Occupational Benzene Exposure and Lung Cancer Risk: A Pooled Analysis of 14 Case-Control Studies.

Rationale: Benzene has been classified as carcinogenic to humans, but there is limited evidence linking benzene exposure to lung cancer. Objectives: We aimed to examine the relationship between occupational benzene exposure and lung cancer. Methods: Subjects from 14 case-control studies across Europe and Canada were pooled. We used a quantitative job-exposure matrix to estimate benzene exposure. Logistic regression models assessed lung cancer risk across different exposure indices. We adjusted for smoking and five main occupational lung carcinogens and stratified analyses by smoking status and lung cancer subtypes. Measurements and Main Results: Analyses included 28,048 subjects (12,329 cases, 15,719 control subjects). Lung cancer odds ratios ranged from 1.12 (95% confidence interval, 1.03-1.22) to 1.32 (95% confidence interval, 1.18-1.48) (Ptrend = 0.002) for groups with the lowest and highest cumulative occupational exposures, respectively, compared with unexposed subjects. We observed an increasing trend of lung cancer with longer duration of exposure (Ptrend < 0.001) and a decreasing trend with longer time since last exposure (Ptrend = 0.02). These effects were seen for all lung cancer subtypes, regardless of smoking status, and were not influenced by specific occupational groups, exposures, or studies. Conclusions: We found consistent and robust associations between different dimensions of occupational benzene exposure and lung cancer after adjusting for smoking and main occupational lung carcinogens. These associations were observed across different subgroups, including nonsmokers. Our findings support the hypothesis that occupational benzene exposure increases the risk of developing lung cancer. Consequently, there is a need to revisit published epidemiological and molecular data on the pulmonary carcinogenicity of benzene.

Systematic review and meta-analyses: What has the application of Mendelian randomization told us about the causal effect of adiposity on health outcomes?

Mendelian randomization (MR) is increasingly used for generating estimates of the causal impact of exposures on outcomes. Evidence suggests a causal role of excess adipose tissue (adiposity) on many health outcomes. However, this body of work has not been systematically appraised. We systematically reviewed and meta-analysed results from MR studies investigating the association between adiposity and health outcomes prior to the SARS-CoV-2/COVID-19 pandemic (PROSPERO: CRD42018096684). We searched Medline, EMBASE, and bioRxiv up to February 2019 and obtained data on 2,214 MR analyses from 173 included articles. 29 meta-analyses were conducted using data from 34 articles (including 66 MR analyses) and results not able to be meta-analysed were narratively synthesised. Body mass index (BMI) was the predominant exposure used and was primarily associated with an increase in investigated outcomes; the largest effect in the meta-analyses was observed for the association between BMI and polycystic ovary syndrome (estimates reflect odds ratios (OR) per standard deviation change in each adiposity measure): OR = 2.55; 95% confidence interval (CI) = 1.22-5.33. Only colorectal cancer was investigated with two exposures in the meta-analysis: BMI (OR = 1.18; 95% CI = 1.01-1.37) and waist-hip ratio (WHR; OR = 1.48; 95% CI = 1.08-2.03). Broadly, results were consistent across the meta-analyses and narrative synthesis. Consistent with many observational studies, this work highlights the impact of adiposity across a broad spectrum of health outcomes, enabling targeted follow-up analyses. However, missing and incomplete data mean results should be interpreted with caution.

Mechanism-based structure-activity relationship (SAR) analysis of aromatic amines and nitroaromatics carcinogenicity via statistical analyses based on CPDB.

Cancer is a leading cause of human mortality around the globe. In this study, mechanism-based SAR (Structure-Activity Relationship) was employed to investigate the carcinogenicity of aromatic amines and nitroaromatics based on CPDB. Principal component analysis and cluster analysis were used to construct the SAR model. Principle component analysis generated three principal components from 12 mechanism-based descriptors. The extracted principal components were later used for cluster analysis, which divided the selected 55 chemicals into six clusters. The three principal components were proposed to describe the "transport", "reactivity" and "electrophilicity" properties of the chemicals. Cluster analysis indicated that the relevant "transport" properties positively correlated with the carcinogenic potential and were contributing factors in determining the carcinogenicity of the studied chemicals. The mechanism-based SAR analysis suggested the electron donating groups, electron withdrawing groups and planarity are significant factors in determining the carcinogenic potency for studied aromatic compounds. The present study may provide insights into the relationship between the three proposed properties and the carcinogenesis of aromatic amines and nitroaromatics.