t-10, c-12 CLA Dietary Supplementation Inhibits Atherosclerotic Lesion Development Despite Adverse Cardiovascular and Hepatic Metabolic Marker Profiles
Bibliographic record
Abstract
Animal and human studies have indicated that fatty acids such as the conjugated linoleic acids (CLA) found in milk could potentially alter the risk of developing metabolic disorders including diabetes and cardiovascular disease (CVD). Using susceptible rodent models (apoE(-/-) and LDLr(-/-) mice) we investigated the interrelationship between mouse strain, dietary conjugated linoleic acids and metabolic markers of CVD. Despite an adverse metabolic risk profile, atherosclerosis (measured directly by lesion area), was significantly reduced with t-10, c-12 CLA and mixed isomer CLA (Mix) supplementation in both apoE(-/-) (p<0.05, n = 11) and LDLr(-/-) mice (p<0.01, n = 10). Principal component analysis was utilized to delineate the influence of multiple plasma and tissue metabolites on the development of atherosclerosis. Group clustering by dietary supplementation was evident, with the t-10, c-12 CLA supplemented animals having distinct patterns, suggestive of hepatic insulin resistance, regardless of mouse strain. The effect of CLA supplementation on hepatic lipid and fatty acid composition was explored in the LDLr(-/-) strain. Dietary supplementation with t-10, c-12 CLA significantly increased liver weight (p<0.05, n = 10), triglyceride (p<0.01, n = 10) and cholesterol ester content (p<0.01, n = 10). Furthermore, t-10, c-12 CLA also increased the ratio of 18∶1 to 18∶0 fatty acid in the liver suggesting an increase in the activity of stearoyl-CoA desaturase. Changes in plasma adiponectin and liver weight with t-10, c-12 CLA supplementation were evident within 3 weeks of initiation of the diet. These observations provide evidence that the individual CLA isomers have divergent mechanisms of action and that t-10, c-12 CLA rapidly changes plasma and liver markers of metabolic syndrome, despite evidence of reduction in atherosclerosis.
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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) | 0.001 | 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".