A Pilot 24-Week ‘Bulk and Cut’ Dietary Protocol Combined with Resistance Training Is Feasible and Improves Body Composition and TNF-α Concentrations in Untrained Adult Males
Bibliographic record
Abstract
Background/Objectives: This study piloted a 24-week bodybuilding program combining resistance training (RT) with a dietary bulk-and-cut protocol in middle-aged adult males. Methods: Seven untrained males (33 ± 3.0 years; BMI = 35.0 ± 4.6 kg/m2; body fat = 36 ± 5%) completed a 24-week intervention combining RT with a dietary protocol consisting of 12-week cycles of caloric bulking (0–12 weeks) and cutting (12–24 weeks). The participant retention rate was 64%, while compliance with training was 96.7%, and adherence to dietary cycles was over 93%. To assess the preliminary efficacy of the intervention, venous blood samples and measurements of body composition (BodPod), muscle strength, and VO2max (cycle ergometer) were collected at baseline (week 0) and following the bulking (week 12) and cutting (week 24) cycles. Circulating lipids (triglycerides, total, low-density, and high-density cholesterol), C-reactive protein (CRP), tumor necrosis factor-alpha (TNF-α), interleukin-6 (IL-6), and interleukin-10 (IL-10) were measured in serum. Results: The training led to significant increases in muscle strength, especially in the deadlift (+46%, p < 0.001) and squat (+65%, p < 0.001). Improvements in body composition were characterized by an increase in fat-free mass and a decrease in body fat percentage over the 24-week intervention (+3% and −6%, respectively, p < 0.05). Lipids, CRP, IL-6, and IL-10 did not change significantly, but there was a notable reduction in TNF-α (time effect p = 0.05, pη2 = 0.39), with 15% lower concentrations at week 24 compared to baseline, indicating reduced inflammation. Conclusions: Overall, the pilot intervention achieved high compliance and adherence rates, leading to improvements in body composition and lower resting TNF-α concentrations in a group of middle-aged males with obesity.
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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".