A high glycemic index diet: a contribution to the pathogenesis of childhood NAFLD?
Bibliographic record
Abstract
Nonalcoholic fatty liver disease (NAFLD; hepatic large‐droplet steatosis ± inflammation ± fibrosis) can cause serious liver disease in children. Most children with NAFLD have elevated BMI; some have elevated serum aminotransferases and hyperlipidemia. Hyperinsulinemia with variable insulin resistance appears to be a critical feature of disease mechanism. Our hypothesis is dietary pattern elicits hyperinsulinemia and contributes to the hepatic disease process. We prospectively studied 20 children clinically diagnosed with NAFLD. Body composition, liver biochemistries, fasting glucose, insulin, and lipid profiles were obtained. Insulin resistance was assessed using the homeostasis model of insulin resistance (HOMA‐IR). Three‐day food intake records were collected to assess the nutrient content of the diet. Mean age (±SD) of study participants was 14.1 (±3.1) yrs. 14 out of 20 children were obese (BMI: 29.9 ±5.5). Patients had elevations of ALT (117 ± 98 U/L), triglycerides (1.9 ± 0.6 mmol/L) and HOMA‐IR scores (>2) consistent with insulin resistance. HOMA‐IR scores correlated to increased plasma triglycerides (p=0.02), BMI (p=0.006), dietary glycemic index (p=0.04) and sucrose intake (p=0.05). Diets were high in saturated fat (11.6% ± 2.3), glycemic load (137.2 ±42.9) and low in polyunsaturated fat (4.6% ± 2.3), vitamin E (4.4 ± 3.6 mg) and fiber (14.7 ± 5.6). Children with NAFLD consume a diet that generates a metabolic environment leading to insulin resistance. Nutritional strategies aimed at lowering dietary glycemic index and sucrose intake represent a physiologically based intervention for childhood NAFLD. (Supported by CIHR/CAG/Nestle).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".