<scp>W</scp>hat are the <i>trans</i> fatty acids issues in foods after discontinuation of industrially produced <i>trans</i> fats? Ruminant products, vegetable oils, and synthetic supplements
Bibliographic record
Abstract
Adverse health effect of trans fatty acids (TFAs) are well recognized, which has precipitated efforts to reduce their content in food products. With the decline of dietary TFAs derived from partial oil hydrogenation, interest is focused on the remaining sources of dietary TFAs derived from ruminants (rTFA), deodorized vegetable oils, trans isomerization during frying, and synthetic conjugated FA (CFA) supplements. Partial hydrogenation and heat‐treated oils result in a random distribution of TFA isomers, whereas rTFAs contain specific isomers resulting from enzymatic processes. Reviews of human observational and metabolic intervention studies have suggested that consumption of rTFAs at current levels should not raise health concerns. However, these studies were not based on rTFA levels currently sold in retail markets. Current feeding practices of ruminants result in higher total TFAs with trans ‐18:1 isomers other than vaccenic acid, and many CFAs other than rumenic acid. The definition of TFA adopted by several countries does not distinguish among isolated TFAs of concern and ignores CFAs that should be included in total TFA due to their negative health effects. Accurate TFA definition establishment and nutritional assessment of individual TFA isomers is needed for labeling purposes. Also required are adoptions of analytical methods to resolve all TFA isomers. This information can then be used to construct a database and as a basis for meaningful recommendations. Practical applications: Areas that require further investigations have been identified: 1) Establish a uniform TFA nomenclature based on chemical structure. 2) Standardize, assess and normalize appropriate methodologies for the analysis of TFAs. 3) Synthesize pure reference standards for TFA identification. 4) Report total TFA levels with undesirable biological activities rather than total TFA content. 5) Assess the nutritional characteristics of individual TFAs independent of their origin. 6) Develop strategies to increase the content of desirable TFA isomers in ruminants. 7) Identify potential bioactive trans metabolites produced from PUFAs in ruminants. 8) Develop labeling regulations based on specific chemical structures and physiological effects regardless of their origin. 9) Construct an accurate database complemented with specific surveys of ruminant product fatty acid (FA) composition that can be accessed by regulatory agencies to make appropriate TFA recommendations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.110 | 0.039 |
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".