Tree Nut and Peanut Consumption and Risk of Cardiovascular Disease: A Systematic Review and Meta-Analysis of Randomized Controlled Trials
Bibliographic record
Abstract
Cardiovascular disease (CVD) is the leading cause of death globally. Habitual consumption of tree nuts and peanuts is associated with cardioprotective benefits. Food-based dietary guidelines globally recommend nuts as a key component of a healthy diet. This systematic review and meta-analysis were conducted to examine the relationship between tree nut and peanut consumption and risk factors for CVD in randomized controlled trials (RCTs) (PROSPERO: CRD42022309156). MEDLINE, PubMed, CINAHL, and Cochrane Central databases were searched up to 26 September, 2021. All RCT studies that assessed the effects of tree nut or peanut consumption of any dose on CVD risk factors were included. Review Manager software was used to conduct a random effect meta-analysis for CVD outcomes from RCTs. Forest plots were generated for each outcome, between-study heterogeneity was estimated using the I2 test statistic and funnel plots and Egger’s test for outcomes with ≥10 strata. The quality assessment used the Health Canada Quality Appraisal Tool, and the certainty of the evidence was assessed using grading of recommendations assessment, development, and evaluation (GRADE). A total of 153 articles describing 139 studies (81 parallel design and 58 cross-over design) were included in the systematic review, with 129 studies in the meta-analysis. The meta-analysis showed a significant decrease for low-density lipoprotein (LDL) cholesterol, total cholesterol (TC), triglycerides (TG), TC:high-density lipoprotein (HDL) cholesterol, LDL cholesterol:HDL cholesterol, and apolipoprotein B (apoB) following nut consumption. However, the quality of evidence was “low” for only 18 intervention studies. The certainty of the body of evidence for TC:HDL cholesterol, LDL cholesterol:HDL cholesterol, and apoB were “moderate” because of inconsistency, for TG were “low,” and for LDL cholesterol and TC were “very low” because of inconsistency and the likelihood of publication bias. The findings of this review provide evidence of a combined effect of tree nuts and peanuts on a range of biomarkers to create an overall CVD risk reduction.
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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 | 0.020 | 0.046 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.025 | 0.040 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 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".