The Athlete Gut Microbiome: A Narrative Review of Multi-Omics Insights and Next-Generation Probiotic Strategies
Bibliographic record
Abstract
The gut microbiome plays a pivotal role in athletic health and performance by influencing metabolism, immunity, gastrointestinal integrity, and recovery. Advances in high-throughput sequencing and integrative multi-omics approaches have provided unprecedented insights into how microbial communities adapt to the physiological demands of training and competition. Key microbial taxa, including short-chain fatty acid producers, lactate utilizers, and carbohydrate fermenters, have been linked to enhanced endurance, reduced inflammation, and improved recovery, opening exciting opportunities for targeted probiotic interventions. While conventional probiotics remain valuable, next-generation engineered strains hold particular promise, supported by recent regulatory milestones such as FDA approvals of engineered probiotics and gene-edited livestock. In this narrative review, we synthesize recent multi-omics research to examine mechanistic links between the athlete gut microbiome and performance, evaluate current and emerging probiotic strategies, and highlight challenges related to personalization, standardization, and regulation. With longitudinal, performance-oriented trials and harmonized frameworks, microbiome-targeted interventions are poised to move beyond exploratory research toward evidence-based, precision tools that optimize athletic performance and recovery.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".