Dietary Fatty Acid Composition Modulates Obesity and Interacts with Obesity‐Related Genes
Bibliographic record
Abstract
The prevalence of obesity is skyrocketing worldwide. The scientific evidence has associated obesity risk with many independent factors including the quality of dietary fat and genetics. Dietary fat exists as the main focus of dietary guidelines targeting obesity reduction. To prevent/minimize the adipogenic effect of dietary fatty acids (FA), intakes of long-chain saturated- and trans-FA should be reduced and substituted with unsaturated FA. The optimal proportions of dietary unsaturated FA are yet to be defined, along with a particular emphasis on the need to achieve a balanced ratio of n-3:n-6 polyunsaturated FA and to increase monounsaturated FA consumption at the expense of saturated FA. However, inter-individual variability in weight loss in response to a dietary intervention is evident, which highlights the importance of exploring gene-nutrient interactions that can further modulate the risk for obesity development. The quality of dietary fat was found to modulate obesity development by interacting with genes involved in fatty acid metabolism, adipogenesis, and the endocannabinoid system. This review summarizes the current knowledge on the effect of the quality of dietary fat on obesity phenotype and obesity-related genes. The evidence is not only supporting the modulatory effect of fat quality on obesity development but also presenting a number of interactions between obesity-related genes and the quality of dietary fat. The identified gene-FA interaction may have a clinical importance and holds a promise for the possibility of using genetically targeted dietary interventions to reduce obesity risk in the future.
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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) | 0.001 | 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) | 0.002 | 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".