Antidepressant Use and Lung Cancer Risk and Survival: A Meta-analysis of Observational Studies
Bibliographic record
Abstract
Recent preclinical studies have linked antidepressants (AD) to their potential anticancer effects in multiple cancers, but the impact on lung cancer remains unclear. This meta-analysis examined the associations between ADs and lung cancer incidence and survival. The Web of Science, Medline, CINAHL, and PsycINFO databases were searched to identify eligible studies published by June 2022. We conducted a meta-analysis using a random-effects model to compare the pooled risk ratio (RR) and 95% confidence interval (CI) in those treated with or without ADs. Heterogeneity was examined using Cochran Q test and inconsistency I2 statistics. The methodologic quality of the selected studies was assessed using the Newcastle-Ottawa Scale for observational studies. Our analysis, including 11 publications involving 1,200,885 participants, showed that AD use increased lung cancer risk by 11% (RR = 1.11; 95% CI = 1.02–1.20; I2 = 65.03%; n = 6) but was not associated with overall survival (RR = 1.04; 95% CI = 0.75–1.45; I2 = 83.40%; n = 4). One study examined cancer-specific survival. Subgroup analysis showed that serotonin and norepinephrine reuptake inhibitors (SNRIs) were associated with an increased lung cancer risk by 38% (RR = 1.38; 95% CI = 1.07–1.78; n = 2). The quality of selected studies was good (n = 5) to fair (n = 6). Our data analysis suggests that SNRIs were associated with an elevated risk of lung cancer, raising concerns regarding the use of AD treatment in patients vulnerable to lung cancer. The effects of ADs—particularly SNRIs—and their interplay with cigarette use and lung cancer risk in vulnerable patients merits further study. Significance: In this meta-analysis of 11 observational studies, we found evidence of a statistically significant association between the use of certain ADs and lung cancer risk. This effect merits further study, particularly as it relates to known environmental and behavioral drivers of lung cancer risk, such as air pollution and cigarette smoke.
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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 | 0.022 | 0.043 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.065 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".