Will Proton Pump Inhibitors Increase the Risk of Diabetes Mellitus? A Systemic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Proton pump inhibitor use was reported to potentially provide benefits to prevent diabetes mellitus. This study aims to investigate the association between proton pump inhibitor use and the risk of developing diabetes mellitus. METHODS: This study was registered on the International Prospective Register of Systematic Reviews (PROSPERO) (ID: CRD42021238481). A systematic literature search was conducted to identify eligible studies up to February 2021. Quality assessment was conducted according to Jadad Scoring Scale and Newcastle-Ottawa Scale. The heterogeneity among studies was tested and estimated by Q test and I2. Pooled hazard ratio with 95% CI was calculated using the random-effects or fixed-effects model depending on the heterogeneity. Subgroup analyses, sensitivity analysis, and publication bias assessment were also performed. RESULTS: Eight studies including 850 019 participants were included. We found that proton pump inhibitor use was associated with a statistically non-significant increased risk of diabetes mellitus (pooled hazard ratio was 1.06, 95% CI = 0.89-1.28, P = .50). In subgroup analysis, 5 studies conducted in North America confirmed the overall result; however, one study conducted in Europe demonstrated a statistically significant increased risk, while one study in Asia revealed a statistically significant decreased risk. CONCLUSION: Proton pump inhibitor use is not associated with either increased or decreased risk of diabetes mellitus. However, more well- designed studies focusing on proton pump inhibitor use and the risk of diabetes mellitus, especially among populations with different backgrounds, are still needed.
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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 | 0.018 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.042 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".