Effects of nuts on glycemic control and coronary heart disease risk factors in type 2 diabetes
Bibliographic record
Abstract
Background Nut consumption, including peanuts, has been associated with a reduced risk of coronary heart disease (CHD). More recently, interest has grown in the potential value of nuts in diets of individuals with diabetes. Objective To determine if tree nuts and peanuts improve glycemic control and CHD risk factors in type 2 diabetes. Methods 117 subjects with type 2 diabetes were randomized to a 3‐month parallel design study. Subjects were randomized to one of three treatments: 1) Test (Full Dose Nut Diet): 75g/d for 2,000kcal/d; 2) Test (Half Dose Nut Diet): half‐dose of nuts and half‐dose of control muffin; and 3) Control: whole wheat muffins matched with energy content of nut supplements. Fasting blood samples were collected at baseline and weeks 2, 4, 8, 10 and 12 for markers of glycemic control and CHD risk factors. Results Compared to the control, the full dose nut supplement significantly lowered HbA1c (P=0.039), total‐C (P=0.002), LDL‐C (P=0.007), total‐C:HDL‐C (P=0.015), and LDL‐C:HDL‐C (P=0.028). The half‐dose of nuts did not result in any significant improvements in glycemic control or blood lipids. Conclusions The addition of nuts to the diet improves glycemic control and reduces blood lipid CHD risk factors in type 2 diabetes. (Funded by Canada Research Chair Endowment; International Tree Nut Council Nutrition Research & Education Foundation; The Peanut Institute)
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".