High-Intensity Interval Training Improves Lipocalin-2 and Omentin-1 Levels in Men with Obesity
Bibliographic record
Abstract
Abstract Objectives: We investigated the effects of 12 weeks of high-intensity interval training (HIIT) on selected circulating adipokines and other cardiovascular diseases (CVD) risks factors in males with obesity. Methods: Thirty males with obesity were randomly assigned to HIIT and control groups. The HIIT group participated in a prescribed exercise program for 12 weeks, three times per week. Blood lipids, insulin resistance, and select serum adipokines were assessed before and after 12 weeks of the intervention period. Results: HIIT improved body composition and lipid profiles (p<0.05) as well as decreased fasting insulin levels (p=0.001) and HOMA-IR (p=0.002) levels. Furthermore, HIIT increased levels of lipocalin-2 (lcn2) (p=0.002) while decreasing omentin-1 levels (p=0.001) in males with obesity. Changes in lcn2 and omentin-1 concentrations correlated with the changes in risk factors in the HIIT group (p<0.05). Conclusions: The results indicate that 12 weeks of supervised HIIT exercise significantly improves both circulating concentrations of lcn2 and omentin-1, two recently described adipokines, and markers of CVD risk in males with obesity. Further research is necessary to understand the molecular mechanisms involved with these changes.
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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".