Evidence‐based review of the nutritional treatment of obesity and metabolic dysfunction‐associated steatotic liver disease in children and adolescents
Bibliographic record
Abstract
The growing pediatric obesity epidemic has paralleled the surge in metabolic dysfunction-associated steatotic liver disease (MASLD) and metabolic dysfunction-associated steatohepatitis. It develops due to nutritional imbalances, microbiome dysbiosis, gene regulation, hormonal changes, and environmental factors like food deserts, low activity level, and an unhealthy lifestyle. The prevalence of MASLD and obesity is rising every year. Lifestyle changes remain the mainstay treatment for obesity and MASLD. Per the 2023 American Association for the Study of Liver Diseases Practice Guidance on MASLD, achieving ≥5% weight loss can reduce hepatic steatosis, ≥7% weight loss can reduce hepatic inflammation, and ≥10% weight loss can reduce liver fibrosis. Therefore, nutritional interventions can be a powerful tool to help correct metabolic dysfunction and promote healthy weight loss. Current endorsed nutritional interventions for weight loss or MASLD include the Mediterranean diet, low glycemic/low carbohydrate diet, plant-based diet/anti-inflammatory diet, ketogenic diet, and intermittent fasting. This review provides evidence-based insights into current nutritional interventions for children and adolescents with obesity and MASLD to help guide pediatric gastroenterologists in making the best dietary-based recommendations in clinical practice.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".