Dietary food patterns as determinants of the gut microbiome–endocannabinoidome axis in humans
Bibliographic record
Abstract
The gut microbiota and the endocannabinoidome (eCBome) play important roles in regulating energy homeostasis, and both are closely linked to dietary habits. However, the complex and compositional nature of these variables has limited our understanding of their interrelationship. This study aims to decipher the interrelation between dietary intake and the gut microbiome-eCBome axis using two different approaches for measuring dietary intake: one based on whole food and the other on macronutrient intakes. We reveal that food patterns, rather than macronutrient intakes, were associated with the gut microbiome-eCBome axis in a sample of healthy men and women (n = 195). N-acyl-ethanolamines (NAEs) and gut microbial families were correlated with intakes of vegetables, refined grains, olive oil and meats independently of adiposity and energy intakes. Specifically, higher intakes in vegetables and olive oil were associated with increased relative abundance of Clostridiaceae, Veillonellaceae and Peptostreptococaceae, decreased relative abundance of Acidominococaceae, higher circulating levels of NAEs, and higher HDL and LDL cholesterol levels. Our findings highlight the relative importance of food patterns in determining the gut microbiome-eCBome axis. They emphasize the importance of recognizing the contribution of dietary habits in these systems to develop personalized dietary interventions for preventing and treating metabolic disorders through this axis.
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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 | 0.000 |
| 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 | 0.000 | 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".