Antihypertensive medications and cancer risk: Evidence from 0.27 million patients with newly diagnosed hypertension
Bibliographic record
Abstract
Background Whether specific antihypertensive treatments increase cancer risk in patients with hypertension is still controversial. We aimed to estimate the associations of different antihypertensive treatments with cancer risk in real-world settings. Methods A longitudinal cohort study was designed in a population of 1.2 million individuals from the CHinese Electronic health Records Research in Yinzhou (CHERRY). Propensity score matching (PSM) and the Cox regression model were used to estimate the associations. Several sensitivity analyses were then performed to reduce potential residual confounding. Results From 2009 to 2019, a total of 270,320 patients with newly diagnosed hypertension were included in this study. With a median follow-up time of 7.7 years, 14,264 cases of cancer occurred. There were no significant associations of angiotensin-converting enzyme inhibitors (ACEIs), angiotensin receptor blockers (ARBs), β-blockers, or thiazide diuretics (TDs) with cancer risk (p > 0.05). Compared with other antihypertensive treatments, the use of calcium channel blockers (CCBs) was significantly associated with a marginally mild increase in the risk of all cancers (hazard ratio, HR = 1.05; 95% CI: 1.01, 1.09; p = 0.017). However, this association was no longer observed in sensitivity analyses excluding patients with less than 1, 2, or 3 years of follow-up. Nevertheless, the association between CCBs and the risk of endocrine cancer, especially thyroid cancer, still exists. Conclusion Despite previous controversy, in this study, we found no clinically meaningful cancer risk associated with antihypertensive medications. However, the association of CCBs with specific cancer still requires further research. These findings should be interpreted with caution due to the potential residual confounding.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".