Magnesium Supplementation and Blood Pressure: A Systematic Review and Meta-Analysis of Randomized Controlled Trials
Bibliographic record
Abstract
BACKGROUND: There are inconsistent reports regarding the effect of magnesium intake on blood pressure (BP) across hypertensive and normotensive populations. METHODS: We performed a meta-analysis and dose-response analysis to explore the relationship between magnesium supplementation and BP in randomized-controlled trials with a duration of ≥4 weeks, using a cubic spline regression model. RESULTS: Thirty-eight randomized controlled trials involving 2709 participants were eligible for inclusion. Studies included an elemental magnesium dose from 82.3 mg to 637 mg with a median dose of 365 mg and a median intervention period of 12 weeks. Mean differences of changes in BP were calculated by random effects meta-analysis. Magnesium intake resulted in a reduction in systolic BP of −2.81 mm Hg (95% CI, −4.32 to −1.29) and diastolic BP by −2.05 mm Hg (95% CI, −3.23 to −0.88) compared with placebo. Hypertensive individuals on BP-lowering medication and individuals with hypomagnesemia yielded greater systolic BP reductions of −7.68 and −5.97 mm Hg, respectively ( P <0.05), and diastolic BP reductions of −2.96 and −4.75 mm Hg, respectively ( P <0.05). In normotensive groups, statistical significance was not reached. We identified high heterogeneity across studies. We found no dose-response relationship between magnesium and BP changes (all P ≥0.20). CONCLUSIONS: Our findings support the beneficial effect of magnesium on reducing BP among populations with hypertension and hypomagnesemia, although effects should be interpreted with caution due to high heterogeneity of studies. Larger, well-designed studies assessing higher magnesium doses are needed to refine the dose-response relationship between magnesium intake and BP and identify potential optimal supplementation strategies for subpopulations.
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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 | 0.019 | 0.056 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.033 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 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".