Conjugated Linoleic Acid Effects on Cancer, Obesity, and Atherosclerosis: A Review of Pre-Clinical and Human Trials with Current Perspectives
Bibliographic record
Abstract
Obesity and its comorbidities, including type 2 diabetes and cardiovascular disease, are straining our healthcare system, necessitating the development of novel strategies for weight loss. Lifestyle modifications, such as exercise and caloric restriction, have proven effective against obesity in the short term, yet obesity persists because of the high predilection for weight regain. Therefore, alternative approaches to achieve long term sustainable weight loss are urgently needed. Conjugated linoleic acid (CLA), a fatty acid found naturally in ruminant animal food products, has been identified as a potential anti-obesogenic agent, with substantial efficacy in mice, and modest efficacy in obese human populations. Originally described as an anti-carcinogenic fatty acid, in addition to its anti-obesogenic effects, CLA has now been shown to possess anti-atherosclerotic properties. This review summarizes the pre-clinical and human studies conducted using CLA to date, which collectively suggest that CLA has efficacy against cancer, obesity, and atherosclerosis. In addition, the potential mechanisms for the many integrative physiological effects of CLA supplementation will be discussed in detail, including an introduction to the gut microbiota as a potential mediator of CLA effects on obesity and 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".