Body mass index and lung cancer risk: results from the ICARE study, a large, population-based case-control study

Pulmonary circulatory system characteristics are associated with future lung cancer risk

BACKGROUND

Most of the subjects eligible for annual low-dose computed tomography (LDCT) lung screening will not develop lung cancer for their life. It is important to identify novel biomarkers that can help identify those at risk of developing lung cancer and improve the efficiency of LDCT screening programs.

OBJECTIVE

This study aims to investigate the association between the morphology of the pulmonary circulatory system (PCS) and lung cancer development using LDCT scans acquired in the screening setting.

METHODS

We analyzed the PLuSS cohort of 3635 lung screening patients from 2002 to 2016. Circulatory structures were segmented and quantified from LDCT scans. The time from the baseline CT scan to lung cancer diagnosis, accounting for death, was used to evaluate the prognostic ability (i.e., hazard ratio (HR)) of these structures independently and with demographic factors. Five-fold cross-validation was used to evaluate prognostic scores.

RESULTS

Intrapulmonary vein volume had the strongest association with future lung cancer (HR = 0.63, p < 0.001). The joint model of intrapulmonary vein volume, age, smoking status, and clinical emphysema provided the strongest prognostic ability (HR = 2.20, AUC = 0.74). The addition of circulatory structures improved risk stratification, identifying the top 10% with 28% risk of lung cancer within 15 years.

CONCLUSION

PCS characteristics, particularly intrapulmonary vein volume, are important predictors of lung cancer development. These factors significantly improve prognostication based on demographic factors and noncirculatory patient characteristics, particularly in the long term. Approximately 10% of the population can be identified with risk several times greater than average.

BMI changes and the risk of lung cancer in male never-smokers: A prospective cohort study

BACKGROUND

To investigate the association between the risk of lung cancer and short-term body mass index (BMI) changes in male never-smokers of a large population-based prospective study.

METHODS

A total of 37,085 male never-smokers from Kailuan cohort with at least ≥2 BMI measurements were recruited in the present study. The BMI change in the follow-up was calculated as the annual percent change between BMI at last examination and that at baseline, and categorized into five groups: stable (-0.1 to <0.1 kg/m² /year), minor loss (-1.0 to <0.1 kg/m² /year) or gain (0.1 to <1.0 kg/m² /year), and major loss (<-1.0 kg/m² /year) or gain (≥1.0 kg/m² /year). The hazards ratios (HRs) and its 95% confidence intervals (CI) were estimated using Cox regression models.

RESULTS

During a median follow-up of 5.16 years, 224 lung cancer cases were identified. We found a U-shaped association between BMI changes and lung cancer risk. Compared to men with stable BMI, those with major loss had a nearly twofold higher risk of lung cancer (HR = 1.97, 95% CI: 1.12-3.45), as well as those with major gain had more than twofold higher risk of lung cancer (HR = 2.15, 95% CI: 1.15-4.02). The associations existed when the analysis was stratified by BMI, waist circumference and blood lipids, and lipoproteins concentration at baseline examination.

CONCLUSIONS

The dramatic changes in BMI, both gain and loss, might increase lung cancer risk. The control of body weight would be a potential way for lung cancer prevention especially for the nonsmokers.

Body mass index and lung cancer risk: a pooled analysis based on nested case-control studies from four cohort studies

BACKGROUND

Obesity has been proposed as a potential protective factor against lung cancer. We examined the association between BMI and lung cancer risk in a pooled analysis based on nested case-control studies from four cohort studies.

METHODS

A case-control study was nested within four cohorts in USA, Europe, China and Singapore that included 4172 cases and 8471 control subjects. BMI at baseline was calculated as weight in kilograms divided by height in meters squared (kg/m2), and classified into 4 categories: underweight (BMI < 18.5), normal weight (18.5 ≤ BMI < 25), overweight (25 ≤ BMI < 30) and obese (≥30). Odds ratios (ORs) and 95% confidence intervals (CIs) for BMI-lung cancer associations were estimated using unconditional logistic regression, adjusting for potential confounders.

RESULTS

Considering all participants, and using normal weight as the reference group, a decreased risk of lung cancer was observed for those who were overweight (OR 0.77, 95% CI: 0.68-0.86) and obese (OR 0.69, 95% CI: 0.59-0.82). In the stratified analysis by smoking status, the decreased risk for lung cancer was observed among current, former and never smokers (P for interaction 0.002). The adjusted ORs for overweight and obese groups were 0.79 (95% CI: 0.68-0.92) and 0.75 (95% CI: 0.60-0.93) for current smokers, 0.70 (95% CI: 0.53-0.93) and 0.55 (95% CI: 0.37-0.80) for former smokers, 0.77 (95% CI: 0.59-0.99), and 0.71 (95% CI: 0.44-1.14) for never smokers, respectively. While no statistically significant association was observed for underweight subjects who were current smokers (OR 1.24, 95% CI: 0.98-1.58), former smokers (OR 0.27, 95% CI: 0.12-0.61) and never smokers (OR 0.83, 95% CI: 0.5.-1.28).

