MétaCan
Menu
Back to cohort

Acute effects of pistachios on glucose, insulin, gut hormones and satiety in persons with metabolic syndrome

2012· article· en· W3176335723 on OpenAlexaff
Cyril W.C. Kendall, Janice Campbell, Alexandra L. Jenkins, David J.A. Jenkins

Bibliographic record

VenueThe FASEB Journal · 2012
Typearticle
Languageen
FieldNursing
TopicNuts composition and effects
Canadian institutionsGlycemic Index LaboratoriesUniversity of Toronto
Fundersnot available
KeywordsPostprandialMealMedicineFood scienceMetabolic syndromeDiabetes mellitusInsulinCarbohydrateInternal medicineEndocrinologyBiology

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.655
Threshold uncertainty score0.349

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.007
GPT teacher head0.230
Teacher spread0.223 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueThe FASEB JournalSame topicNuts composition and effectsFrench-language works237,207