Dietary iron interacts with diet composition to modulate the endocannabinoidome and the gut microbiome in mice
Bibliographic record
Abstract
Abstract The endocannabinoidome (eCBome) and the gut microbiota have been implicated in diet-induced obesity and impaired metabolism. While the eCBome and the gut microbiome are known to respond to diet macronutrient composition, interaction with micronutrient intake has been relatively unexplored. Iron (Fe) is an essential micronutrient for the function of enzymes involved in energy and lipid metabolism. Here, we evaluated how 28 days of Fe depletion and enrichment, in interaction with Low Fat-Low Sucrose (LFLS) or High Fat-High Sucrose (HFHS) diets, affect the host via the eCBome, and modulate intestinal gut microbial communities. Circulating levels of N -oleoyl-ethanolamine (OEA) showed an elevation associated with Fe-enriched LFLS diet, while the Fe-depleted HFHS diet showed an elevation of N -arachidonoyl-ethanolamine (anandamide, AEA) and a decrease of circulating linoleic acid. In parallel, the response of intestinal inflammatory mediators to Fe in the diet showed decreased levels of prostaglandins PGE 1 , PGE 3 , and 1a,1b-dihomo PGF 2 α in the caecum. Individual differences in microbial taxa were less pronounced in the ileum than in the caecum, where Eubacterium coprostanoligenes group showed an increase in relative abundance associated with Fe-depleted LFLS diets. In conclusion, our study shows that Fe intake modulates the response to the macronutrient composition of the diet in mice.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".