Proton pump inhibitors linked to hypomagnesemia: a systematic review and meta-analysis of observational studies
Bibliographic record
Abstract
BACKGROUND: The reported risk of hypomagnesemia in patients with proton pump inhibitor (PPI) use is conflicting. The objective of this meta-analysis was to assess the association between the use of PPIs and the risk of hypomagnesemia. METHODS: A literature search of observational studies was performed using MEDLINE, EMBASE and Cochrane Database of Systematic Reviews from inception through September 2014. Studies that reported odd ratios or hazard ratios comparing the risk of hypomagnesemia in patients with PPI use were included. Pooled risk ratios (RRs) and 95% confidence interval (CI) were calculated using a random-effect, generic inverse variance method. RESULTS: Nine observational studies (three cohort studies, five cross-sectional studies and a case-control study) with a total of 109,798 patients were identified and included in the data analysis. The pooled RR of hypomagnesemia in patients with PPI use was 1.43 (95% CI, 1.08-1.88). The association between the use of PPIs and hypomagnesemia remained significant after the sensitivity analysis including only studies with high quality score (Newcastle-Ottawa scale score ≥ 8) with a pooled RR of 1.63 (95% CI, 1.14-2.23). CONCLUSIONS: Our study demonstrates a statistically significant increased risk of hypomagnesemia in patients with PPI use. The finding of this meta-analysis of observational studies suggests that PPI use is associated with hypomagnesemia and may impact clinical management of patients who are taking PPIs and at risk for hypomagnesemia related cardiovascular events.
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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 | 0.017 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.034 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".