Does changing the PUFA content of a high saturated fat meal influence postprandial lipid and lipoprotein expression in children with nonalcoholic fatty liver disease?
Bibliographic record
Abstract
Background Hyperinsulinemia leading to altered lipid metabolism induced by high saturated intakes may contribute to NAFLD. We hypothesized that consumption of a ↑saturated/↓PUFA meal would evoke a postprandial metabolic environment characterized by prolonged hyperinsulinemia, hypertriglyceridemia and hypercholesterolemia when compared to a ↑saturated/↓ PUFA meal in childhood NAFLD. Methods We studied 36 children (n=15 NAFLD; n=21 lean controls) following consumption of two meals: ↑saturated/↓ PUFA vs↑saturated/↓PUFA that were equivalent except for PUFA content (2.8% vs.4.9%). Blood for analysis of total‐HDL‐LDL‐cholesterol, triglycerides (TG), glucose, insulin, non‐esterifiedfatty‐ acids (NEFA), Apo‐B48, ApoB‐100, ApoC‐III and ALT (fasting) was collected at 0, 1, 3 and 6 hr. Data was analyzed using the trapezoidal method for area under the curve (AUC)/iAUC. Results : Mean age and BMI‐z (±SD) of participants was 13.4±2.8 and 2.3±0.4 (NAFLD); and 13.8±2.6 and 0.1±1.0 (lean). Mean insulin AUC (371 ±238 vs. 82±26) and iAUC (210 ± 179 vs. 41 ±20) for NAFLD was significantly higher than for controls (p<0.05). No significant differences in AUC/iAUC for insulin, TG, total‐and‐LDL cholesterol or ApoB48 was observed between meal types (p>0.05), although significant differences in iAUC NEFA (1.27±0.91 (↓PUFA) vs. 0.42±0.17 (↑PUFA), HDL‐cholesterol and ApoB‐100 by meal type in children with NAFLD and lean controls were observed (p<0.05). Conclusions : Children with NAFLD experience postprandial hyperinsulinemia and marked changes in lipid, cholesterol, and lipoprotein expression when compared to lean children when fed meals with high saturated fat with varying PUFA content.
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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".