Association Between Subclinical Magnesium Deficiency and Major Cardiovascular Events in Adults: A Meta-Analysis
Bibliographic record
Abstract
Background: Magnesium is a crucial mineral involved in cardiovascular regulation, yet subclinical deficiency remains highly prevalent. Although prior observational studies suggest an association between hypomagnesemia and cardiovascular outcomes, evidence remains inconsistent regarding risk thresholds and mediating pathways. This meta-analysis aimed to evaluate the association between subclinical magnesium deficiency and major cardiovascular events in adults. Methods: A systematic search of PubMed, Embase, Web of Science, and Cochrane Library was conducted from inception until June 2024 in accordance with PRISMA guidelines. Eligible studies included prospective cohort designs assessing baseline serum magnesium concentrations and subsequent cardiovascular outcomes in adults (≥18 years) with a minimum follow-up of five years. Data extraction and quality assessment were independently performed using the Newcastle–Ottawa Scale. Random-effects models estimated pooled hazard ratios (HRs) with 95% confidence intervals (Cis). Subgroup analyses were conducted by exposure threshold (<0.70 vs. ≤0.80 mmol/L), adjustment for hypertension/diabetes, and follow-up duration. Results: This meta-analysis included three prospective cohorts with 37,733 participants and follow-ups of 8.7–28.6 years. Low serum magnesium consistently correlated with elevated cardiovascular risk. In the Rotterdam Study, magnesium ≤0.80 mmol/L increased coronary heart disease mortality by 36% (HR 1.36, 95% CI 1.09–1.69) and sudden cardiac death by 54% (HR 1.54, 95% CI 1.12–2.11). NHANES I Follow-up showed magnesium <0.70 mmol/L doubled stroke mortality risk (HR 2.55, 95% CI 1.18–5.48). The ARIC study linked higher magnesium to reduced ischemic stroke risk (HR 0.70, 95% CI 0.56–0.88). Conclusion: Subclinical hypomagnesemia independently predicts major cardiovascular events, highlighting its clinical importance.
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.056 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".