Acute effects of raisin consumption on glucose and insulin reponses in healthy individuals
Bibliographic record
Abstract
Raisins are popular snacks with a favourable nutrient profile, being high in dietary fibre, polyphenols and a number of vitamins and minerals, in addition to being rich in fructose. In light of evidence demonstrating improvements in glycaemic control with moderate fructose intake and low-glycaemic index (GI) fruits, our aim was to determine the GI, insulin index (II) and postprandial responses to raisins in an acute feeding setting. A total of ten healthy participants (four male and six female) consumed breakfast study meals on four occasions over a 2- to 8-week period: meal 1: white bread (WB) (108 g WB; 50 g available carbohydrate) served as the control and was consumed on two separate occasions; meal 2: raisins (R50) (69 g raisins; 50 g available carbohydrate); and meal 3: raisins (R20) (one serving, 28 g raisins; 20 g available carbohydrate). Postprandial glucose and insulin were measured over a 2 h period for the determination of GI, glycaemic load (GL) and II. The raisin meals, R50 and R20, resulted in significantly reduced postprandial glucose and insulin responses when compared with WB (P < 0·05). Furthermore, raisins were determined to be low-GI, -GL and -II foods. The favourable effect of raisins on postprandial glycaemic response, their insulin-sparing effect and low GI combined with their other metabolic benefits may indicate that raisins are a healthy choice not only for the general population but also for individuals with diabetes or insulin resistance.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| 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".