Hepatokines modulation in obesity: which exercise training model is better in men with obesity?
Bibliographic record
Abstract
Objective: Obesity is associated with an exacerbated metabolic condition that is related to impaired secretion of hepatokines. This study aimed to compare the effects of 12 weeks continuous aerobic, Tabata, and high intensity interval training (HIIT) on the levels of fetuin-A, fetuin-B, fibroblast growth factor 21 (FGF-21), plasminogen activator inhibitor-1 (PAI-1), fibrinogen-like protein 1 (FGL1), and selenoprotein P. Methods: 44 obese males were randomly divided into four groups (n=11): control group (CT), endurance training group (ET), Tabata group (Tabata), and HIIT group (HIIT). Exercise training programs lasted for 12 weeks/three sessions per week. Each session lasted 60 minutes, containing warm-up (10 minutes), main training for each group (40 minutes), and ending with cooling down (10 minutes). Measurements were made12 and 48 h before the initiation of the main interventional protocols, and then again 48 h after the final session of the training protocol. Results: Baseline data were similar in all groups (p>0.05). There was a significant group-by-time interaction for fetuin B (p=0.0004), FGF-21 (p=0.007), FGL-1 (p=0.0139), weight (p=0.0110), BMI (p=0.009), %fat (p<0.0001) and selenoprotein-1 (p=0.0052). There was a main effect of time for fetuin A and PAI-1 (p<0.0001). The reductions in fetuin B were greater in HIIT vs. CT (mean diff: -0.13) and Tabata vs. CG (mean diff: -0.14) (p<0.05), while decreases in FGF-21 were greater in ET vs. CT (mean diff: -19.1), HIIT vs. CT (mean diff: -26.22)(p<0.05), and Tabata vs. CT (mean diff: -22.8). Reductions in FGL-1 were greater in ET vs. CT (mean diff: -11.5)(p<0.05), HIIT vs. CT (mean diff: -8.1), and Tabata vs. CT (mean diff: -11.3)(p<0.05). Conclusion: Performing 12 weeks of physical activity led to decreases in weight, BMI, %fat, fetuin-B, FGF-21, FGL-1, and selenoprotein P, and greater reduction observed in the Tabata and the HIIT groups.
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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) | 0.002 | 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".