Health effects of ruminant trans fatty acids with emphasis on type 2 diabetes
Bibliographic record
Abstract
Recent government bans on industrial trans fatty acids (TFA) in developed countries has left naturally occurring TFA from ruminant products (e.g., dairy, beef, and lamb) as the sole source of TFA in the food supply. In contrast to industrial TFA, which have undisputed adverse health effects, ruminant TFA such as trans vaccenic acid (TVA; trans11-18:1), rumenic acid (RA; cis9, trans11-18:2) and trans palmitoleic acid (TPA; trans9-16:1) have been associated with reduced risk for some diseases such as type 2 diabetes. The present review summarizes the findings from observational, animal and human studies investigating the effects of ruminant TFA on metabolic parameters related to type 2 diabetes, and provides an update on the current knowledge of their biosynthesis, intake and factors affecting their concentrations in ruminant derived foods. Overall, observational studies and a small number of animal studies suggest that ruminant TFA may be protective against type 2 diabetes, whereas the same benefits have not been observed in other animal studies or in human clinical trials. Additional clinical and mechanistic studies are needed to better understand the isomer-specific effects of ruminant TFA. Until then, production practices resulting in increased levels of this group of fatty acids in ruminant milk and meat should be carefully reconsidered.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".