CONCLUSION

The results of this study provide additional evidence that obesity is associated with a decreased risk of lung cancer. Further biological studies are needed to address this association.

Anthropometry and the Risk of Lung Cancer in EPIC

The associations of body mass index (BMI) and other anthropometric measurements with lung cancer were examined in 348,108 participants in the European Investigation Into Cancer and Nutrition (EPIC) between 1992 and 2010. The study population included 2,400 case patients with incident lung cancer, and the average length of follow-up was 11 years. Hazard ratios were calculated using Cox proportional hazard models in which we modeled smoking variables with cubic splines. Overall, there was a significant inverse association between BMI (weight (kg)/height (m)(2)) and the risk of lung cancer after adjustment for smoking and other confounders (for BMI of 30.0-34.9 versus 18.5-25.0, hazard ratio = 0.72, 95% confidence interval: 0.62, 0.84). The strength of the association declined with increasing follow-up time. Conversely, after adjustment for BMI, waist circumference and waist-to-height ratio were significantly positively associated with lung cancer risk (for the highest category of waist circumference vs. the lowest, hazard ratio = 1.25, 95% confidence interval: 1.05, 1.50). Given the decline of the inverse association between BMI and lung cancer over time, the association is likely at least partly due to weight loss resulting from preclinical lung cancer that was present at baseline. Residual confounding by smoking could also have influenced our findings.

Mendelian randomization study of adiposity-related traits and risk of breast, ovarian, prostate, lung and colorectal cancer

BACKGROUND

Adiposity traits have been associated with risk of many cancers in observational studies, but whether these associations are causal is unclear. Mendelian randomization (MR) uses genetic predictors of risk factors as instrumental variables to eliminate reverse causation and reduce confounding bias. We performed MR analyses to assess the possible causal relationship of birthweight, childhood and adult body mass index (BMI), and waist-hip ratio (WHR) on the risks of breast, ovarian, prostate, colorectal and lung cancers.

METHODS

We tested the association between genetic risk scores and each trait using summary statistics from published genome-wide association studies (GWAS) and from 51 537 cancer cases and 61 600 controls in the Genetic Associations and Mechanisms in Oncology (GAME-ON) Consortium.

RESULTS

We found an inverse association between the genetic score for childhood BMI and risk of breast cancer [odds ratio (OR) = 0.71 per standard deviation (s.d.) increase in childhood BMI; 95% confidence interval (CI): 0.60, 0.80; P = 6.5 × 10(-5)). We also found the genetic score for adult BMI to be inversely associated with breast cancer risk (OR = 0.66 per s.d. increase in BMI; 95% CI: 0.57, 0.77; P = 2.5 × 10(-7)), and positively associated with ovarian cancer (OR = 1.35; 95% CI: 1.05, 1.72; P = 0.017), lung cancer (OR = 1.27; 95% CI: 1.09, 1.49; P = 2.9 × 10(-3)) and colorectal cancer (OR = 1.39; 95% CI: 1.06, 1.82, P = 0.016). The inverse association between genetically predicted adult BMI and breast cancer risk remained even after adjusting for directional pleiotropy via MR-Egger regression.

CONCLUSIONS

Findings from this study provide additional understandings of the complex relationship between adiposity and cancer risks. Our results for breast and lung cancer are particularly interesting, given previous reports of effect heterogeneity by menopausal status and smoking status.

Body mass index and risk of lung cancer: Systematic review and dose-response meta-analysis

Questions remain about the significance of the dose-response relationship between body mass index (BMI) and lung cancer (LC) risk. Pertinent studies were identified through a search in EMBASE and PUBMED from July 2014 until March 2015. The summary relative risk (SRR) and confidence interval (CI) were estimated. The dose-response relationship was assessed using a restricted cubic spline. The overall meta-analysis showed evidence of a nonlinear association between BMI and LC risk (Pnonlinearity < 0.001). The SRR were 0.98 (95%CI: 0.95-1.01) for 25 kg/m(2), 0.91 (95%CI: 0.85-0.98) for 30 kg/m(2) and 0.81 (95% CI: 0.72-0.91) for 35 kg/m(2), with mild between-study heterogeneity (I(2) = 5%). The results of the stratified analysis by gender were comparable to those of the overall meta-analysis. When stratified by smoking status, linear dose-response associations were observed for current smokers, ex-smokers and non-smokers (Pnonlinearity > 0.05), whereas the effects were attenuated when restricting analysis to non-smokers, and at the point of 30 kg/m(2), the SRR was 0.96 (95%CI: 0.86-1.07) for males and 0.95 (95%CI: 0.89-1.02) for females. This meta-analysis provides quantitative evidence that increasing BMI is a protective factor against LC. Keeping normal-to-moderate BMI should be prescribed as an evidence-based lifestyle tip for LC prevention in smokers.

