The Role of the Gut Microbiome in Diet and Exercise Effects on Cognition: A Review of the Intervention Literature
Bibliographic record
Abstract
Interest in the gut-brain axis and its implications for neurodegenerative diseases, such as Alzheimer's disease and related dementias, is growing. Microbial imbalances in the gastrointestinal tract, which are associated with impaired cognition, may represent a therapeutic target for lowering dementia risk. Multicomponent lifestyle interventions are a promising dementia risk reduction strategy and most often include diet and exercise, behaviors that are also known to modulate the gut microbiome. A better understanding of the role of the gut microbiome in diet and exercise effects on cognition may help to optimize these lifestyle interventions. The purpose of this review is to summarize findings from diet and exercise interventions that have investigated cognitive changes via effects on the microbiome. We aim to discuss the underlying mechanisms, highlight current gaps in the field, and provide new research directions. There is evidence mainly from rodent studies supporting the notion that microbiota changes mediate the effects of diet and exercise on cognition, with potential mechanisms including end-product metabolites and regulation of local and systemic inflammation. The field lacks whole diet and exercise interventions, especially those involving human participants. It is further limited by heterogeneous rodent models, outcome assessments, and the absence of proper mediation analyses. Trials including older adults with dementia risk factors, factorial designs of diet and exercise, and pre and post measures of microbiota, end-product metabolites, and inflammation would help to elucidate and potentially leverage the role of the microbiome in lowering dementia risk through lifestyle modification.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".