Impact of Polyphenol Supplementation on Energy Expenditure Measured by Indirect Calorimetry in Adolescents with Metabolic Dysfunction-Associated Steatotic Liver Disease: A Pilot Randomized Study
Bibliographic record
Abstract
BACKGROUND: Metabolic dysfunction-associated steatotic liver disease (MASLD) is increasingly prevalent among adolescents, especially those with obesity. It is the leading cause of liver-related morbidity and mortality and can progress to metabolic dysfunction-associated steatohepatitis, and eventually irreversible cirrhosis. There is currently no medical treatment recommended for MASLD in adolescents. Nutritional interventions, such as polyphenol supplementation, could be a non-pharmacological option to improve metabolic outcomes. OBJECTIVES: This pilot study aimed to preliminarily assess the impact of a 60-day polyphenol supplementation on measured resting energy expenditure (mREE) by indirect calorimetry (IC) in adolescents with MASLD. It also compared mREE by IC with predicted resting energy expenditure (pREE) using the WHO and Schofield formulae. METHODS: This single-blind randomized controlled trial enrolled 23 adolescents with MASLD, of which 11 completed IC assessments before and after the 60-day polyphenol supplementation (intervention group, n = 5) or no supplementation (controls, n = 6). There was no placebo. Caloric intake was assessed to evaluate its impact on mREE and mREE was compared to pREE using the WHO and Schofield equations. RESULTS: = 0.021). No significant changes were observed when adjusting mREE for body weight. Also, there were no significant changes in body weight in the two groups between the visits. Both the WHO and Schofield equations overestimated pREE with an average percentage of pREE of 88.8% and 91.0%, respectively. CONCLUSIONS: Although several methodological limitations prevent clear conclusions from being drawn at this stage, this study suggests that polyphenol supplementation could increase REE in adolescents with MASLD and that the WHO and Schofield equations tend to overestimate REE in obese patients.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".