Relationship Between Amounts of Daily Cigarette Consumption and Abdominal Obesity Moderated by CYP2A6 Genotypes in Chinese Male Current Smokers
Bibliographic record
Abstract
BACKGROUND: Cigarette smoking is an important risk factor for abdominal obesity. However, the degree to which the CYP2A6 genotype moderates the relationship between smoking and abdominal obesity has not been established. PURPOSE: This study aims to investigate whether or not the relationship between smoking quantity and abdominal obesity is influenced by CYP2A6 genotypes. METHODS: Nine hundred fifty-four male current smokers were selected. A venous specimen was collected to test serum cotinine and CYP2A6 genotype, and all smokers were divided into heavy (>15 cigarettes/day) and light smokers (≤15 cigarettes/day). RESULTS: Heavy smoking increased the risk of abdominal obesity (odds ratio (OR) = 1.57; 95% CI, 1.13-2.19) compared with light smoking. Furthermore, heavy smoking had a positive interactive effect with CYP2A6 poor metabolizer genotype on abdominal obesity (OR = 3.90; 95% CI, 1.25-12.18). Moreover, CYP2A6 poor metabolizer genotypes were associated with slower nicotine metabolism. CONCLUSIONS: Heavy smoking may increase the risk of abdominal obesity-particularly in smokers with CYP2A6 poor metabolizer genotypes.
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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 | 0.000 |
| 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) | 0.002 | 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".