Supplementation with a proprietary blend of ancient peat and apple extract may improve body composition without affecting hematology in resistance-trained men
Bibliographic record
Abstract
Adenosine-5'-triphosphate (ATP) is primarily known as a cellular source of energy. Increased ATP levels may have the potential to enhance body composition. A novel, proprietary blend of ancient peat and apple extracts has been reported to increase ATP levels, potentially by enhancing mitochondrial ATP production. Therefore, the purpose of this investigation was to determine the supplement's effects on body composition when consumed during 12 weeks of resistance training. Twenty-five healthy, resistance-trained, male subjects (age, 27.7 ± 4.8 years; height, 176.0 ± 6.5 cm; body mass, 83.2 ± 12.1 kg) completed this study. Subjects supplemented once daily with either 1 serving (150 mg) of a proprietary blend of ancient peat and apple extracts (TRT) or placebo (PLA). Supervised resistance training consisted of 8 weeks of daily undulating periodized training followed by a 2-week overreach and a 2-week taper phase. Body composition was assessed using dual-energy X-ray absorptiometry and ultrasound at weeks 0, 4, 8, 10, and 12. Vital signs and blood markers were assessed at weeks 0, 8, and 12. Significant group × time (p < 0.05) interactions were present for ultrasound-determined cross-sectional area, which increased in TRT (+0.91 cm(2)) versus PLA (-0.08 cm(2)), as well as muscle thickness (TRT: +0.46; PLA: +0.04 cm). A significant group × time (p < 0.05) interaction existed for creatinine (TRT: +0.06; PLA: +0.15 mg/dL), triglycerides (TRT: +24.1; PLA: -20.2 mg/dL), and very-low-density lipoprotein (TRT: +4.9; PLA: -3.9 mg/dL), which remained within clinical ranges. Supplementation with a proprietary blend of ancient peat and apple extracts may enhance resistance training-induced skeletal muscle hypertrophy without affecting fat mass or blood chemistry in healthy males.
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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 | 0.000 |
| 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) | 0.002 | 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".