Acute effects of pistachios on glucose, insulin, gut hormones and satiety in persons with metabolic syndrome
Bibliographic record
Abstract
Background Nut consumption has been found to decrease risk of CHD and diabetes, and to promote healthy body weights, possibly related to their favorable macronutrient profile. Methods 20 subjects with metabolic syndrome as defined by NCEP ATP III guidelines were recruited. Each subject participated in a total of 5 breakfast study meals over 5–10wks. Study meal order was randomized. Meals were consumed after an overnight fast. Meal 1 was a control meal of white bread (50g available CHO). Meals 2 (white bread, butter and cheese) and 3 (white bread plus 3oz pistachios) had similar macronutrient profiles. Meals 4 (white bread) and 5 (3oz pistachios alone) had the same amount of available CHO (12g). Results and Conclusions The addition of pistachios to a carbohydrate meal decreased postprandial glucose levels similar to other sources of fat and protein but may have insulin sparing properties. Pistachios consumed alone appeared to increase GIP and GLP‐1 levels. Both insulin sparing and increased GLP‐1 levels associated with pistachio consumption may be beneficial properties for individuals with diabetes and metabolic syndrome. Supported by the American Pistachio Growers
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".