Use of ACE (Angiotensin-Converting Enzyme) Inhibitors and Risk of Lung Cancer
Bibliographic record
Abstract
BACKGROUND: Use of angiotensin-converting enzyme inhibitors (ACEIs)was associated with increased risk of lung cancer in a cohort study from the United Kingdom. We aimed to replicate these findings in a Danish population. METHODS: We conducted a nested case-control study using data from 4 Danish national health and administrative registries. New users of ACEIs or angiotensin II receptor blockers in Denmark from January 1, 2000 were followed until December 31, 2015, incident lung cancer, death, or emigration. Each lung cancer case was matched with up to 20 controls on age, sex, duration of follow-up, and year of cohort entry using risk-set sampling. Conditional logistic regression was used to estimate odds ratios (ORs) for incident, histologically verified lung cancer with high use of ACEIs defined as a cumulative dose above 3650 defined daily doses. We examined different cumulative doses of ACEI (≤1800, 1801-3650, >3650 defined daily doses), examined whether the association varied with lung cancer histology, and repeated the analyses using thiazides as active comparator. RESULTS: We included 9652 lung cancer cases matched to 190 055 controls. High use of ACEIs was associated with lung cancer (adjusted OR, 1.33 [95% CI, 1.08-1.62]). Lower cumulative doses showed neutral associations (≤1800 defined daily doses OR, 1.01 [95% CI, 0.94-1.09]; 1801-3650 defined daily doses OR, 1.03 [95% CI, 0.90-1.19]). CIs were wide and included the null when stratifying on histology. Using thiazides as active comparator yielded comparable results (OR, 1.34 [95% CI, 0.96-1.88]). CONCLUSIONS: Use of high cumulative ACEI doses was associated with modestly increased odds of lung cancer although use of lower doses showed neutral associations. The established benefits of ACEIs should be considered when interpreting these findings.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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".