EFFECTS OF BLOOD PRESSURE-LOWERING ON CANCER RISK: AN INDIVIDUAL PARTICIPANT DATA META-ANALYSIS OF 300,000 PARTICIPANTS
Bibliographic record
Abstract
Objective: Evidence for the effects of pharmaceutical blood pressure (BP)-lowering on cancer risk is inconsistent and based on observational data. We therefore investigated the effect of BP-lowering on the risk of cancer in a large collaborative study. Design and method: We included randomised trials participating in the Blood Pressure-Lowering Treatment Trialists’ Collaboration. Placebo-controlled trials, drug class comparison trials and trials comparing more-vs-less intensive BP-lowering that provided individual-level participant data (IPD) on cancer events were pooled. We investigated the effects of BP reduction on cancer risk by conducting one-stage IPD meta-analyses using Cox proportional hazard models, stratified by trial and accounting for competing risks. We also investigated effects stratified by age, gender, body mass index (BMI), smoking and previous antihypertensive use at baseline. Results: This analysis included 300,098 participants (42% women) from 39 trials. At baseline, the mean age of participants was 66 (standard deviation [SD] = 9), mean BMI was 28 (SD = 5), 18% were current smokers and 75% were previously on BP-lowering medication. Over a median duration of 4 years, 16,748 participants were diagnosed with cancer, and 4347 cancer deaths were reported. The hazard ratio (HR) per 5mmHg reduction in systolic BP was 1.03 (95% confidence interval [CI] 0.99–1.07) for any cancer and 1.05 (95% CI 0.98–1.12) for cancer death. We found heterogeneity in the effects of BP-lowering across age, gender, BMI and smoking groups for any cancer and cancer death, and across groups defined by previous antihypertensive use for any cancer. However, there was no evidence that BP-lowering significantly increased the risk of developing cancer in specific patient subgroups. We found no evidence for trends in increasing or decreasing risk over time for either outcome (P for trend: any cancer = 0.98, cancer death = 0.99). Conclusions: This large-scale IPD meta-analysis found no evidence that BP-lowering had an important effect on cancer risk. Although we found that BP-lowering effects differed across several patient characteristics, there was no evidence for trend in cancer risk over time. We plan to further investigate the effects of BP-lowering on site-specific cancers (breast, colorectal, kidney, lung, prostate and skin) and present these results at the meeting.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".