations of BMI z scores for assessing weight change: A clinical tool versus individual risk.

Although pediatric growth curves provide clinical utility, using these metrics for within-person change over time can be misleading. As research is focused on understanding cardiometabolic consequences of weight gain, it is important to use precise metrics to analyze these longitudinal research questions. Despite several foundational recommendations to limit the use of reference pediatric growth curves (e.g., BMI z scores) for within-person longitudinal research, it has evolved into the "gold standard" for using growth curves for pediatric weight gain analyses. Therefore, the objective of this paper is to discuss (A) the methodology used to create reference growth curves; (B) the appropriate use of reference pediatric BMI growth curves within the context of cross-sectional and longitudinal analyses in research; and (C) how to select metrics based on desired evaluations. Careful consideration using standardized references scores is essential when assessing obesity-related questions and comorbid risk over time in pediatric populations.

Associations between Dietary Sugar and Fiber with Infant Gut Microbiome Colonization at 6 Mo of Age.

BACKGROUND: The taxonomic composition of the gut microbiome undergoes rapid development during the first 2-3 y of life. Poor diet during complementary feeding has been associated with alterations in infant growth and compromised bone, immune system, and neurodevelopment, but how it may affect gut microbial composition is unknown. OBJECTIVES: This cross-sectional study aimed to examine the associations between early-life nutrition and the developing infant gut microbiota at 6 mo of age. METHODS: Latino mother-infant pairs from the Mother's Milk Study (n = 105) were included. Infant gut microbiota and dietary intake were analyzed at 6 mo of age using 16S ribosomal RNA amplicon sequencing and 24-h dietary recalls, respectively. Poisson generalized linear regression analysis was performed to examine associations between dietary nutrients and microbial community abundance while adjusting for infants' mode of delivery, antibiotics, infant feeding type, time of introduction of solid foods, energy intake, and body weight. A P value of <0.05 was used to determine the statistical significance in the study. RESULTS: Infants with higher consumption of total sugar exhibited a lower relative abundance of the genera Bacteroides (ß = -0.01; 95% CI: -0.02, -0.00; P = 0.03) and genus Clostridium belonging to the Lachnospiraceae family (ß = -0.02; 95% CI: -0.03, -0.00; P = 0.01). In addition, a higher intake of free sugar (which excludes sugar from milk, dairy, and whole fruit) was associated with several bacteria at the genus level, including Parabacteroides genus (ß = 0.03; 95% CI: 0.01, 0.05; P = 0.001). Total insoluble fiber intake was associated with favorable bacteria at the genus level such as Faecalibacterium (ß = 0.28; 95% CI: 0.03, 0.52; P = 0.02) and Coprococcus (ß = 0.28; 95% CI: 0.02, 0.52; P = 0.03). CONCLUSION: These findings demonstrate that early-life dietary intake at 6 mo impacts the developing gut microbiome associated with the presence of both unfavorable gut microbes and dietary fiber-associated commensal microbes.

Associations between metals and metabolomic profiles related to diabetes among adults in a rural region.

INTRODUCTION: Exposure to metals is associated with increased risk of type 2 diabetes (T2D). Potential mechanisms for metals-T2D associations involve biological processes including oxidative stress and disruption of insulin-regulated glucose uptake. In this study, we assessed whether associations between metal exposure and metabolite profiles relate to biological pathways linked to T2D. MATERIALS AND METHODS: We used data from 29 adults rural Colorado residents enrolled in the San Luis Valley Diabetes Study. Urinary concentrations of arsenic, cadmium, cobalt, lead, manganese, and tungsten were measured. Metabolic effects were evaluated using untargeted metabolic profiling, which included 61,851 metabolite signals detected in serum. We evaluated cross-sectional associations between metals and metabolites present in at least 50% of samples. Primary analyses adjusted urinary heavy metal concentrations for creatinine. Metabolite outcomes associated with each metal exposure were evaluated using pathway enrichment to investigate potential mechanisms underlying the relationship between metals and T2D. RESULTS: Participants had a mean age of 58.5 years (standard deviation = 9.2), 48.3% were female, 48.3% identified as Hispanic/Latino, 13.8% were current smokers, and 65.5% had T2D. Of the detected metabolites, 455 were associated with at least one metal, including 42 associated with arsenic, 22 with cadmium, 10 with cobalt, 313 with lead, 66 with manganese, and two with tungsten. The metabolic features were linked to 24 pathways including linoleate metabolism, butanoate metabolism, and arginine and proline metabolism. Several of these pathways have been previously associated with T2D, and our results were similar when including only participants with T2D. CONCLUSIONS: Our results support the hypothesis that metals exposure may be associated with biological processes related to T2D, including amino acid, co-enzyme, and sugar and fatty acid metabolism. Insight into biological pathways could influence interventions to prevent adverse health outcomes due to metal exposure.

