Dietary Lipids, Gut Microbiota, and Their Metabolites: Insights from Recent Studies
Bibliographic record
Abstract
Dietary lipid intake can influence the gut microbiota (GM) and their metabolites, such as short-chain fatty acids (SCFA) and bile acids, which are key mediators of health. The objective is to examine how dietary lipids’ quantity and quality influence the GM and metabolite profiles. A literature review of 33 studies in animals and humans was performed on the effects of saturated fatty acids (SFAs), monounsaturated fatty acids (MUFAs), polyunsaturated fatty acids (PUFAs), trans-fatty acids (TFAs), and sterols on GM composition and gut-derived metabolites. The results show that diets rich in MUFAs, n-3 PUFAs, and short-chain FAs have the potential to enhance beneficial bacteria and metabolites. In addition, trans-palmitoleic acid, conjugated linoleic acid, and phytosterols may also have potentially beneficial effects on GM, but more research is needed. Medium-chain FAs and n-6 PUFAs have variable effects on the GM. Conversely, intakes of high-fat diets, long-chain SFAs, industrial TFAs, and cholesterol disrupt GM balance. In conclusion, animal studies clearly demonstrate that dietary fats influence the GM and related metabolites. Yet, human studies are limited. Therefore, well-designed human studies that consider the whole diet and baseline health status are needed to better understand the effects of dietary lipids on GM.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".