The Effects of Conjugated Linoleic Acid Supplementation during Resistance Training
Bibliographic record
Abstract
PURPOSE: We determined the effects of conjugated linoleic acid (CLA) supplementation during resistance training. METHODS: Seventy-six subjects were randomized to receive CLA (5 g.d(-1)) or placebo (PLA) for 7 wk while resistance training 3 d.wk(-1). Seventeen subjects crossed over to the opposite group for an additional 7 wk. Measurements at baseline, 7 wk, and 14 wk (for subjects in the crossover study) included body composition, muscle thickness of the elbow flexors and knee extensors, resting metabolic rate (RMR), bench and leg press strength, knee extension torque, and urinary markers of myofibrillar degradation (3-methylhistidine (3MH) and bone resorption (cross-linked N-telopeptides (Ntx)). RESULTS: After 7 wk the CLA group had greater increases in lean tissue mass (LTM) (+1.4 vs +0.2 kg; P < 0.05), greater losses of fat mass (-0.8 vs +0.4 kg; P < 0.05), and a smaller increase in 3MH (-0.1 vs + 1.3 micromol.kg LTM.d(-1); P < 0.05) compared with PLA. Changes between groups were similar for all other measurements, except for a greater increase in bench press strength for males on CLA (P < 0.05). In the crossover study subjects had minimal changes in body composition, but smaller increases in 3MH (-1.2 vs +2.2 micromol.kg LTM.d(-1); P < 0.01) and NTx (-4.8 vs +7.3 nmol.kg(-1) LTM.d(-1); P < 0.01) while on CLA versus PLA. CONCLUSIONS: Supplementation with CLA during resistance training results in relatively small changes in body composition accompanied by a lessening of the catabolic effect of training on muscle protein.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.001 | 0.000 |
| 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".