Glycemic Index and Glycemic Load and Liver Enzyme Activity
Bibliographic record
Abstract
Objective The relationship between non‐alcoholic fatty liver disease (NAFL) and insulin resistance, suggests that dietary interventions to reduce postprandial glycemia and insulin demand, i.e. low glycemic index (GI) and low glycemic load (GL) diets, may be relevant to NAFL. Methods Liver enzymes (ALT and AST) were measured in two 3‐month clinical trials of low GI or GL versus control (high cereal fiber) diets in participants with type 2 diabetes (n=212). Baseline liver enzymes were also measured in three additional studies of type 2 diabetes (n=299). Results In study 1, the low GI diet resulted in significant reductions in both liver enzymes with a greater reduction for AST when compared to the control diet (P<0.05). In study 2, the low GL diet demonstrated significant reductions in both liver enzymes, while the control diet significantly reduced AST. However, the reductions in AST were significantly greater on the low GL diet compared to control (P<0.05). Using baseline data from studies 1 and 2 as well as 3 additional studies, baseline correlations between liver enzymes and markers of metabolic syndrome revealed significant positive correlations for both AST and ALT with diastolic blood pressure and with triglycerides, and for ALT with fasting glucose, HbA1c and systolic blood pressure (P<0.05). Of the dietary factors correlated with liver enzymes, only dietary cholesterol was positively associated with AST and ALT (P<0.05). Conclusion Lower GI and GL diets improved liver enzymes and thus may play a role in reducing the risk of NAFL. Funding: Barilla
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".