Exercise dose on hepatic fat and cardiovascular health in adolescents with excess of adiposity
Bibliographic record
Abstract
OBJECTIVE: The HEPAFIT study was aimed at examining the impact of a 6-month physical education intervention, considering various levels of exercise intensity, on hepatic fat and cardiometabolic health outcomes in adolescents with excess adiposity. METHODS: Adolescents (n = 120), 11-17 years with excess adiposity by body fat >30%, were randomly assigned to one of the following 4 groups for 6 months: (1) standard physical education lessons, control (CTRL); (2) high-intensity physical education (HIPE); (3) low-to-moderate intensity physical education (LIPE) and (4) combined HIPE and LIPE (PLUS). The primary outcome was hepatic fat content measured by vibration-controlled transient elastography (controlled attenuation parameter [CAP]). Secondary outcomes were traditional cardiovascular health markers (body composition, serum lipids, aminotransferases and health-related physical fitness components). RESULTS: Adjusted mixed effects linear models revealed a significant decrease in CAP levels in HIPE (-20.02 dB/m, p < 0.0001) (p = 0.001 vs. CTRL group) and PLUS (-16.25 dB/m, p = 0.005) groups. Body fat decreased in the HIPE (-2.88%, p < 0.001) (p = 0.001 vs. CTRL group) and LIPE (-1.26%, p = 0.022) groups. The physical fitness components were increased in the HIPE and PLUS group relative to the baseline (p < 0.05), and the HIPE group showed a reduction in the total cholesterol and low-density lipoprotein cholesterol (LDL-C) levels (p < 0.05). CONCLUSIONS: Implementation of a 6-month physical education exercise program, particularly high-intensity or combined high and low-intensity, improves hepatic fat storage and significantly reduces cardiometabolic markers in adolescents with excess of adiposity. Interventions involving supervised physical exercise may help to improve metabolism and fat deposition at the hepatic level, thus preventing the development of non-alcoholic fatty liver disease in adolescents.
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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 | 0.001 |
| 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".