The Impact of Nutrition, Physical Activity, Beneficial Microbes, and Fecal Microbiota Transplant for Improving Health
Bibliographic record
Abstract
The recognition that microbes are integral to human life has led to studies on how to manipulate them in favor of health outcomes. To date, there has been no conjoint recommendation for the intake of dietary compounds that can complement the ingested organisms in terms of promoting an improved health outcome. The aim of this review is to discuss how beneficial microbes in the form of probiotics, fermented foods, and donor feces are being used to manage health. In addition, we explore the rationale for selecting beneficial microbial strains and aligning diets to accommodate their propagation in the gut. A pilot clinical trial design is presented to examine the effects of probiotics and exercise in patients with phenylketonuria (PKU); it is the most common inborn error of amino acid metabolism, and it is a complication that requires lifelong dietary intervention. The example design is provided to illustrate the importance of using omics technology to see if the intervention elevates neuroactive biogenic amines in the plasma; increases the abundance of Eubacterium rectale, Coprococcus eutactus, Akkermansia muciniphila, or Butyricicoccus; and increases Escherichia/Shigella in the gut, all as markers of improved health. By emphasizing the combined importance of diet, microbial supplements, and the gut microbiome, we hope that future studies will better align these components, not only to improve outcomes, but also to enhance our understanding of the mechanisms.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".