Individual <i>trans</i> 18:1 Isomers are Metabolised Differently and Have Distinct Effects on Lipogenesis in 3T3‐L1 Adipocytes
Bibliographic record
Abstract
The objective of this research was to study the metabolism of individual trans fatty acids (FAs) that can be found in ruminant fat or partially hydrogenated vegetable oils (PHVO) and determine their effects on FA composition and lipogenic gene expression in adipocytes. Differentiated 3T3-L1 adipocytes were treated with 200 µM of either trans-9-18:1, trans-11-18:1, trans-13-18:1, cis-9-18:1 or BSA vehicle control for 120 h. Trans-9-18:1 increased total cell FA content (µmole/well) compared to other FA treatments, which was mainly related to the accumulation of trans-9-18:1 in the cells. Adipocytes were able to desaturate a significant proportion of absorbed trans-11-18:1 and trans-13-18:1 (~20 and 30% respectively) to cis-9,trans-11-18:2 and cis-9,trans-13-18:2, whereas trans-9-18:1 was mostly incorporated intact resulting in a greater lipophilic index (i.e. decreased mean FA fluidity) of adipocytes. Trans-9-18:1 up-regulated (P < 0.05) the expression of lipogenic genes including acetyl-CoA carboxylase (1.65 fold), FA synthase (1.45 fold), FA elongase-5 (1.52 fold) and stearoyl-CoA desaturase-1 (1.49 fold), compared to the control, whereas trans-11-18:1 and trans-13-18:1 did not affect the expression of these genes compared to control. Our results suggest that the metabolism and lipogenic properties of trans-11-18:1 and trans-13-18:1, typically the most abundant trans FA in beef from cattle fed forage-based diets, are similar and are different from those of trans-9-18:1, the predominant trans FA in PHVO.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".