A flexible modeling approach to estimating the component effects of smoking behavior on lung cancer

Rachet, Bernard; Siemiatycki, Jack; Abrahamowicz, Michał; Leffondré, Karen

doi:10.1016/j.jclinepi.2004.02.014

Cited by 48 publications

(36 citation statements)

References 52 publications

(78 reference statements)

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“…The characteristics of PI MZ and PI MM subjects are shown in table 3. There were no statistically significant differences between the two groups in age, gender, pack-years or FEV 1 . A cut-off of 40 pack-years was selected for the piecewise linear approach based on visual inspection and model fit.…”

Section: Resultsmentioning

confidence: 65%

“…All of the study samples had a similar pattern of an initial strong negative effect of smoking on FEV 1 level with a subsequent decrease in the negative impact of additional pack-years. With the exception of the Norway study, there seemed to be a plateau phase at which additional pack-years were not associated with a further decline in FEV 1 . In all four samples the slope of the FEV 1 /pack-years relation decreased at an FEV 1 level of approximately 30e50% of predicted.…”

Section: Resultsmentioning

confidence: 77%

“…A similar non-linear phenomenon in which risk tapers at higher levels of smoking exposure has been demonstrated with smoking intensity in lung cancer. 1 The two most likely explanations for the non-linear relations observed are (1) survival bias (ie, differential population sampling at higher levels of cigarette exposure) and (2) a physiological floor in FEV 1 which, once reached, results in a diminished FEV 1 response to additional cigarette exposure. If these two mechanisms are active, the data points of most interest would be those that occur prior to the plateau phase in the relation between FEV 1 and pack-years, since the points on the plateau portion of the curve are likely to be affected either by survival bias or floor effects that may act to dilute the strength of any observed gene-by-smoking interactions.…”

Section: Discussionmentioning

confidence: 99%

“…3e5 However, it is not clear that these findings apply to the types of study samples typically assembled for COPD genetic association studiesdnamely, cross-sectional samples that include subjects with a wide range of lung function impairment including severe disease. In this setting, a number of factors may result in a non-linear relation between pack-years and FEV 1 . These factors could include survival bias due to the welldemonstrated association between FEV 1 and mortality, 6 and floor effects resulting from a diminished effect of cigarette smoking at very low levels of FEV 1 .…”

mentioning

confidence: 99%

“…In this setting, a number of factors may result in a non-linear relation between pack-years and FEV 1 . These factors could include survival bias due to the welldemonstrated association between FEV 1 and mortality, 6 and floor effects resulting from a diminished effect of cigarette smoking at very low levels of FEV 1 .…”

mentioning

confidence: 99%

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The Impact Of Nonlinear Smoking Effects In The Identification Of Gene-by-Smoking Interactions In COPD Genetics Studies

Castaldi

DeMeo

Hersh

et al. 2010

D17. Gene Environment Interactions

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BackgroundThe identification of gene-by-environment interactions is important for understanding the genetic basis of chronic obstructive pulmonary disease (COPD). Many COPD genetic association analyses assume a linear relationship between pack-years of smoking exposure and forced expiratory volume in 1 s (FEV 1 ); however, this assumption has not been evaluated empirically in cohorts with a wide spectrum of COPD severity. Methods The relationship between FEV 1 and pack-years of smoking exposure was examined in four large cohorts assembled for the purpose of identifying genetic associations with COPD. Using data from the Alpha-1 Antitrypsin Genetic Modifiers Study, the accuracy and power of two different approaches to model smoking were compared by performing a simulation study of a genetic variant with a range of gene-by-smoking interaction effects. Results Non-linear relationships between smoking and FEV 1 were identified in the four cohorts. It was found that, in most situations where the relationship between pack-years and FEV 1 is non-linear, a piecewise linear approach to model smoking and gene-by-smoking interactions is preferable to the commonly used total pack-years approach. The piecewise linear approach was applied to a genetic association analysis of the PI*Z allele in the Norway CaseeControl cohort and a potential PI*Z-by-smoking interaction was identified (p¼0.03 for FEV 1 analysis, p¼0.01 for COPD susceptibility analysis). Conclusion In study samples of subjects with a wide range of COPD severity, a non-linear relationship between pack-years of smoking and FEV 1 is likely. In this setting, approaches that account for this non-linearity can be more powerful and less biased than the more common approach of using total pack-years to model the smoking effect.Chronic obstructive pulmonary disease (COPD) is well suited to the study of gene-by-environment interactions since the major environmental risk factor for COPDdcigarette smokingdis known and quantifiable. With the advent of large wellpowered genome-wide association studies in COPD, the identification of such interactions may be feasible. However, there are a number of challenges to the identification of gene-by-smoking interactions in COPD: (1) the principal genetic risk factors for COPD are still in the process of being identified; (2) a variety of approaches have been used to model smoking effects; and (3) there is no empirical knowledge of the nature, extent or functional form of gene-by-smoking interactions in COPD.While cigarette smoking is easily quantifiable in terms of pack-years ((average daily number of cigarettes smoked/20 cigarettes per pack) 3 years of smoking), previous work has shown that packyears alone may be an overly simplistic means of modelling smoking exposure, and non-linear relations may be present.1 2 Many COPD genetic association analyses model smoking effects by including a pack-years term in a regression model, which assumes a linear relation between pack-years and forced expiratory volume in 1 s (FEV 1 ) or, in...

