Influence of dietary components on the gut microbiota of middle-aged adults: the gut-Mediterranean connection
Bibliographic record
Abstract
BACKGROUND: A plant-focused, healthy dietary pattern, such as the Mediterranean diet enriched with dietary fiber, polyphenols, and polyunsaturated fats, is well known to positively influence the gut microbiota. Conversely, a processed diet high in saturated fats and sugars negatively impacts gut diversity, potentially leading to weight gain, insulin resistance, and chronic, low-grade inflammation. Despite this understanding, the mechanisms by which the Mediterranean diet impacts the gut microbiota and its associated health benefits remain unclear. METHODS: This retrospective, observational study explored the relationships between Mediterranean dietary components-vegetables, fruits and nuts, legumes, whole grains, fish, meat, dairy, alcohol, saturated and unsaturated fats-and the gut microbiota in middle-aged adults enrolled in Alberta's Tomorrow Project, Canada. Diet was recorded using the Canadian Dietary History Questionnaire (CDHQ-II) and participants were classified into four quartiles based on a modified Mediterranean Diet Score. Blood and fecal samples were collected for metabolomics and 16S rRNA sequencing, respectively. RESULTS: Findings revealed that higher adherence to the Mediterranean diet was associated with increased alpha diversity and a greater abundance of beneficial fiber-degrading bacteria, including Prevotella, Parabacteroides, Clostridium XIVb, Coprobacter, and Turicibacter. Furthermore, participants who consumed more Mediterranean diet components exhibited higher concentrations of serum microbial metabolites including p-hydroxy hippuric acid and indole-acetaldehyde. CONCLUSIONS: Results demonstrate a pivotal role of the gut microbiota, via its metabolites in harnessing the health benefits of the Mediterranean diet, highlighting its potential to promote metabolic health and prevent chronic disease.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".