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Background: In humans, methane (CH4) is exclusively produced by the intestinal microbiota and has been implicated in several conditions including cardiovascular disease. After microbial production of CH4 in the gut, it steadily crosses into the systemic circulation and reaches the lungs where it can be detected in the exhaled breath, as a surrogate measure for intestinal CH4 production. Recent reports have shown an association between CH4 and vagal dysfunction as well as the inhibition of CH4 activity on ileal contractions with atropine, suggesting its action on the parasympathetic nervous system. Given these findings, we hypothesized that CH4 may be affecting resting heart rate (HR) based on the potential effect of CH4 on the vagus nerve. Objectives: Given its possible role in the parasympathetic nervous system, we aimed to study the relationship between breath CH4 and resting HR in humans. Additionally, we performed a longitudinal study analyzing the change in HR and its association with breath CH4 over time. Methods: First, we reviewed 1,126 subjects and compared HR in subjects with detectable and undetectable breath CH4. Second, we performed a post hoc analysis of a randomized control trial to compare the change in HR for those who had an increase in breath CH4 versus those that had a decrease in breath CH4 over 14 weeks. Last, we assessed whether a larger decrease in CH4 is associated with a larger increase in HR over time. Results: In the retrospective cohort, subjects with detectable CH4 had a lower HR compared to those with undetectable CH4 (73.0 ± 0.83 vs. 76.0 ± 0.44 beats/min, p = 0.01). In the post hoc analysis, a decrease in CH4 over time was associated with an increase in HR (median ∆ = 6.5 ± 8.32 beats/min, p = 0.0006). Last, we demonstrated a biological gradient whereby a larger drop in CH4 was associated with a greater increase in HR (R = −0.31, p = 0.03). Conclusion: Our findings suggest a potential role for the microbiome (and specifically CH4 from methanogens) to regulate HR. Considering these findings, mechanistic studies are warranted to further investigate this potential novel microbiome-neurocardiac axis.

Irritable bowel syndrome (IBS) includes diarrhea-predominant (IBS-D) and constipation-predominant (IBS-C) subtypes. We combined breath testing and stool microbiome sequencing to identify potential microbial drivers of IBS subtypes.IBS-C and IBS-D subjects from 2 randomized controlled trials (NCT03763175 and NCT04557215) were included. Baseline breath carbon dioxide, hydrogen (H 2 ), methane (CH 4 ), and hydrogen sulfide (H 2 S) levels were measured by gas chromatography, and baseline stool microbiome composition was analyzed by 16S rRNA sequencing. Microbial metabolic pathways were analyzed using Kyoto Encyclopedia of Genes and Genomes collection databases.IBS-C subjects had higher breath CH 4 that correlated with higher gut microbial diversity and higher relative abundance (RA) of stool methanogens, predominantly Methanobrevibacter , as well as higher absolute abundance of Methanobrevibacter smithii in stool. IBS-D subjects had higher breath H 2 that correlated with lower microbial diversity and higher breath H 2 S that correlated with higher RA of H 2 S-producing bacteria, including Fusobacterium and Desulfovibrio spp. The predominant H 2 producers were different in these distinct microtypes, with higher RA of Ruminococcaceae and Christensenellaceae in IBS-C/CH 4 + (which correlated with Methanobacteriaceae RA) and higher Enterobacteriaceae RA in IBS-D. Finally, microbial metabolic pathway analysis revealed enrichment of Kyoto Encyclopedia of Genes and Genomes modules associated with methanogenesis and biosynthesis of methanogenesis cofactor F420 in IBS-C/CH 4 + subjects, whereas modules associated with H 2 S production, including sulfate reduction pathways, were enriched in IBS-D.Our findings identify distinct gut microtypes linked to breath gas patterns in IBS-C and IBS-D subjects, driven by methanogens such as M. smithii and H 2 S producers such as Fusobacterium and Desulfovibrio spp, respectively.

Hormone therapy (HT) is used to treat menopause-related conditions and symptoms. The small intestine plays key roles in metabolic and endocrine function, but the effects of HT on the small intestinal microbiome are unknown. Here, we characterize duodenal microbiome differences, and the effects of HT, in postmenopausal women.Female participants undergoing esophagogastroduodenoscopy who were postmenopausal and taking HT (HT+), postmenopausal but not taking HT (HT-), or of reproductive age and not taking exogenous hormones (RA), were identified and matched for body mass index (±3 kg/m2). DNAs were isolated from duodenal aspirates obtained during upper endoscopy. V3 and V4 libraries were used for 16S rRNA sequencing. Serum hormone levels were analyzed by Luminex FlexMap.The core duodenal microbiome was different in HT- participants (n = 12) when compared with RA participants (n = 10), but more similar in HT+ (n = 13) and RA participants. HT- participants had increased Proteobacteria taxa, leading to greater microbial dysbiosis compared with HT+ participants, and had decreased prevalence of Bacteroidetes, which was associated with higher fasting glucose levels, lower duodenal microbial diversity, and lower testosterone levels. HT+ participants had significantly higher estradiol (P = 0.04) and progesterone (P = 0.04), and lower fasting glucose (P = 0.03), than HT- participants, and had increased relative abundance of Prevotella (P = 0.01), and decreased Escherichia (P = 1.12E-7), Klebsiella (P = 5.93E-7), and Lactobacillus (P = 0.02), all associated with lower cardiovascular disease risks.These findings support previous studies suggesting that HT may have beneficial effects following menopause, and although preliminary, may also support a beneficial effect of HT on the duodenal microbiome.

