Beef Fat Enriched with Polyunsaturated Fatty Acid Biohydrogenation Products Improves Insulin Sensitivity Without Altering Dyslipidemia in Insulin Resistant JCR:LA‐<i>cp</i> Rats
Bibliographic record
Abstract
The main dietary sources of trans fatty acids are partially hydrogenated vegetable oils (PHVO), and products derived from polyunsaturated fatty acid biohydrogenation (PUFA-BHP) in ruminants. Trans fatty acid intake has historically been associated with negative effects on health, generating an anti-trans fat campaign to reduce their consumption. The profiles and effects on health of PHVO and PUFA-BHP can, however, be quite different. Dairy products naturally enriched with vaccenic and rumenic acids have many purported health benefits, but the putative benefits of beef fat naturally enriched with PUFA-BHP have not been investigated. The objective of the present experiment was to determine the effects of beef peri-renal fat (PRF) with differing enrichments of PUFA-BHP on lipid and insulin metabolism in a rodent model of dyslipidemia and insulin resistance (JCR:LA-cp rat). The results showed that 6 weeks of diet supplementation with beef PRF naturally enriched due to flaxseed (FS-PRF) or sunflower-seed (SS-PRF) feeding to cattle significantly improved plasma fasting insulin levels and insulin sensitivity, postprandial insulin levels (only in the FS-PRF) without altering dyslipidemia. Moreover, FS-PRF but not SS-PRF attenuated adipose tissue accumulation. Therefore, enhancing levels of PUFA-BHP in beef PRF with FS feeding may be a useful approach to maximize the health-conferring value of beef-derived fats.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".