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Section: Resultsmentioning

confidence: 65%

Section: Resultsmentioning

confidence: 77%

Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

See 3 more Smart Citations

The Impact Of Nonlinear Smoking Effects In The Identification Of Gene-by-Smoking Interactions In COPD Genetics Studies

Castaldi

DeMeo

Hersh

et al. 2010

D17. Gene Environment Interactions

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Lung cancer risk associated with occupational exposure to nickel, chromium VI, and cadmium in two population‐based case–control studies in Montreal

Beveridge

Pintos

Parent

et al. 2010

American J Industrial Med

106

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Body mass index, lifetime smoking intensity and lung cancer risk

El‐Zein

Parent

Nicolau

et al. 2013

Intl Journal of Cancer

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There is as yet no generally accepted explanation for the common finding that low body mass index (BMI) is associated with an increased risk of lung cancer. We investigated this association in a Canadian population-based case-control study (1996)(1997)(1998)(1999)(2000)(2001)(2002) with a particular view to assessing the hypothesis that the observed association was due to residual confounding by smoking. Analyses were based on 1,076 cases and 1,439 controls who provided their height at enrollment and their weight at two points in time, at age 20 and 2 years before enrollment. BMI, in kg/m 2 , was classified into underweight (<18.5), normal (18.5-24.9), overweight (25.0-29.9), and obese (30). Smoking history was synthesized into a comprehensive smoking index (CSI) that integrated duration, intensity and time since quitting. Odds ratios (ORs) and 95% confidence intervals (CIs) for BMI-lung cancer associations were estimated, adjusting for CSI as well as several sociodemographic, lifestyle and occupational factors. The normal BMI category was used as the reference. Among those who were underweight at age 20, there was a lower risk of lung cancer (OR 5 0.69, 95% CI: 0.50-0.95). Conversely, lung cancer risk was increased among those who were underweight 2 years before enrollment (OR 5 2.30, 95% CI: 1.30-4.10). The results were almost identical when stratifying analyses based on smoking history into never/lighter and heavier smokers. The inverse association between recent BMI and lung cancer is unlikely to be largely attributable to residual confounding by smoking. Reverse causality or a true relationship between BMI and lung cancer remain plausible.Elevated body mass index (BMI) has been associated with an increased risk of several cancers, including esophageal, pancreatic, colorectal, endometrial, postmenopausal breast, and prostate cancers.1 By contrast, BMI appears to be inversely related to lung cancer risk.2-16 However, the robustness and meaning of this ostensible inverse association has been challenged in some studies by the finding that such an inverse association was not present among nonsmokers. 4,9,10,12 Smoking is both a powerful risk factor for lung cancer, 17 and is also inversely associated with body weight. 18,19 Several explanations have been hypothesized regarding the observed inverse BMI-lung cancer association. First, a methodological argument, according to which residual confounding by smoking accounted for observing an inverse, or disguising a true positive, BMI-lung cancer relation. Second, reverse causality due to pre-clinical effects of lung cancer, especially for studies in which weight was documented shortly before cancer diagnosis. Third, an etiologic explanation, namely through carcinogenic DNA adducts 6 or effects of estrogens. 16 In any case, the interplay between BMI, smoking, and lung cancer remains uncertain.In the context of a large population-based case-control study on the environmental etiology of lung cancer, data on sociodemographic, anthropometric, and lifestyle factors were c...

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A flexible modeling approach to estimating the component effects of smoking behavior on lung cancer

Cited by 48 publications

References 52 publications

The Impact Of Nonlinear Smoking Effects In The Identification Of Gene-by-Smoking Interactions In COPD Genetics Studies

The Impact Of Nonlinear Smoking Effects In The Identification Of Gene-by-Smoking Interactions In COPD Genetics Studies

Lung cancer risk associated with occupational exposure to nickel, chromium VI, and cadmium in two population‐based case–control studies in Montreal

Body mass index, lifetime smoking intensity and lung cancer risk

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