Stool form assessment relies on subjective patient reports using the Bristol Stool Scale (BSS). In a novel smartphone application (app), trained artificial intelligence (AI) characterizes digital images of users' stool. In this study, we evaluate this AI for accuracy in assessing stool characteristics.Subjects with diarrhea-predominant irritable bowel syndrome image-captured every stool for 2 weeks using the app, which assessed images for 5 visual characteristics (BSS, consistency, fragmentation, edge fuzziness, and volume). In the validation phase, using 2 expert gastroenterologists as a gold standard, sensitivity, specificity, accuracy, and diagnostic odds ratios of subject-reported vs AI-graded BSS scores were compared. In the implementation phase, agreements between AI-graded and subject-reported daily average BSS scores were determined, and subject BSS and AI stool characteristics scores were correlated with diarrhea-predominant irritable bowel syndrome symptom severity scores.In the validation phase (n = 14), there was good agreement between the 2 experts and AI characterizations for BSS (intraclass correlation coefficients [ICC] = 0.782-0.852), stool consistency (ICC = 0.873-0.890), edge fuzziness (ICC = 0.836-0.839), fragmentation (ICC = 0.837-0.863), and volume (ICC = 0.725-0.851). AI outperformed subjects' self-reports in categorizing daily average BSS scores as constipation, normal, or diarrhea. In the implementation phase (n = 25), the agreement between AI and self-reported BSS scores was moderate (ICC = 0.61). AI stool characterization also correlated better than subject reports with diarrhea severity scores.A novel smartphone application can determine BSS and other visual stool characteristics with high accuracy compared with the 2 expert gastroenterologists. Moreover, trained AI was superior to subject self-reporting of BSS. AI assessments could provide more objective outcome measures for stool characterization in gastroenterology.

Gut microbiome changes have been associated with human ageing and implicated in age-related diseases including Alzheimer’s disease and Parkinson’s disease. However, studies to date have used stool samples, which do not represent the entire gut. Although more challenging to access, the small intestine plays critical roles in host metabolism and immune function. In this paper (Leite et al. (2021), Cell Reports, doi: 10.1016/j.celrep.2021.109765), we demonstrate significant differences in the small intestinal microbiome in older subjects, using duodenal aspirates from 251 subjects aged 18-80 years. Differences included significantly decreased microbial diversity in older subjects, driven by increased relative abundance of phylum Proteobacteria, particularly family Enterobacteriaceae and coliform genera Escherichia and Klebsiella. Moreover, while this decreased diversity was associated with the ‘ageing process’ (comprising chronologic age, number of medications, and number of concomitant diseases), changes in certain taxa were found to be associated with number of medications alone (Klebsiella), number of diseases alone (Clostridium, Bilophila), or chronologic age alone (Escherichia, Lactobacillus, Enterococcus). Lastly, many taxa associated with increasing chronologic age were anaerobes. These changes may contribute to changes in human health that occur during the ageing process.

Tobacco use is the leading preventable cause of cancer, and affects the respiratory, oral, fecal, and duodenal mucosa-associated microbiota. However, the effects of smoking on the duodenal luminal microbiome have not been studied directly. We aimed to compare the duodenal luminal microbiome in never-smokers, current smokers, and ex-smokers who quit ≥ 10 years ago. In a cross-sectional study, current smokers (CS, n = 24) were identified and matched to never-smokers (NS, n = 27) and ex-smokers (XS, n = 27) by age (± 5 years), body mass index (BMI, ± 3 kg/m2), and sex. Current antibiotic users were excluded. The duodenal luminal microbiome was analysed in 1 aspirate sample per subject by 16S rRNA gene sequencing. Relative abundances (RA) of families associated with increased duodenal microbial diversity, Prevotellaceae, Neisseriaceae, and Porphyromonadaceae, were significantly lower in CS vs. NS. This was driven by lower RA of unknown Prevotella and Porphyromonas species, and Neisseria subflava and N. cinerea, in CS. In contrast, RA of Enterobacteriaceae and Lactobacillaceae (associated with decreased diversity), were significantly higher in CS, due to higher RA of Escherichia-Shigella, Klebsiella and Lactobacillus species. Many of these changes were absent or less pronounced in XS, who exhibited a duodenal luminal microbiome more similar to NS. RA of taxa previously found to be increased in the oral and respiratory microbiota of smokers were also higher in the duodenal luminal microbiome, including Bulledia extructa and an unknown Filifactor species. In conclusion, smoking is associated with an altered duodenal luminal microbiome. However, ex-smokers have a duodenal luminal microbiome that is similar to never-smokers.