Inflammation status of healthy young men: initial and specific responses to resistance training
Bibliographic record
Abstract
Our primary aim was to study the effects of a 4-week preparatory resistance-training (RT) period followed by 12 weeks of 2 specific RT protocols (either hypertrophic-strength (HS) or strength-hypertrophy-power training) on inflammation markers and the possible relationship of the changes in abdominal fat and lean mass to the changes in inflammation status. A total of 82 healthy men were included in the study. Maximal concentric leg press strength (1-repetition maximum), total body lean mass, total body and abdominal fat mass, circulating high-sensitivity C-reactive protein, interleukin-6, interleukin-1 receptor antagonist (IL-1ra), monocyte chemoattractant protein 1 (MCP-1), and selected adipocytokines (resistin, adiponectin, and leptin) concentrations were measured before and after 4 (wk4) and 16 weeks (wk16) of RT. After the initial phase of RT, on wk4, abdominal and total fat mass as well as plasma leptin concentrations were significantly reduced (p < 0.05), whereas muscle mass, IL-1ra, resistin, and MCP-1 concentrations were significantly increased (p < 0.05). During specialized training phase, at wk16, only HS led to further reduction in abdominal and total fat mass, resistin, and leptin (p < 0.05), whereas both training modes led to lower MCP-1 concentrations (p < 0.05). Abdominal fat mass and circulating leptin were reduced already after 4 weeks of RT. Simultaneously, circulating MCP-1 and resistin concentrations increased, possibly as markers of muscle adaptation and regeneration. The present findings also suggest that RT with hypertrophic focus is beneficial for further reductions in abdominal fat mass and to decrease circulating inflammatory markers.
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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 | 0.001 | 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".