Effect of whole-body resistance training at different load intensities on circulating inflammatory biomarkers, body fat, muscular strength, and physical performance in postmenopausal women
Bibliographic record
Abstract
The primary purpose of this study was to identify the impact of whole-body resistance training (RT) at different load intensities on adipokines, adhesion molecules, and extracellular heat shock proteins in postmenopausal women. As secondary purpose, we analyzed the impact of RT at different load intensities on body fat, muscular strength, and physical performance. Forty participants were randomized into lower-load intensity RT (LIRT, n = 20, 30–35 repetition maximum in the first set of each exercise) or higher-load intensity RT (HIRT, n = 20, 8–12 repetition maximum in the first set of each exercise). Adipokines (adiponectin and leptin), adhesion molecules (MCP-1 and ICAM-1), extracellular heat shock proteins (HO-1 and eHSP60), body fat, muscular strength (1RM), and physical performance [400-meter walking test (400-M) and 6-minute walking test (6MWT)] were analyzed at baseline and after 12-weeks RT. There was a significant time-by-group interaction for eHSP60 (P = 0.049) and 400-M (P = 0.003), indicating superiority of HIRT (d = 0.47 and 0.55). However, both groups similarly improved adiponectin, ICAM-1, HO-1, body fat, 1RM, and 6MWT (P < 0.05). Our study suggests that load intensity does not seem to determine the RT effect on several obesity-related pro-inflammatory and chemotactic compounds, body fat, 1RM, and 6MWT in postmenopausal women, although a greater improvement has been revealed for eHSP60 and 400-M in HIRT. Novelty: Higher-load intensity resistance training improves eHSP60 and 400-M in postmenopausal women. Resistance training improves the inflammatory profile, body fat, muscle strength, and 6MWT, regardless of load intensity.
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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) | 0.001 | 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 | 0.000 | 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".