Intensity Of Smoking Increases The Odds Of Acute Myeloid Leukemia In Adult Smokers
Bibliographic record
Abstract
Abstract Introduction A number of risk factors for the development of acute myeloid leukemia (AML) have been previously described. The role of smoking in the development of AML has been postulated as a potential environmental risk factor. This association has been studied in previous observational reports; however, the dose-response relation between smoking and AML has not been evaluated. The primary objective of this meta-analysis is to evaluate the dose-relationship between smoking and the development of AML. Secondary objectives were to identify potential gender and/or geographical disparities. Methods A PubMed search from January 1, 1993 to December 31, 2012 was undertaken using the keywords: “(smoking OR tobacco OR cigarette) AND leukemia”. Prospective cohort and case-control studies reporting on the incidence of AML were included. Studies reporting on acute promyelocytic leukemia, acute leukemia or myeloid leukemia without specifying subtype and cross-sectional studies were excluded. Studies included in a previous meta-analysis by Brownson et al. (1993) were also excluded. The outcome of interest was the odds ratio (OR) with 95% confidence interval (CI) of developing AML in smokers compared to never smokers. Because the overall risk of AML in the general population is low, the relative risk mathematically approximates the OR, allowing the pooling of cohort and case-control studies (rare disease assumption). The random effects model (REM), which accounts for intra and inter-study heterogeneity, was used to estimate the combined outcome. Heterogeneity was also quantified by the I2statistic. Publication bias was assessed by the trim-and-fill analysis. Stratified analyses were performed in current smokers and ever smokers. Subset analyses were performed by sex, study design, geographical region, number of cigarettes smoked, number of years of smoking and cumulative smoking in pack-years. The quality of the studies was assessed by the Newcastle-Ottawa scale (NOS). Literature search, data gathering and quality assessment were performed independently by at least two of the investigators. All calculations and graphs were obtained using Comprehensive Meta-Analysis version 2.2.050 (Biostat, Englewood, NJ, USA). Results Our initial search yielded 573 articles, from which 16 case-control and 6 cohort studies were included in our final analysis; 10 studies were from Europe, 9 from North America and 3 from Asia. All the studies were considered of intermediate and high quality, based on the NOS score. Ever smokers had an OR of 1.26 (95% CI 1.15-1.38; p<0.01). The OR of AML in male and female ever smokers was 1.45 (95% CI 1.14-1.85; p<0.01) and 1.14 (95% CI 1.00-1.29; p=0.05), respectively. Current smokers had an OR of 1.42 (95% CI 1.24-1.62; p<0.01). Male and female current smokers had an OR 1.42 (95% CI 1.12-1.81; p<0.01) and 1.28 (95% CI 1.03-1.60; p=0.03), respectively. The odds of AML were increased in ever and current smokers regardless of the study design and the geographical region. Heterogeneity was minimal to moderate and publication bias analysis would have not changed our results. The dose-response analysis showed that smoking >20 cigarettes per day was associated with OR 1.76 (95% CI 1.41-2.18; p<0.01) while smoking <20 cigarettes with OR 1.38 (95% CI 1.23-1.55; p<0.01) of developing AML. Smoking >20 years showed OR of 1.35 (95% CI 1.16-1.57; p<0.01) but smoking <20 years was not associated with increased odds of AML (OR 1.04, 95% CI 0.90-1.20; p=0.56). Conclusions There is a 26% increase in the odds of developing of AML in those who have ever smoked compared to never smokers. In those who are current smokers the odds are increased by 42%. The odds of AML in male smokers appear somewhat higher than in female smokers but there does not appear to be a geographical disparity in the odds of AML based on smoking status. The dose-response analysis supports that the intensity of smoking might be a stronger driver of the odds of AML than the duration of smoking in adult individuals. Disclosures: No relevant conflicts of interest to declare.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.010 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".