Kale improves bowel movements in constipated women and affects some intestinal microbes and metabolites: a pilot study.

Dietary fiber improves intestinal environments, by, among others, increasing stool frequency. Kale is a good source of dietary fiber and minerals; however, the effects of kale on the intestinal environment have not yet been evaluated. This study determined how the intestinal environment, including the intestinal microbiota and its metabolome, and stool frequency are affected by the consumption of kale, in humans. A randomized controlled crossover trial, with a 4-week consumption of kale or control food, was conducted. An integrated analysis of the intestinal microbiota and metabolome was performed, and their relationship with improvements in stool frequency was analyzed. Kale intake for 4 weeks significantly increased stool frequency and altered some intestinal microbes, such as an increase in the [Eubacterium] eligens group and a decrease in the [Ruminococcus] gnavus group. Analysis of subjects with increased stool frequency revealed that this group had smaller amounts of stool before kale intake. Our findings indicate that kale modifies certain gut microbes, such as [Eubacterium] eligens and [Ruminococcus] gnavus, and improves bowel movements, particularly in those with smaller stool amounts.

Integrated analysis of the oral and intestinal microbiome and metabolome of elderly people with more than 26 original teeth: a pilot study.

Elderly subjects with more than 20 natural teeth have a higher healthy life expectancy than those with few or no teeth. The oral microbiome and its metabolome are associated with oral health, and they are also associated with systemic health via the oral-gut axis. Here, we analyzed the oral and gut microbiome and metabolome profiles of elderly subjects with more than 26 natural teeth. Salivary samples collected as mouth-rinsed water and fecal samples were obtained from 22 healthy individuals, 10 elderly individuals with more than 26 natural teeth and 24 subjects with periodontal disease. The oral microbiome and metabolome profiles of elderly individuals resembled those of subjects with periodontal disease, with the metabolome showing a more substantial differential abundance of components. Despite the distinct oral metabolome profiles, there was no differential abundance of components in the gut microbiome and metabolomes, except for enrichment of short-chain fatty acids in elderly subjects. Finally, to investigate the relationship between the oral and gut microbiome and metabolome, we analyzed bacterial coexistence in the oral cavity and gut and analyzed the correlation of metabolite levels between the oral cavity and gut. However, there were few associations between oral and gut for bacteria and metabolites in either elderly or healthy subjects. Overall, these results indicate distinct oral microbiome and metabolome profiles, as well as the lack of an oral-gut axis in elderly subjects with a high number of natural teeth.

Leveraging explainable AI for gut microbiome-based colorectal cancer classification.

Studies have shown a link between colorectal cancer (CRC) and gut microbiome compositions. In these studies, machine learning is used to infer CRC biomarkers using global explanation methods. While these methods allow the identification of bacteria generally correlated with CRC, they fail to recognize species that are only influential for some individuals. In this study, we investigate the potential of Shapley Additive Explanations (SHAP) for a more personalized CRC biomarker identification. Analyses of five independent datasets show that this method can even separate CRC subjects into subgroups with distinct CRC probabilities and bacterial biomarkers.

Recent advances of machine learning applications in human gut microbiota study: from observational analysis toward causal inference and clinical intervention.

Statistical methods, especially machine learning, learning(ML), are pivotal for the analyses of large data generated by multiomics human gut microbiota study. These analyses lead to the discovery of microbe-disease associations. Furthermore, recent efforts for more data transparency and accessible analytical tools improved data availability and study reproducibility. Our recent accumulated knowledge on microbe-disease associations brings light to the next questions: what is the role of microbes in disease progression and how can we apply our knowledge of microbiome in clinical settings? Here, we introduce recent studies that implemented ML to answer the questions of causal inference and clinical translation.