Associations of gut microbiome richness and diversity with objective and subjective sleep measures in a population sample.

STUDY OBJECTIVES: Alterations in gut microbiota composition have been associated with several conditions, and there is emerging evidence that sleep quantity and quality are associated with the composition of the gut microbiome. Therefore, this study aimed to assess the associations between several measures of sleep and the gut microbiome in a large, population-based sample. METHODS: Data were collected from participants in the Survey of the Health of Wisconsin from 2016 to 2017 (N = 720). Alpha diversity was estimated using Chao1 richness, Shannon's diversity, and Inverse Simpson's diversity. Beta diversity was estimated using Bray-Curtis dissimilarity. Models for each of the alpha-diversity outcomes were calculated using linear mixed effects models. Permutational multivariate analysis of variance tests were performed to test whether gut microbiome composition differed by sleep measures. Negative binomial models were used to assess whether sleep measures were associated with individual taxa relative abundance. RESULTS: Participants were a mean (SD) age of 55 (16) years and 58% were female. The sample was 83% non-Hispanic white, 10.6% non-Hispanic black, and 3.5% Hispanic. Greater actigraphy-measured night-to-night sleep duration variability, wake-after-sleep onset, lower sleep efficiency, and worse self-reported sleep quality were associated with lower microbiome richness and diversity. Sleep variables were associated with beta-diversity, including actigraphy-measured night-to-night sleep duration variability, sleep latency and efficiency, and self-reported sleep quality, sleep apnea, and napping. Relative abundance of several taxa was associated with night-to-night sleep duration variability, average sleep latency and sleep efficiency, and sleep quality. CONCLUSIONS: This study suggests that sleep may be associated with the composition of the gut microbiome. These results contribute to the body of evidence that modifiable health habits can influence the human gut microbiome.

Characterizing alterations in the gut microbiota following postpartum weight change.

IMPORTANCE: Previous research has reported differences in the gut microbiome associated with varying body compositions. More specifically, within populations of mothers, the focus has been on the impact of gestational weight gain. This is the first study to examine postpartum weight change and its association with changes in the gut microbiome, similarly, it is the first to use a Latina cohort to do so. The results support the idea that weight gain may be an important factor in reducing gut microbiome network connectivity, diversity, and changing abundances of specific microbial taxa, all measures thought to impact host health. These results suggest that weight gain dynamically alters mothers' gut microbial communities in the first 6 months postpartum, with comparatively little change in mothers who lost weight; further research is needed to examine the health consequences of such changes.

Human milk EV-miRNAs: a novel biomarker for air pollution exposure during pregnancy.

Exposure to ambient and near-roadway air pollution during pregnancy has been linked with several adverse health outcomes for pregnant women and their babies. Emerging research indicates that microRNA (miRNA) expression can be altered by exposure to air pollutants in a variety of tissues. Additionally, miRNAs from breast tissue and circulating miRNAs have previously been proposed as a biomarker for breast cancer diagnosis and prognosis. Therefore, this study sought to evaluate the associations between pregnancy exposures to ambient (PM10, PM2.5, NO2, O3) and near-roadway air pollution (total NOx, freeway NOx, non-freeway NOx) with breast milk extracellular vesicle miRNA (EV-miRNA), measured at 1-month postpartum, in a cohort of 108 Latina women living in Southern California. We found that PM10 exposure during pregnancy was positively associated with hsa-miR-200c-3p, hsa-miR-200b-3p, and hsa-let-7c-5p, and was negatively associated with hsa-miR-378d. We also found that pregnancy PM2.5 exposure was positively associated with hsa-miR-200c-3p and hsa-miR-200b-3p. First and second trimester exposure to PM10 and PM2.5 was associated with several EV-miRNAs with putative messenger RNA targets related to cancer. This study provides preliminary evidence that air pollution exposure during pregnancy is associated with human milk EV-miRNA expression.

