From cigarettes to symptoms: the association between smoking and depression in the German National Cohort (NAKO)
Bibliographic record
Abstract
BACKGROUND: Although the association between smoking and depression is well-established, the underlying mechanisms and contextual factors remain insufficiently understood. We examined the association between smoking and depression, including detailed dose-response and timing-related relationships, using baseline data from a large population-based cohort, the German National Cohort (NAKO). METHODS: The analysis comprised 173,890 participants (19-72 years, 50.21% female). Lifetime and current depression were assessed via self-reported physician's diagnosis, the Major Depressive Disorder module of the MINI International Neuropsychiatric Interview (MINI), and the depression scale of the Patient Health Questionnaire (PHQ-9). Smoking behavior was assessed using self-reported smoking status, age at initiation, cigarettes per day, and time since smoking cessation. Associations between smoking and depression measures were analyzed using regression models adjusted for sex, age, age², education, Body Mass Index, and alcohol consumption. RESULTS: Lifetime depression was more prevalent among individuals who currently or formerly smoked compared to those who never smoked. Currently smoking individuals also reported most current depressive symptoms, followed by formerly smoking individuals and those who never smoked. A dose-response relationship was observed, with more cigarettes per day being associated with more current depressive symptoms. Later age at smoking initiation was associated with later depression onset. Time since smoking cessation was positively associated with time since last depressive episode and negatively with current depressive symptoms. CONCLUSIONS: Our findings support an association between smoking and depression. Robust dose-response relationships were found, with higher cigarette consumption associated with more severe depressive symptoms, and longer time since cessation linked to lower depression levels. These results highlight smoking as a meaningful and modifiable contributor to current and lifetime depression, suggesting that quitting smoking or reducing cigarette consumption may benefit mental health. Early prevention of smoking initiation, along with integrated approaches that combine smoking cessation support with mental health care, may help reduce both smoking rates and depression burden.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.001 | 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".