Strength Training can Modulate Urinary Adipokine Levels in Healthy Young Males
Bibliographic record
Abstract
Adipose tissue stores energy in fat-rich adipocytes, which can produce and release several adipokines and modulate body metabolism. Exercise may induce adipokine production in adipocytes; however, the relationship between the two remains unclear. Few studies have shown the relationship between adipokines and strength training. Thus, we aimed to evaluate the acute and chronic effects of strength training (ST) on urinary adiponectin, leptin, and resistin levels. Twelve untrained young men (23.42 ± 2.67 years) were included in this study. Body composition was evaluated at baseline and after completing of the training protocol using densitometry. Training protocol consisted of three exercises with three sets of 65% of one-repetition maximum (1MR) with a pause of 90 s between sets, each exercise lasting 5 s (2 s concentric / 3 s eccentric). The sessions were carried out three times a week for 10 weeks. Urine was collected during the pre- and post-training in the first and 30th session. Adipokine levels were determined by ELISA. Urinary levels of leptin acutely increased after the first ST session, and after the last ST session. Chronic changes in the leptin levels were also found when comparing the values before the last ST and before the first ST session. Urinary adiponectin levels changed in the comparison of values before and after the last session. There was a significant increase in the adiponectin levels when comparing values after the first and last ST sessions. The levels of resistin chronically increased. Strength training can induce acute and chronic changes in urinary levels of adipokines.
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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) | 0.001 | 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".