Exposure to ambient air pollutants, serum miRNA networks, lipid metabolism, and non-alcoholic fatty liver disease in young adults.

BACKGROUND AND AIM: Ambient air pollution (AAP) exposure has been associated with altered blood lipids and liver fat in young adults. MicroRNAs regulate gene expression and may mediate these relationships. This work investigated associations between AAP exposure, serum microRNA networks, lipid profiles, and non-alcoholic fatty liver disease (NAFLD) risk in young adults. METHODS: Participants were 170 young adults (17-22 years) from the Meta-AIR cohort of the Children's Health Study (CHS). Residential AAP exposure (PM2.5, PM10, NO2, 8-hour maximum O3, redox-weighted oxidative capacity [Oxwt]) was spatially interpolated from monitoring stations via inverse-distance-squared weighting. Fasting serum lipids were assayed. Liver fat was imaged by MRI and NAFLD was defined by ≥ 5.5% hepatic fat fraction. Serum microRNAs were measured via NanoString and microRNA networks were constructed by weighted gene correlation network analysis. The first principal component of each network represented its expression profile. Multivariable mixed effects regression models adjusted for sociodemographic, behavioral, and clinical covariates; baseline CHS town code was a random effect. Effects estimates are scaled to one standard deviation of exposure. Mediation analysis explored microRNA profiles as potential mediators of exposure-outcome associations. DIANA-mirPATH identified overrepresented gene pathways targeted by miRNA networks. RESULTS: Prior-month Oxwt was associated with NAFLD (OR=3.45; p = 0.003) and inversely associated with microRNA Network A (ß = -0.016; p = 0.026). Prior-year NO2 was associated with non-HDL-cholesterol (ß = 7.13; p = 0.01) and inversely associated with miRNA Network A (ß = -0.019; p = 0.022). Network A expression was inversely associated with NAFLD (OR=0.35; p = 0.010) and non-HDL-C (ß = -6.94 mg/dL; p = 0.035). Network A members miR-199a/b-3p and miR-130a, which both target fatty acid synthase, mediated 21% of the association between prior-month Oxwt exposure with NAFLD (p = 0.048) and 23.3% of the association between prior-year NO2 exposure and non-HDL-cholesterol (p = 0.026), respectively. CONCLUSIONS: Exposure to AAP may contribute to adverse lipid profiles and NAFLD risk among young adults via altered expression of microRNA profiles.

Effects of Dietary Sugar Reduction on Biomarkers of Cardiometabolic Health in Latino Youth: Secondary Analyses from a Randomized Controlled Trial.

Pediatric obesity and cardiometabolic disease disproportionately impact minority communities. Sugar reduction is a promising prevention strategy with consistent cross-sectional associations of increased sugar consumption with unfavorable biomarkers of cardiometabolic disease. Few trials have tested the efficacy of pediatric sugar reduction interventions. Therefore, in a parallel-design trial, we randomized Latino youth with obesity (BMI ≥ 95th percentile) [n = 105; 14.8 years] to control (standard diet advice) or sugar reduction (clinical intervention with a goal of ≤10% of calories from free sugar) for 12-weeks. Outcomes included changes in glucose tolerance and its determinants as assessed by a 2-h frequently sample oral glucose tolerance test, fasting serum lipid profile (total cholesterol, HDL, LDL, triglycerides, cholesterol:HDL), and inflammatory markers (CRP, IL-6, TNF-α). Free sugar intake decreased in the intervention group compared to the control group [11.5% to 7.3% vs. 13.9% to 10.7% (% Energy), respectively, p = 0.02], but there were no effects on any outcome of interest (pall > 0.07). However, an exploratory analysis revealed that sugar reduction, independent of randomization, was associated with an improved Oral-disposition index (p < 0.001), triglycerides (p = 0.049), and TNF-α (p = 0.02). Dietary sugar reduction may have the potential to reduce chronic disease risks through improvements in beta-cell function, serum triglycerides, and inflammatory markers in Latino adolescents with obesity.

