Conjugated linoleic acid reduces body fat and prevents seasonal weight gain among overweight adults
Bibliographic record
Abstract
Conjugated linoleic acid (CLA) supplementation has been shown to reduce fat mass in animals, but results in humans have been inconsistent. To further test the effect of CLA on body composition, we conducted a randomized, double‐blind study in 40 overweight adults. For six months including the holiday season, 22 subjects took 3.2 g/d of a 50:50 mixture of cis‐9, trans‐11 and trans‐10, cis‐12 isomers of CLA and 18 subjects took a safflower oil placebo. Using the 4‐compartment model, we found a decrease in body fat with CLA (P = 0.02): while CLA reduced body fat (1.0 ± 2.2 kg, P = 0.05), placebo tended to increase (0.7 ± 3.0 kg, P = NS). CLA also reduced body weight vs. placebo (P = 0.04). Specifically, CLA attenuated weight gain during the holiday season (P = 0.01). To compare our results with previous studies, we performed a meta‐analysis on the rate of fat loss with CLA vs. placebo, which averaged −80 g/wk (P = 0.01). Power analysis indicated that most studies reporting no significant effect of CLA on fat mass were underpowered or of insufficient duration. In conclusion, CLA reduced body fat over six months and prevented weight gain during the holiday season in overweight adults. Research has shown that holiday weight gain contributes to annual weight gain and that overweight individuals are susceptible to greater holiday gains. CLA may aid in reducing these gains, particularly among overweight individuals. Supported by Cognis Co.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| 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".