<i><scp>CHRNA</scp>5‐<scp>A</scp>3‐<scp>B</scp>4</i> genetic variants alter nicotine intake and interact with tobacco use to influence body weight in <scp>Alaska Native</scp> tobacco users
Bibliographic record
Abstract
BACKGROUND AND AIMS: Gene variants in CHRNA5-A3-B4, which encode for the α5, α3 and β4 nicotinic receptor subunits, are associated with altered smoking behaviors in European Americans. Little is known about CHRNA5-A3-B4 and its association with smoking behaviors and weight in Alaska Native people, which is a population with high prevalence but low levels of tobacco consumption, extensive smokeless tobacco use and high rates of obesity. We investigated CHRNA5-A3-B4 haplotype structure and its association with nicotine intake and obesity in Alaska Native people. DESIGN, SETTING AND PARTICIPANTS: A cross-sectional study of 400 Alaska Native individuals, including 290 tobacco users. MEASUREMENTS: CHRNA5-A3-B4 genotype, body weight and tobacco consumption biomarkers such as plasma cotinine and urinary total nicotine equivalents (TNE). FINDINGS: Alaska Native people have a distinct CHRNA5-A3-B4 haplotype structure compared with European/African Americans. In 290 Alaska Native tobacco users the 'G' allele of rs578776, which tagged a 30 kb haplotype in CHRNA5-A3-B4, was prevalent (16%) and associated significantly with nicotine intake (20% higher plasma cotinine, P < 0.001, 16% higher TNE, P = 0.076), while rs16969968 was not associated with nicotine intake. Rs578776 acted in combination with CYP2A6, the main nicotine-metabolizing enzyme, to increase nicotine intake by 1.8-fold compared with the low-risk group (P < 0.001). Furthermore, rs2869950, a single nucleotide polymorphism 5' to CHRNB4, was associated significantly with increased body mass index (P < 0.01) in the tobacco users even after controlling for differences in nicotine intake (P < 0.01). CONCLUSIONS: Genetic variants in CHRNA5-A3-B4 alter nicotine intake and body mass index in a population of Alaska Native people, who have a distinct haplotype structure, smoking behaviors and prevalence of obesity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".