Tree Nuts Improve Glycemic Control: A Systematic Review and Meta‐Analysis of Randomized Controlled Dietary Trials
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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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 | 0.016 | 0.043 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.030 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".