Effect of tree nuts on features of the metabolic syndrome: A systematic review and meta‐analysis of randomized controlled dietary trials
Bibliographic record
Abstract
Objective To assess the effect of tree nuts on features of the metabolic syndrome (MetS), we conducted a systematic review and meta‐analysis of randomized dietary trials. Methods We searched MEDLINE, EMBASE, CINAHL, and the Cochrane Library (through May 25, 2012). We included relevant randomized dietary trials of ≥ 3 weeks reporting all 5 features of MetS (waist circumference, fasting glucose, triglycerides, HDL‐cholesterol [HDL‐C], blood pressure [BP]). Two independent reviewers extracted all relevant data. Data were pooled using the generic inverse variance method and expressed as mean differences (MD) with 95% CI. Heterogeneity was assessed by Cochran's Q and quantified by I2. Study quality was assessed by the Heyland score. Results 7 trials (n=1018) met the eligibility criteria. Diets high in tree nuts lowered triglycerides compared with control diets low in tree nuts (MD = −0.09 mmol/L [95% CI, −0.17, −0.01]). There was no effect on waist circumference, HDL‐C, BP, and fasting glucose, with evidence of heterogeneity in all analyses. Limitations Most of the trials were of short duration (<12 months) and of poor quality. Conclusion Pooled analyses show that diets high in tree nuts lower triglycerides. Longer and higher quality trials are warranted. Clinicaltrials.gov identifier: NCT01630980 Funding source: International Nut Council
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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 | 0.025 | 0.054 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.026 | 0.032 |
| Bibliometrics | 0.009 | 0.009 |
| 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) | 0.004 | 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".