Mental health, cancer risk, and the mediating role of lifestyle factors in the CARTaGENE cohort study
Bibliographic record
Abstract
BACKGROUND: Evidence on the association between mental health disorders and cancer risk is inconclusive, despite well-established associations between mental health disorders and lifestyle factors such as smoking. This study examines the relationships between depression, anxiety and cancer risk, and the potential mediating effects of lifestyle factors. METHODS: A study of 34,571 participants aged 40-69 years in the CARTaGENE cohort was conducted. Depression was defined by questionnaire (PHQ-9), antidepressant use, and a composite of questionnaire, antidepressant use, or lifetime self-reported physician diagnosis. Anxiety was defined by questionnaire (GAD-7). Co-morbid depression and anxiety was also assessed. Cox regression models were used to investigate associations between mental health and risk of prostate, lung, and all cancers combined. Mediating effects of lifestyle factors were assessed using Baron and Kenny mediation criteria. RESULTS: There were positive associations between mental health disorders, all cancers and lung cancer risk, however with the exception of anxiety and lung cancer in women (Hazard Ratio [HR] = 1.67, 95% CI: 1.01-2.76), associations were attenuated with adjustment for sociodemographics, health status and lifestyle factors. In the mediation analysis, smoking accounted for 27%, 18%, and 26%, of the total effect between depression (PHQ-9), anxiety, and co-morbidity and lung cancer, respectively in women. In men, smoking accounted for 17% of the total effect between depression (PHQ-9, antidepressant, or lifetime self-report of physician diagnosis) and all cancers. CONCLUSIONS: Positive associations were observed between mental health disorders, all cancer and lung cancer risk, however most relationships were attenuated with adjustment for lifestyle factors. Smoking status mediated a significant proportion of the relationships between mental health disorders and cancer risk.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".