Association Between Gastroesophageal Reflux Disease and Hypertension: A Systematic Review and Meta‐Analysis
Bibliographic record
Abstract
ABSTRACT Background Gastroesophageal reflux disease (GERD) and hypertension (HTN) are common conditions with substantial health burdens. While prior studies suggest a link between GERD and HTN, findings remain inconsistent. This systematic review and meta‐analysis aimed to clarify the relationship between GERD and HTN. Method A systematic search of PubMed, Web of Science, and Embase was conducted to identify observational studies published through December 2024. Studies evaluating the prevalence and association between GERD and HTN were included. Screening and data extraction were performed using Nested Knowledge software, with quality assessed via the Modified Newcastle–Ottawa Scale. Random‐effects meta‐analyses were used to calculate pooled prevalence and risk estimates, while heterogeneity was quantified using the I2 statistic. Publication bias was evaluated using DOI and funnel plots. Results Twelve studies, with sample sizes ranging from 71 to 12 960 participants, were included. The pooled prevalence of HTN among GERD patients was 16.80% (95% CI: 6.28%–31.02%) with high heterogeneity (I2 = 99%). The pooled risk ratio (RR) for HTN was 1.381 (95% CI: 0.992–1.922) and heterogeneity (I2 = 76%) highlighted variability. Sensitivity analyses and publication bias were evident. Conclusion GERD is a potential risk factor for HTN, with GERD patients demonstrating an elevated likelihood of developing HTN. Future research is required to investigate the underlying mechanisms and confounding factors that may influence this association.
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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 | 0.014 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.036 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".