Dose-dependent effect of nuts on blood pressure: a systematic review and meta-analysis of randomized controlled trials
Bibliographic record
Abstract
Abstract Objective Traditional pairwise meta-analyses indicated that nuts consumption can improve blood pressure. We iamed to determine the dose-dependent effect of nuts on systolic (SBP) and diastolic blood pressure (DBP) in adults. Methods A systematic search was undertaken in PubMed, Scopus, and ISI Web of Science till March 2021. Randomized controlled trials (RCT) evaluating the effects of nuts on SBP and DBP in adults were included. We estimated change in blood pressure for each 20 g/d increment in nut consumption in each trial and then, calculated mean difference (MD) and 95%CI using a random-effects model. We estimated dose-dependent effect using a dose-response meta-analysis of differences in means. The certainty of evidence was rated using the GRADE instrument, with the minimal clinically important difference being considered 2 mmHg. Results A total of 31 RCTs with 2784 participants were included. Each 20 g/d increase in nut consumption reduced SBP (MD: -0.50 mmHg, 95%CI: -0.79, -0.21; I 2 = 12%, n = 31; GRADE = moderate certainty) and DBP (MD: -0.23 mmHg, 95%CI: -0.38, -0.08; I 2 = 0%, n = 31; GRADE = moderate certainty). The effect of nuts on SBP was more evident in patients with type 2 diabetes (MD: -1.31, 95%CI: -2.55, -0.05; I 2 = 31%, n = 6). The results were robust in the subgroup of trials with low risk of bias. Levels of SBP decreased proportionally with the increase in nuts consumption up to 40 g/d (MD 40g/d : -1.60, 95%CI: -2.63, -0.58), and then appeared to plateau with a slight upward curve. A linear dose-dependent reduction was seen for DBP, with the greatest reduction at 80 g/d (MD 80g/d : -0.80, 95%CI: -1.55, -0.04). Conclusions The available evidence provides a good indication that nut consumption can result in a small improvement in blood pressure in adults. Well-designed trials are needed to confirm the findings in long term follow-up.
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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 | 0.021 | 0.049 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.044 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".