Conjugated Linoleic Acid Combined with Creatine Monohydrate and Whey Protein Supplementation during Strength Training
Bibliographic record
Abstract
PURPOSE: The authors examined the combined effects of conjugated linoleic acid (CLA), creatine (C), and whey protein (P) supplementation during strength training. METHODS: Sixty-nine participants (52 men, 17 women; M +/- SD age 22.5 +/-2.5 yr) were randomly assigned (double-blind) to 1 of 3 groups: CCP (6 g/d CLA + 9 g/d C + 36 g/d P; n = 22), CP (C + P + placebo oil; n = 25), or P (P + placebo oil; n = 22) during 5 wk of strength training (4-5 sets, 6-12 repetitions, 6 d/wk). Measurements were taken for body composition (air-displacement plethysmography), muscle thickness (ultrasound) of the flexors and extensors of the elbow and knee, 1-repetition-maximum (1-RM) strength (leg press and bench press), urinary markers of bone resorption (N-telopeptides, NTx), myofibrillar protein catabolism (3-methylhistidine; 3-MH), oxidative stress (8-isoprostanes), and kidney function (microalbumin) before and after training. RESULTS: Contrast analyses indicated that the CCP group had a greater increase in bench-press (16.2% +/- 11.3% vs. 9.7% +/- 17.0%; p < .05) and leg-press (13.1% +/- 9.9% vs. 7.7% +/-14.2%; p < .05) strength and lean-tissue mass (2.4% +/- 2.8% vs. 1.3% +/-4.1%; p < .05) than the other groups combined. All groups increased muscle thickness over time (p < .05). The relative change in 3-MH (CCP -4.7% +/- 70.2%, CP -0.4% +/- 81.4%, P 20.3% +/- 75.2%) was less in the groups receiving creatine (p < .05), with the difference for NTx also close to significance (p = .055; CCP -3.4% +/- 66.6%, CP -3.9% +/- 64.9%, P 26.0% +/- 63.8%). There were no changes in oxidative stress or kidney function. CONCLUSION: Combining C, CLA, and P was beneficial for increasing strength and lean-tissue mass during heavy resistance training.
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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) | 0.001 | 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".