Effect of Almonds on Insulin Secretion and Insulin Resistance: A Randomized Controlled Cross‐over Trial
Bibliographic record
Abstract
Background: Cohort studies have shown that nut consumption is strongly associated with a reduced risk of coronary heart disease (CHD). Intervention studies demonstrate that almonds reduce serum LDL‐C. However, their documented reduction in LDL‐C can only explain a proportion of the reduced CHD risk. Methods: Whole almonds, taken as snacks, were compared to low‐saturated fat (<5% energy) whole‐wheat muffins (control) in the therapeutic diets of subjects with hyperlipidemia. Twenty seven hyperlipidemic men and women each consumed three iso‐energetic (mean 423kcal/d) supplements for 1 month. Supplements consisted of full‐dose almonds (73±3g/d), half‐dose almonds plus half‐dose muffins, and full‐dose muffins. In each phase, subjects were assessed at weeks 0, 2 and 4. Results: No baseline or treatment differences were seen in fasting serum glucose, insulin, C‐peptide or calculated insulin resistance (HOMA‐IR). However, creatinine corrected, 24‐hour urinary C‐peptide output, a marker of daily insulin secretion, was significantly reduced on both the half‐ and full‐almond doses compared to the control (P=0.002 and P=0.004, respectively). Conclusion: Almond consumption reduced 24‐hour insulin secretion. This may be a further metabolic advantage of nuts that in the long‐term may help to explain the strong association between nuts and CHD risk reduction. Funding 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".