Association between waterpipe smoking and obesity:Population-based study in Qatar
Bibliographic record
Abstract
INTRODUCTIONOver the past decade obesity prevalence has been increasing rapidly in the Gulf region (GR) including Qatar, becoming one of the major health issues in the region.Concomitantly, waterpipe (WP) smoking is increasing worldwide especially in the GR, and although the effect of cigarette smoking on body weight is well-established, studies indicating an association between WP smoking and obesity are scarce.Thus, we explored the association between WP smoking and obesity in comparison with cigarette smokers and healthy population in Qatar.METHODS We performed a cross-sectional study using data from Qatar Biobank and analyzed anthropometric measurements among 879 adults (aged 18-65 years) that included WP smokers, cigarette smokers, dual smokers and never smokers.Body composition was measured using bioelectrical impedance analysis and reported as lean mass, fat mass, and body fat percentage.RESULTS Overall, 12% (n=108) were WP smokers, 22% (n=196) were cigarette smokers, 9% (n=77) smoked both WP and cigarettes and 57% (n=498) were never smokers.Age, sex, history of diabetes, and hypertension, in addition to nationality were considered as confounding factors.Our analysis revealed that WP smokers had a significantly higher BMI (kg/m 2 ) and fat mass when compared with cigarette smokers (p<0.05).Moreover, compared to cigarette smoking, WP smoking had a higher significant effect on BMI (β=3.8,SE=0.38; and β=5.5, SE=0.46; respectively), and fat mass (β=5.1,SE=0.79; and β=9.0,SE=0.97; respectively).However, WP users were similar to never-smokers in terms of body fat percent.CONCLUSIONS Our data indicate that compared to never smokers, daily WP users have higher BMI and fat mass, and are likely to be obese.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".