Identification of carbohydrate gene clusters obtained from in vitro fermentations as predictive biomarkers of prebiotic responses.

BACKGROUND: Prebiotic fibers are non-digestible substrates that modulate the gut microbiome by promoting expansion of microbes having the genetic and physiological potential to utilize those molecules. Although several prebiotic substrates have been consistently shown to provide health benefits in human clinical trials, responder and non-responder phenotypes are often reported. These observations had led to interest in identifying, a priori, prebiotic responders and non-responders as a basis for personalized nutrition. In this study, we conducted in vitro fecal enrichments and applied shotgun metagenomics and machine learning tools to identify microbial gene signatures from adult subjects that could be used to predict prebiotic responders and non-responders. RESULTS: Using short chain fatty acids as a targeted response, we identified genetic features, consisting of carbohydrate active enzymes, transcription factors and sugar transporters, from metagenomic sequencing of in vitro fermentations for three prebiotic substrates: xylooligosacharides, fructooligosacharides, and inulin. A machine learning approach was then used to select substrate-specific gene signatures as predictive features. These features were found to be predictive for XOS responders with respect to SCFA production in an in vivo trial. CONCLUSIONS: Our results confirm the bifidogenic effect of commonly used prebiotic substrates along with inter-individual microbial responses towards these substrates. We successfully trained classifiers for the prediction of prebiotic responders towards XOS and inulin with robust accuracy (≥ AUC 0.9) and demonstrated its utility in a human feeding trial. Overall, the findings from this study highlight the practical implementation of pre-intervention targeted profiling of individual microbiomes to stratify responders and non-responders.

Output Consistency Scale to Standardize Ostomate Output Description in Clinical Practice and Studies.

Stool descriptors have become popular due to the large diffusion of the Bristol Stool Form Scale (BSFS) via clinical studies, clinical trials, and social media. The applications have been numerous and centered around standardization of terminology that can be used by health care professionals and patients alike, as well as individuals interested in their wellness and the associated partners in the wellness industry. For a portion of the population, the digestive content is rerouted to an external manufactured pouch or bag, making the use of the BSFS visual descriptors of stool difficult. From day one post-resection surgery, ostomates are challenged with output management. The lack of standardized descriptors may hinder proper communication between the individual and the support team, as well as providing proper characterization in clinical studies and clinical trials. We propose the Lincoln Ostomy Output Consistency Scale for jejunostomy, ileostomy and colostomy (LOOCS) to overcome the limitations of the BSFS for qualifying ostomy outputs. The design was based on the need to describe perceived consistency from the ostomate point of view. We anticipate that the LOOCS scale can be effective in pediatric and adult clinical research settings, as well as self-monitoring to manage the quality of life.