Association of α4β2 nicotinic receptor and heavy smoking in schizophrenia
Bibliographic record
Abstract
INTRODUCTION: Previously we suggested that the CHRNA7 polymorphism in nicotinic receptor genes, in particular the D15S1360 in CHRNA7, is associated with smoking in schizophrenia. Schizophrenia patients are usually heavy smokers. In this study we hypothesized that high-affinity nicotinic receptors are associated with smoking in such patients. OBJECTIVE: To investigate the role of α4 (Ch 20) and β2 (Ch 1) genes in conferring a risk for smoking and for smoking a large number of cigarettes daily in subjects with schizophrenia. METHODS: Our study sample consisted of 241 white European schizophrenia patients (157 smokers and 84 nonsmokers) from the Toronto area. Current smoking status was assessed by the medical history. We investigated 4 markers located in the CHRNA4 gene and 3 markers located in the CHRNB2 gene. RESULTS: There was no difference in age or ethnicity between the 2 groups and the population was not stratified (λ = 0.4527). We found a significant association between the CHRNA4 rs3746372 allele 1 and a large number of cigarettes smoked daily (p = 0.0203). The intragenic interaction between rs3787116 and rs3746372 (p = 0.0050) in CHRNA4 showed a significant interaction for the number of cigarettes smoked. CONCLUSION: Although our findings suggest an association between rs3746372 allele 1 and heavy smoking, further study is warranted to investigate the relation between smoking and high-affinity nicotinic receptor genes in schizophrenia.
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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 | 0.001 | 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) | 0.005 | 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".