Fecal Metabolic and Microbial Responses to Five Acute Fueling Strategies During a 60-Minute Rowing Protocol: A Randomized Crossover Multi-Omics Pilot Study
Background/Objectives: We compared next-morning fecal metabolic and microbial responses to five acute fueling strategies. Methods: Eight male recreational rowers with complete specimens from 12 crossover completers received an erythritol comparator, two carbohydrate doses, fixed carbohydrate–protein, and personalized carbohydrate–protein (P-CPS). Eighty specimens yielded 40 paired changes. Participant-blocked, period-adjusted models used restricted permutations and Benjamini–Hochberg false discovery rate (FDR) correction. Results: No primary global test passed across-modality FDR correction: SCFAs, p = 0.0321, q = 0.0963; metabolome, q = 0.1334; microbiome, q = 0.2813. No metabolite or originally annotated genus passed its omnibus FDR family. Post hoc SILVA 138.2 reclassification retained a nonsignificant global result but identified three low-count genus-level omnibus signals. Secondary change-score contrasts indicated higher isovaleric and isobutyric post/pre fold changes under P-CPS, fixed carbohydrate–protein, and high carbohydrate than under the comparator. However, no concentration or composite-endpoint contrast remained FDR-positive in post hoc models adjusting for same-trial pre-trial values. Caproic acid evidence also weakened with expanded correction and analytical-range checks. Two metabolome–SCFA principal-component correlations survived FDR correction and persisted after exclusion of overlapping annotations. Conclusions: This pilot identified model-sensitive secondary chemical signals without FDR-supported global intervention effects. Baseline imbalance, unmeasured beverage osmolality and fecal water, and quantification limitations preclude attributing these signals to increased fermentation or nutritional benefit.
Edible insects are emerging as sustainable functional foods, yet human evidence for microbiome-mediated effects remains limited, particularly in Asian populations. Therefore, this study investigated whether cricket powder supplementation modulates gut microbiome composition and metabolic outputs in Thai subjects with h...
A. Prachansuwan, Pitchayaporn Sukkha, Parunya Thiyajai et al.· Current Research in Food Sci...· 0 citations
Gastrointestinal microbial communities are dynamic and adapt to host diet, environment, and physiological status. However, fecal metabolites are rarely quantified alongside species diversity and abundance in microbiome research, limiting functional interpretations of microbial shifts. To test the hypothesis that a st...
A. DiSilvestro, L. T. Wesolowski, Brooke D. Williams et al.· Journal of Animal Science· 0 citations
Exercise alters gut microbiome composition, but the temporal dynamics and diet-dependent metabolic interactions remain unclear. We investigated longitudinal changes in gut microbiota, functional pathways, and metabolite profiles during and after an exercise intervention. Twenty-four healthy adults completed a sequentia...
Background: Although dietary fat and carbohydrates are major determinants of gut microbiota composition, their interactive effects across changing dietary monosaccharide-to-lard energy ratios remain incompletely understood. This study descriptively examined treatment-level cecal microbiome profiles across dietary gradi...
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Dietary fiber shapes both gut microbial community structure and the fermentable substrates available to resident taxa, yet how fiber deprivation reshapes taxonomic representation and predicted functional potential remains incompletely resolved. We performed an in silico re-analysis of publicly deposited 16S rRNA gene d...
Shaza N. Alkhatib· International Journal of Mol...· 0 citations
Alternative exercise prescriptions and doses would be expected to result in distinct signatures due to differences in duration and intensity. We tested two novel combined endurance and resistance exercise regimens to understand how differing prescriptions alter the timecourse of metabolomics response. Serum metabolomic...
Z. Graham, K. Pathak, K. Garcia-Mansfield et al.· Physiological Reports· 0 citations
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.