Almonds, Glycemic Index, Dietary Antioxidants and Risk Factors for Coronary Heart Disease
Bibliographic record
Abstract
Background Low glycemic index (GI) diets may be of benefit in reducing risk of coronary heart disease (CHD) and diabetes, possibly by reducing postprandial glucose, insulin and oxidative stress. Objective To assess whether almonds reduce the glycemic response, insulinemia, and oxidative stress more than the same reduction by a low GI food (parboiled rice) without endogenous antioxidants. Methods Two studies were undertaken. The first study (n=10) assessed the dose‐response effect of almonds in reducing postprandial glycemia. The second study (n=15) assessed a high GI meal (mashed potatoes) compared to two low GI meals (parboiled rice, almonds) on postprandial glucose, insulin and measures of oxidative stress. All meals contained 50g available carbohydrates and were balanced for protein and fat. Results The first study found that 60g almonds significantly reduced the glycemic response of white bread. In the second study, the glycemic responses (mean±SE) of the low GI almond (54.5±6.9, P<0.001) and rice (37.5±6.3, P<0.001) meals were significantly reduced compared to white bread, while the high GI potato (94.3±11.1, P=0.61) meal showed no significant difference. Insulin values reflected the glycemic responses. Protein thiols tended to be higher following the almond meal indicating less oxidative damage. Conclusion Low GI foods lower postprandial glycemic and insulinemic response curves, and tend to reduce oxidative stress. Preliminary data indicate that low GI foods as part of a healthy diet may reduce risk factors for CHD and diabetes. Support: Almond Board of California
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".