EPA/DHA but Not ALA Reduces Visceral Adiposity and Adipocyte Size in High Fat Diet‐Induced Obese Delta‐6 Desaturase Knockout Mice
Bibliographic record
Abstract
The objective of this omega-3 feeding study was to elucidate the independent effects of α-linolenic acid (ALA) versus eicosapentaenoic (EPA)/docosahexaenoic acid (DHA) on visceral adiposity and inflammatory signaling in diet-induced obese delta-6 desaturase (Fads2) knockout (KO) mice. Male wildtype (WT) and Fads2 KO mice were fed a high-fat diet (45% kcal from fat) containing either lard (no omega-3s), flaxseed (ALA), or menhaden (EPA/DHA) for 21 weeks. Epididymal white adipose tissue (eWAT) was analyzed for changes in tissue weight, adipocyte size, triacylglycerol (TAG) and fatty acid content, and inflammatory markers. Despite no differences in final body weight, menhaden-fed mice had lower eWAT weight, smaller adipocytes, and lower TAG content compared to lard-fed mice regardless of Fads2 genotype. The eWAT of flaxseed-fed WT mice resembled menhaden-fed mice, while the eWAT of flaxseed-fed KO mice resembled lard-fed mice. No differences were observed in the expression of genes regulating eWAT inflammatory signaling (Tnfα, Nfκb, Mapk14, Mcp1, Ccl5, Tlr4, Nlrp3, or Adipoq) or the abundance of select proteins (p38-MAPK or MCP-1). In conclusion, a high-fat diet containing EPA/DHA, but not ALA, attenuates adipocyte hypertrophy and lowers TAG content but has no effect on eWAT inflammation in a mouse model of long-term diet-induced obesity.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".