Influence of technical and maternal-infant factors on the measurement and expression of extracellular miRNA in human milk.

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.

The effect of low-abundance OTU filtering methods on the reliability and variability of microbial composition assessed by 16S rRNA amplicon sequencing.

PCR amplicon sequencing may lead to detection of spurious operational taxonomic units (OTUs), inflating estimates of gut microbial diversity. There is no consensus in the analytical approach as to what filtering methods should be applied to remove low-abundance OTUs; moreover, few studies have investigated the reliability of OTU detection within replicates. Here, we investigated the reliability of OTU detection (% agreement in detecting OTU in triplicates) and accuracy of their quantification (assessed by coefficient of variation (CV)) in human stool specimens. Stool samples were collected from 12 participants 22-55 years old. We applied several methods for filtering low-abundance OTUs and determined their impact on alpha-diversity and beta-diversity metrics. The reliability of OTU detection without any filtering was only 44.1% (SE=0.9) but increased after filtering low-abundance OTUs. After filtering OTUs with <0.1% abundance in the dataset, the reliability increased to 87.7% (SE=0.6) but at the expense of removing 6.97% reads from the dataset. When filtering was based on individual sample, the reliability increased to 73.1% after filtering OTUs with <10 copies while removing only 1.12% of reads. High abundance OTUs (>10 copies in sample) had lower CV, indicating better accuracy of quantification than low-abundance OTUs. Excluding very low-abundance OTUs had a significant impact on alpha-diversity metrics sensitive to the presence of rare species (observed OTUs, Chao1) but had little impact on relative abundance of major phyla and families and alpha-diversity metrics accounting for both richness and evenness (Shannon, Inverse Simpson). To increase the reliability of microbial composition, we advise removing OTUs with <10 copies in individual samples, particularly in studies where only one subsample per specimen is available for analysis.

Longitudinal profiles of the fecal metabolome during the first 2 years of life.

During the first 2 years of life, the infant gut microbiome is rapidly developing, and gut bacteria may impact host health through the production of metabolites that can have systemic effects. Thus, the fecal metabolome represents a functional readout of gut bacteria. Despite the important role that fecal metabolites may play in infant health, the development of the infant fecal metabolome has not yet been thoroughly characterized using frequent, repeated sampling during the first 2 years of life. Here, we described the development of the fecal metabolome in a cohort of 101 Latino infants with data collected at 1-, 6-, 12-, 18-, and 24-months of age. We showed that the fecal metabolome is highly conserved across time and highly personalized, with metabolic profiles being largely driven by intra-individual variability. Finally, we also identified several novel metabolites and metabolic pathways that changed significantly with infant age, such as valerobetaine and amino acid metabolism, among others.

The impact of social determinants of health on obesity and diabetes disparities among Latino communities in Southern California.

BACKGROUND: Social determinants of health (SDoH) describe the complex network of circumstances that impact an individual before birth and across the lifespan. SDoH contextualize factors in a community that are associated with chronic disease risk and certain health disparities. The main objective of this study was to explore the impact of SDoH on the prevalence of obesity and diabetes, and whether these factors explain disparities in these health outcomes among Latinos in Southern California. METHODS: We utilized three composite indices that encompass different SDoH: the Healthy Places Index (HPI), Social Vulnerability Index (SVI), and CalEnviroScreen (CES). Univariate linear regression models explored the associations between index scores with adult obesity, adult diabetes, and childhood obesity. RESULTS: Communities with lower HPI scores were associated with higher prevalence of metabolic disease and a greater proportion of Latino residents. Cities in the lowest decile of HPI scores had 71% of the population identifying as Latino compared to 12% in the highest decile. HPI scores explained 61% of the variability in adult obesity (p < 0.001), 41% of the variability in childhood obesity (p < 0.001), and 47% of the variability in adult diabetes (p < 0.001). Similar results were observed when examining SVI and CES with these health outcomes. CONCLUSIONS: These results suggest that Latinos in Southern California live in communities with adverse SDoH and face a greater burden of adult obesity, diabetes, and childhood obesity.

