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Integrated gut microbiome and metabolome analyses identified fecal biomarkers for bowel movement regulation by Bifidobacterium longum BB536 supplementation: A RCT
BACKGROUND: Bifidobacterium longum BB536 supplementation can be used to regulate bowel movements in various people, including healthy subjects and patients with irritable bowel syndrome (IBS); however, individuals vary in their responses to B. longum BB536 treatment. One putative factor is the gut m...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Research Network of Computational and Structural Biotechnology
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9636538/ https://www.ncbi.nlm.nih.gov/pubmed/36382178 http://dx.doi.org/10.1016/j.csbj.2022.10.026 |
Sumario: | BACKGROUND: Bifidobacterium longum BB536 supplementation can be used to regulate bowel movements in various people, including healthy subjects and patients with irritable bowel syndrome (IBS); however, individuals vary in their responses to B. longum BB536 treatment. One putative factor is the gut microbiota; recent studies have reported that the gut microbiota mediates the effects of diet or drugs on the host. Here, we investigated intestinal features, such as the microbiome and metabolome, related to B. longum BB536 effectiveness in increasing bowel movement frequency. RESULTS: A randomized, double-blind controlled crossover trial was conducted with 24 adults who mainly tended to be constipated. The subjects received a two-week dietary intervention consisting of B. longum BB536 in acid-resistant seamless capsules or similarly encapsulated starch powder as the placebo control. Bowel movement frequency was recorded daily, and fecal samples were collected at several time points, and analyzed by metabologenomic approach that consists of an integrated analysis of metabolome data obtained using mass spectrometry and microbiome data obtained using high-throughput sequencing. There were differences among subjects in B. longum intake-induced bowel movement frequency. The responders were predicted by machine learning based on the microbiome and metabolome features of the fecal samples collected before B. longum intake. The abundances of eight bacterial genera were significantly different between responders and nonresponders. CONCLUSIONS: Intestinal microbiome and metabolome profiles might be utilized as potential markers of improved bowel movement after B. longum BB536 supplementation. These findings have implications for the development of personalized probiotic treatments. |
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