Replicated association of the <i>NR4A3</i> gene with smoking behaviour in schizophrenia and in bipolar disorder
Bibliographic record
Abstract
Schizophrenia and bipolar disorder are associated with dopamine neurotransmission and show high comorbidity with tobacco dependence. Recent evidence indicates that the family of the NR4A orphan nuclear receptors, which are expressed in dopamine neurons and in dopaminoceptive brain areas, may play a role in dopamine-mediated effects. We have, therefore, analysed the association of six single nucleotide polymorphisms (SNPs) within the three genes belonging to the NR4A orphan nuclear receptor family, NR4A1 (rs2603751, rs2701124), NR4A2 (rs12803, rs834835) and NR4A3 (rs1131339, rs1405209), with the degree of smoking in a sample of 204 unrelated schizophrenia patients, which included 126 smokers and 78 non-smokers. SNPs within the NR4A3 gene (rs1131339 and rs1405209) were significantly associated with heavy smoking in this cohort, using a stepwise analysis of the escalated number of cigarettes smoked per day (P = 0.008 and 0.006, respectively; satisfying the Nyholt significance threshold of 0.009, an adjustment for multiple testing). We then repeated the association analysis of the NR4A3 markers (rs1131339 and rs1405209) in a larger cohort of 319 patients with bipolar disorder, which included 167 smokers and 152 non-smokers. We have replicated the positive association with smoking of the NR4A3 SNP rs1131339 in this group (P = 0.04), providing an important confirmation of the involvement of the NR4A3 gene in nicotine addiction in patients with mental health disease, a population significantly at risk for nicotine addiction.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".