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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".