Body mass index, cholesterol level and risk of lung cancer in Lithuanian men

OBJECTIVE

Our objective was to investigate the association between body mass index (BMI), total serum cholesterol (TSC) level and risk of lung cancer in a Lithuanian population-based cohort study.

MATERIALS AND METHODS

The study included 6729 men initially free from cancer. During the follow-up (1978-2008), 358 lung cancer cases were identified. Cox proportional hazards models were used to estimate hazard ratios (HR) and corresponding 95% confidence intervals (95% CI).

RESULTS

Following adjustment for age, smoking, alcohol consumption, and education, BMI 25-29.9 and ≥30.0kg/m(2) hazard ratios (HR) were significantly associated with decreasing risk for lung cancer, HR=0.73; 95% CI: 0.59, 0.91 and 0.62; 95% CI: 0.45, 0.87, respectively (ptrend=0.001) compared to BMI<25 kg/m(2). Inverse association between BMI and lung cancer was observed among current smokers. We found no evidence that BMI was associated with decreased lung cancer risk in never smokers, although small sample size precluded meaningful analysis. Not significantly lower risk of lung cancer among participants in the 5th quintile compared with the 1st quintile of TSC concentrations was observed. HR per 1 mmol/l increase of TSC was 0.90; 95% CI: 0.82, 1.00. Findings suggest consistent effects of BMI and TSC when follow-up was 1993-2008.

CONCLUSION

Our results show an inverse dose-dependent association between lung cancer risk and BMI in Lithuanian men, especially among current smokers. The inverse association could not be attributed to preclinical cancer effect hypothesis. TSC level was not statistically significantly related to a lung cancer incidence.

Pollution in the working place and social status: co-factors in lung cancer carcinogenesis

INTRODUCTION

Apart from the association with tobacco consumption, other factors of importance for prevention and early diagnosis of lung cancer have received little attention. We present a case-control study focusing on professional exposure to carcinogens and social status.

METHODS

A written questionnaire was completed by 551 consecutive patients with lung cancer and 494 patients with large bowel cancer. The groups were balanced regarding gender and age distribution. The questionnaire included data on place of birth, education, smoking history, diet and alcohol intake, body weight and height, occupation, housing conditions and family income. According to standard epidemiological criteria, professional exposure to carcinogens was classified as professions with exposure to confirmed lung cancer carcinogens, professions with exposure to suspected lung cancer carcinogens and other professions.

RESULTS

As expected, there were significant differences between the two groups regarding smoking status. While there were no significant differences in educational levels, more immigrants were among patients with lung cancer (17.9% vs 11.6%, p=0.005). On average, lung cancer patients had a lower body mass index (BMI) at 24.77, as compared to 26.14 for large bowel cancer (p=0.000). Lung cancer patients had lower income and poorer housing conditions; the bivariate difference was significant both for income levels (p=0.046) and type of residence (p=0.009). The proportion of patients working in professions with exposures to known carcinogens was 33.5% for lung cancer, and 17.1% for large bowel cancer (p=0.000). In the multivariate analysis, smoking (p=0.000), BMI (p=0.000) and type of occupation (p=0.001) were significant factors.

CONCLUSIONS

While there is no doubt about smoking in lung cancer carcinogenesis, professional exposure to carcinogens and belonging to lower socio-economic strata also play an important role.

A semi-parametric approach to estimate risk functions associated with multi-dimensional exposure profiles: application to smoking and lung cancer

BACKGROUND

A common characteristic of environmental epidemiology is the multi-dimensional aspect of exposure patterns, frequently reduced to a cumulative exposure for simplicity of analysis. By adopting a flexible Bayesian clustering approach, we explore the risk function linking exposure history to disease. This approach is applied here to study the relationship between different smoking characteristics and lung cancer in the framework of a population based case control study.

METHODS

Our study includes 4658 males (1995 cases, 2663 controls) with full smoking history (intensity, duration, time since cessation, pack-years) from the ICARE multi-centre study conducted from 2001-2007. We extend Bayesian clustering techniques to explore predictive risk surfaces for covariate profiles of interest.

RESULTS

We were able to partition the population into 12 clusters with different smoking profiles and lung cancer risk. Our results confirm that when compared to intensity, duration is the predominant driver of risk. On the other hand, using pack-years of cigarette smoking as a single summary leads to a considerable loss of information.

CONCLUSIONS

Our method estimates a disease risk associated to a specific exposure profile by robustly accounting for the different dimensions of exposure and will be helpful in general to give further insight into the effect of exposures that are accumulated through different time patterns.

Body mass index, lifetime smoking intensity and lung cancer risk

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 (1996-2002) 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 = 0.69, 95% CI: 0.50-0.95). Conversely, lung cancer risk was increased among those who were underweight 2 years before enrollment (OR = 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.