Cardiometabolic benefits of a non-industrialized-type diet are linked to gut microbiome modulation
Bibliographic record
Abstract
Industrialization adversely affects the gut microbiome and predisposes individuals to chronic non-communicable diseases. We tested a microbiome restoration strategy comprising a diet that recapitulated key characteristics of non-industrialized dietary patterns (restore diet) and a bacterium rarely found in industrialized microbiomes (Limosilactobacillus reuteri) in a randomized controlled feeding trial in healthy Canadian adults. The restore diet, despite reducing gut microbiome diversity, enhanced the persistence of L. reuteri strain from rural Papua New Guinea (PB-W1) and redressed several microbiome features altered by industrialization. The diet also beneficially altered microbiota-derived plasma metabolites implicated in the etiology of chronic non-communicable diseases. Considerable cardiometabolic benefits were observed independently of L. reuteri administration, several of which could be accurately predicted by baseline and diet-responsive microbiome features. The findings suggest that a dietary intervention targeted toward restoring the gut microbiome can improve host-microbiome interactions that likely underpin chronic pathologies, which can guide dietary recommendations and the development of therapeutic and nutritional strategies.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".