Tree Nuts Improve Glycemic Control: A Systematic Review and Meta‐Analysis of Randomized Controlled Dietary Trials
Why this work is in the frame
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Bibliographic record
Abstract
Background Tree nut consumption is associated with reduced diabetes risk, however, results from randomized controlled trials (RCTs) on glycemia have been inconsistent. Aim We conducted a systematic review and meta‐analysis of RCTs to assess the effect of tree nuts on glycemic control. Methods We searched MEDLINE, EMBASE, CINAHL, and Cochrane databases through 8 August 2014 for relevant RCTs 蠅3‐weeks reporting HbA1c, fasting glucose (FBG), fasting insulin (FPI), and/or HOMA‐IR. Two independent reviewers extracted relevant data. Data were pooled using generic inverse variance random effects models and expressed as mean differences (MD) with 95% confidence intervals (CI). Heterogeneity was assessed (Cochran's Q) and quantified (I 2 ). Results 31 trials (n=1645) met the eligibility criteria. Diets emphasizing tree nuts significantly lowered FBG (MD=‐0.11 mmol/L, 95% CI:‐0.18, ‐0.03 mmol/L), FPI (MD=‐4.79 pmol/L, 95% CI:‐8.12, ‐1.46 pmol/L) and HOMA‐IR (MD=‐0.45, 95% CI:‐0.81, ‐0.09) compared with isocaloric control diets. No effects were observed for HbA1c, however the direction of effect favoured tree nuts. Limitations Majority of trials were of poor quality and short duration. Conclusion Pooled analyses show diets higher in tree nuts improve glycemic control. Longer, higher quality trials are needed. Funding International Tree Nut Council Nutrition Research & Education Foundation
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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 | 0.042 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.070 | 0.020 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it