Antihypertensives, hypertension, and the risk of cancer
Bibliographic record
Abstract
Hypertension and antihypertensive drugs have been linked to the risk of cancer for over 3 decades.In this thesis, we address some of the weaknesses of studies to date by conducting a population based observational study using the Saskatchewan Health databases.We followed 77887 subjects initiating antihypertensives between 1980 and 1987 until the end of 2004.A case-control analysis nested within this cohort revealed a small decrease in the risk of cancers at all sites combined in β-blocker users compared to thiazide diuretic users (OR incident 0.9; 95% CI 0.85-0.96,fatal 0.91; 0.83-0.99)driven in large part by a decrease in the risk of colon cancer (OR incident 0.79; 0.67-0.93,fatal 0.74; 0.57-0.97).No other differences in risk were observed between users of commonly prescribed classes of antihypertensives for all cause and common cancers.In a subcohort of 42270 subjects who started using antihypertensive drugs on a regular basis for hypertension, those who started regular use under the age of 60 years had an increased risk of cancer at all sites combined (RR incident 1.34; 1.18-1.52,fatal 1.21; 1.08-1.36)compared to the general population while those who started when they were 60 years and older had a decreased risk (RR incident 0.88; 0.78-0.98,fatal 0.88; 0.80-0.97).Rate ratios were elevated or diminished across most sites of cancer and remained unchanged after excluding cancers in the first 10 years of follow up.However, neglecting minor differences between how cancer rates are derived in the general population versus an exposed cohort of interest led to substantial bias that may explain some of the discrepancies in the literature to date.We conclude that essential hypertension is associated with a modest increase risk of cancer but isolated systolic hypertension appears to be associated with a modest decreased risk.The former association is not due to protopathic or detection bias but may be, at least in part, due to residual confounding from body mass index and alcohol use.Our findings also offer reassurance to antihypertensive drug users with respect to cancer risk.Finally, having conducted the first external comparison study using the Saskatchewan cancer registry, we provide some methodological guidance to investigators who may be interested in performing a similar study in the future.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".