Intensity Of Smoking Increases The Odds Of Acute Myeloid Leukemia In Adult Smokers
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,013 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,010 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».