The potential role of early life feeding patterns in shaping the infant fecal metabolome: implications for neurodevelopmental outcomes.

Infant fecal metabolomics can provide valuable insights into the associations of nutrition, dietary patterns, and health outcomes in early life. Breastmilk is typically classified as the best source of nutrition for nearly all infants. However, exclusive breastfeeding may not always be possible for all infants. This study aimed to characterize associations between levels of mixed breastfeeding and formula feeding, along with solid food consumption and the infant fecal metabolome at 1- and 6-months of age. As a secondary aim, we examined how feeding-associated metabolites may be associated with early life neurodevelopmental outcomes. Fecal samples were collected at 1- and 6-months, and metabolic features were assessed via untargeted liquid chromatography/high-resolution mass spectrometry. Feeding groups were defined at 1-month as 1) exclusively breastfed, 2) breastfed >50% of feedings, or 3) formula fed ≥50% of feedings. Six-month groups were defined as majority breastmilk (>50%) or majority formula fed (≥50%) complemented by solid foods. Neurodevelopmental outcomes were assessed using the Bayley Scales of Infant Development at 2 years. Changes in the infant fecal metabolome were associated with feeding patterns at 1- and 6-months. Feeding patterns were associated with the intensities of a total of 57 fecal metabolites at 1-month and 25 metabolites at 6-months, which were either associated with increased breastmilk or increased formula feeding. Most breastmilk-associated metabolites, which are involved in lipid metabolism and cellular processes like cell signaling, were associated with higher neurodevelopmental scores, while formula-associated metabolites were associated with lower neurodevelopmental scores. These findings offer preliminary evidence that feeding patterns are associated with altered infant fecal metabolomes, which may be associated with cognitive development later in life.

Assessing the relationship between physical activity and the gut microbiome in a large, population-based sample of Wisconsin adults.

The gut microbiome is an important factor in human health and disease. While preliminary studies have found some evidence that physical activity is associated with gut microbiome richness, diversity, and composition, this relationship is not fully understood and has not been previously characterized in a large, population-based cohort. In this study, we estimated the association between several measures of physical activity and the gut microbiota in a cohort of 720 Wisconsin residents. Our sample had a mean age of 55 years (range: 18, 94), was 42% male, and 83% of participants self-identified as White. Gut microbial composition was assessed using gene sequencing of the V3-V4 region of 16S rRNA extracted from stool. We found that an increase of one standard deviation in weekly minutes spent in active transportation was associated with an increase in alpha diversity, particularly in Chao1's richness (7.57, 95% CI: 2.55, 12.59) and Shannon's diversity (0.04, 95% CI: 0.0008, 0.09). We identified interactions in the association between Inverse Simpson's diversity and physical activity, wherein active transportation for individuals living in a rural environment was associated with additional increases in diversity (4.69, 95% CI: 1.64, 7.73). We also conducted several permutational ANOVAs (PERMANOVA) and negative binomial regression analyses to estimate the relationship between physical activity and microbiome composition. We found that being physically active and increased physical activity time were associated with increased abundance of bacteria in the family Erysipelotrichaceae. Active transportation was associated with increased abundance of bacteria in the genus Phascolarctobacterium, and decreased abundance of Clostridium. Minutes in active transportation was associated with a decreased abundance of the family Clostridiaceae.

Postnatal exposure to ambient air pollutants is associated with the composition of the infant gut microbiota at 6-months of age.

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 (PM2.5 and PM10) and nitrogen dioxide (NO2) 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, PM10 exposure was positively associated with Dialister, Dorea, Acinetobacter, and Campylobacter while PM2.5 was positively associated with Actinomyces. Further, exposure to PM10 and PM2.5 was inversely associated with Alistipes and NO2 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.

Assessing the impact of storage time on the stability of stool microbiota richness, diversity, and composition.

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.

Early life gut microbiota is associated with rapid infant growth in Hispanics from Southern California.

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 (pall<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 (R2 = 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.