Could the Quality of Dietary Fat, and Not Just Its Quantity, Be Related to Risk of Obesity?
Bibliographic record
Abstract
This review focuses on the possible association between types of fatty acids and weight change. It examines the biological plausibility underlining these associations and the evidence obtained to date from clinical trials and epidemiological studies. Animal studies have shown that dietary short- and medium-chain fatty acids compared to long-chain fatty acids appear to promote weight loss. Similarly, monounsaturated fatty acids (MUFAs) appear to favor weight loss compared to saturated fatty acids (SFAs) in human studies. The structure of fatty acids seems to affect their degree of oxidation and deposition. Although results are conflicting, human studies follow the general trend reported in animal studies. These trials suggest that some fatty acids are prone to oxidation and some others lead to fat storage when comparing isocaloric diets. For instance, n-3 polyunsaturated fatty acids (PUFAs), eicosapentaenoic and docosahexaenoic acids are preferentially oxidizied to other PUFA but results remain inconsistent. Epidemiological studies concerning this issue reported that total dietary fat, which includes MUFA, PUFA, and SFA could increase the risk of obesity, but results are few and conflicting. The rising biological plausibility linking dietary fat quality and risk of obesity, together with the rather recent addition of fatty acids content in food composition tables, support the need for major epidemiological studies in that area.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".