Adherence to the Mediterranean diet can beneficially affect the gut microbiota composition: a systematic review
Bibliographic record
Abstract
AIM: Dietary patterns could have a notable role in shaping gut microbiota composition. Evidence confirms the positive impact of the Mediterranean diet (MD), as one of the most studied healthy dietary patterns, on the gut microbiota profile. We conducted this systematic review to investigate the results of observational studies and clinical trials regarding the possible changes in the gut microbiota composition, metabolites, and clinical outcomes following adherence to MD in healthy cases or patients suffering from metabolic disorders. METHODS: A systematic literature search was conducted on PubMed, Web of Science, and Scopus databases until October 2023. Two researchers separately screened the titles, abstracts, and then full-text of the articles and selected the relevant studies. Quality assessment of observational and interventional studies was performed by Newcastle-Ottawa and Cochrane checklists, respectively. RESULTS: A total of 1637 articles were obtained during the initial search. Ultimately, 37 articles, including 17 observational and 20 interventional studies, were included in this systematic review. Ten observational and 14 interventional studies reported a correlation between MD adherence and microbiota diversity. Faecalibacterium and Prevotella were the most frequent bacterial genera with increased abundance in both observational and interventional studies; an Increment of Bacteroides genus was also reported in observational studies. Better glycemic control, lowering fat mass, better bowel movement, decreased bloating, inflammation, and hospitalization risk were the reported clinical outcomes. CONCLUSION: Adherence to the MD is associated with significant beneficial changes in the gut microbiota diversity, composition, and functions and major clinical improvements in most populations.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".