Animal models support a role for the gut microbiota in the development of hypertension. There has been a lack of epidemiological cohort studies to confirm these findings in human populations. We examined cross-sectional associations between measures of gut microbial diversity and taxonomic composition and blood pressure (BP) in 529 participants of the biracial (black and white) CARDIA study (Coronary Artery Risk Development in Young Adults). We sequenced V3-V4 regions of the 16S ribosomal RNA marker gene using DNA extracted from stool samples collected at CARDIA’s Year 30 follow-up examination (2015–2016; aged 48–60 years). We quantified associations between BP (hypertension [defined as systolic BP ≥140 mm Hg or diastolic BP ≥90 mm Hg or antihypertension medication use] and systolic BP) and within and between-person diversity measures. We conducted genera-specific multivariable-adjusted regression analysis, accounting for multiple comparisons using the false discovery rate. Hypertension and systolic BP were inversely associated with measures of α-diversity, including richness and the Shannon Diversity Index, and were distinguished with respect to principal coordinates based on a similarity matrix of genera abundance. Several specific genera were significantly associated with hypertension and systolic BP, though results were attenuated with adjustment for body mass index. Our findings support associations between within-person and between-person gut microbial community diversity and taxonomic composition and BP in a diverse population-based cohort of middle-aged adults. Future study is needed to define functional pathways that underlie observed associations and identify specific microbial targets for intervention.

IMPORTANCEAnimal experiments and small clinical studies support a role for the gut microbiota in cognitive functioning. Few studies have investigated gut microbiota and cognition in large community samples. OBJECTIVE To examine associations of gut microbial composition with measures of cognition in an established population-based study of middle-aged adults. DESIGN, SETTING, AND PARTICIPANTS This cross-sectional study analyzed data from the prospective Coronary Artery Risk Development in Young Adults (CARDIA) cohort in 4 US metropolitan centers between 2015 and 2016. Data were analyzed in 2019 and 2020.EXPOSURES Stool DNA were sequenced, and the following gut microbial measures were gathered:(1) β-diversity (between-person) derived with multivariate principal coordinates analysis; (2) α-diversity (within-person), defined as richness (genera count) and the Shannon index (integrative measure of genera richness and evenness); and (3) taxonomy (107 genera, after filtering). MAIN OUTCOMES AND MEASURESCognitive status was assessed using 6 clinic-administered cognitive tests: Montreal Cognitive Assessment (MoCA), Digit Symbol Substitution Test (DSST), Rey-Auditory Verbal Learning Test (RAVLT), Stroop, category fluency, and letter fluency. A global score measure derived using principal components analysis was also assessed; the first principal component explained 56% of variability.RESULTS Microbiome data were available on 597 CARDIA participants; mean (SD) age was 55.2 (3.5) years, 268 participants (44.7%) were men, and 270 (45.2%) were Black. In multivariable-adjusted principal coordinates analysis, permutational multivariate analysis of variance tests for β-diversity were statistically significant for all cognition measures (principal component analysis, P = .001; MoCA, P = .001; DSST, P = .001; RAVLT, P = .001; Stroop, P = .007; category fluency, P = .001) with the exception of letter fluency (P = .07). After adjusting for sociodemographic variables (age, race, sex, education), health behaviors (physical activity, diet, smoking, medication use), and clinical covariates (body mass index, diabetes, hypertension), Barnesiella was positively associated with the first principal component (β, 0.16; 95% CI, 0.08-0.24), DSST (β, 1.18; 95% CI, 0.35-2.00), and category fluency (β, 0.59; 95% CI, 0.31-0.87); Lachnospiraceae FCS020 group was positively associated with DSST (β, 2.67; 95% CI, 1.10-4.23), and Sutterella was negatively associated with MoCA (β, −0.27; 95% CI, −0.44 to −0.11). CONCLUSIONS AND RELEVANCEIn this cross-sectional study, microbial community composition, based on β-diversity, was associated with all cognitive measures in multivariable-adjusted analysis.These data contribute to a growing body of literature suggesting that the gut microbiota may be associated with cognitive aging, but must be replicated in larger samples and further researched to identify relevant pathways.

Batch effects in microbiome data arise from differential processing of specimens and can lead to spurious findings and obscure true signals. Strategies designed for genomic data to mitigate batch effects usually fail to address the zero-inflated and over-dispersed microbiome data. Most strategies tailored for microbiome data are restricted to association testing or specialized study designs, failing to allow other analytic goals or general designs. Here, we develop the Conditional Quantile Regression (ConQuR) approach to remove microbiome batch effects using a two-part quantile regression model. ConQuR is a comprehensive method that accommodates the complex distributions of microbial read counts by non-parametric modeling, and it generates batchremoved zero-inflated read counts that can be used in and benefit usual subsequent analyses. We apply ConQuR to simulated and real microbiome datasets and demonstrate its advantages in removing batch effects while preserving the signals of interest.Advances in 16S rRNA 1 and full metagenome 2 sequencing technologies have enabled large-scale human microbiome profiling studies involving hundreds to thousands of individuals. The large sample sizes of these studies and the rich availability of metadata promise a comprehensive understanding of the role of microorganisms in health and disease. These studies have already revealed associations between bacterial taxa and both diseases and exposures, such as obesity 3 , type 2 diabetes 4 , bacterial vaginosis 5 , antibiotics 6 , and environmental pollutants 7 . However, although large-scale studies facilitate more robust and powerful analyses, they are often subject to serious batch effects-systematic variation in the data originating from differential handling and processing of specimens 8 . Many large studies include samples collected across times or locations and processed in different runs. In a more extreme situation, several studies may be pooled together for integrative analysis, with inter-study heterogeneity introducing even more severe variation. These batch effects pose serious challenges to analysis and can lead to excessive false positive discoveries, obscure true associations between microbes and clinical variables, and hinder prediction modeling and biomarker development. Unfortunately, despite the importance of batch effects,

Previous studies in this and other laboratories have demonstrated that ebselen (EB-1), an organoselenium compound, spares cells from mechlorethamine (HN2) toxicity in vitro. In the present study, the hypothesis that EB-1 will reduce dermal toxicity of HN2 in vivo is put forward and found to have merit. Using the mouse ear vesicant model (MEVM), HN2, applied topically, showed a dose-dependent effect upon ear swelling and thickness 24 h after treatment; whereas tissue injury consistent with vesication was observed at the higher test doses of HN2 (≥ 0.250 µmol per ear). To examine HN2 countermeasure activity using the MEVM, either hydrocortisone (HC), as a positive control, or EB-1, the test countermeasure, was administered as three topical treatments 15 min, 4 and 8 h after HN2 exposure. Using this approach, both HC and EB-1 were found to reduce tissue swelling associated with HN2 toxicity 24 h after exposure to the vesicant. Taken together, these data demonstrate for the first time the effectiveness of EB-1 as a vesicant countermeasure in a relevant in vivo model.