Abstract 6464: Antidepressant use and lung cancer risk and mortality: A meta-analysis of observational studies
Bibliographic record
Abstract
Abstract Objectives: Recent preclinical studies suggested potential anticancer effects of antidepressant (AD) use in multiple cancers, but the effect on lung cancer in human studies remains unclear. This meta-analysis examined the effect of AD use on lung cancer incidence and mortality. Methods: 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 estimate pooled risk ratio (RR) and 95% confidence interval (CI) for comparing those with AD use and non-use. Heterogeneity was examined using Cochran’s Q test and inconsistency I2 statistics. The methodological quality of selected studies was assessed using the Newcastle-Ottawa Scale for observational studies. Results: The meta-analysis, including 11 publications involving 1913172 participants, showed that AD use significantly increased lung cancer risk by 11% (RR= 1.11; 95% CI= 1.01, 1.22; I2= 74.3%; n= 6); however, all-cause mortality was not affected by AD use (RR= 1.05; 95% CI= 0.68, 1.58; I2= 90.1%; n= 4) while lung cancer-specific mortality could be reduced by 32% from onse study. Subgroup analysis showed that serotonin and norepinephrine reuptake inhibitors (SNRIs) were associated with an increased lung cancer risk (RR= 1.38; 95% CI= 1.07, 1.78), but selective serotonin reuptake inhibitors (RR= 1.05; 95% CI=0.91, 1.21) and tricyclic antidepressants (RR= 1.09; 95% CI=0.95, 1.26) were not. In addition, variations in the definition of AD use could be the source of heterogeneity of observed effects. The quality of selected studies was good (n= 5) to fair (n= 6). Conclusions: Evidence suggests that AD use was associated with an elevated risk of lung cancer but not with all-cause mortality. More research is needed to precisely estimate the effect of AD use on lung cancer-specific mortality. (Funding: University of Central Florida Center for Behavioral Health Research and Training; Registration: PROSPERO; registration number: CRD42022350719) Citation Format: Eunkyung Lee, David Li, Yongho Park, Alice Rodriguez-Fuguet, Xiaochuan Wang, Wen Cai Zhang. Antidepressant use and lung cancer risk and mortality: A meta-analysis of observational studies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6464.
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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 | 0.021 | 0.042 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.071 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".