Effect of Pistachios on Postprandial Glucose and Insulin Levels and Gut Satiety Hormone Responses.
Bibliographic record
Abstract
Background Nut consumption is associated with reduced risk of CHD and type 2 diabetes and healthy body weights. Previous studies demonstrated that pistachios decrease the postprandial glycemic response of carbohydrate foods. Objective To assess the effect of pistachios on postprandial serum insulin/glucose levels and gut satiety hormone responses. Methods Fasted subjects (9M, 6F) consumed 3 different meals in random order. Test meals, with similar macronutrient profiles, consisted of white bread (WB) plus 2oz of pistachios, and; WB plus butter and cheese. The control meal was WB. Capillary finger‐prick and venous blood samples were taken and subjective satiety measures via a visual analogue scale were assessed over 3 hours. Results Compared to the control, peak postprandial glucose concentration was reduced in both test meals while plasma insulin was not different. However, GIP iAUC for WB was significantly lower than for the test meals. GLP‐1 levels were consistently higher after WB+Pistachio meal compared to other meals and GLP‐1 iAUC of both WB+Pistachio and WB+Cheese meals were significantly higher than WB control meal. Despite absence of changes in VAS satiety scores, several hunger markers were increased over the first hour in the control meal. Conclusion Reducing postprandial glycemia and altering gut hormones may be further mechanisms by which pistachios contribute to health.
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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 | 0.000 |
| 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) | 0.003 | 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".