Indoor Air Pollution Increases the Risk of Lung Cancer

Abstract

(1) Background: Cooking and burning incense are important sources of indoor air pollutants. No studies have provided biological evidence of air pollutants in the lungs to support this association. Analysis of pleural fluid may be used to measure the internal exposure dose of air pollutants in the lung. The objective of this study was to provide biological evidence of indoor air pollutants and estimate their risk of lung cancer. (2) Methods: We analyzed 14 common air pollutants in the pleural fluid of 39 cases of lung adenocarcinoma and 40 nonmalignant controls by gas chromatography-mass spectrometry. (3) Results: When we excluded the current smokers and adjusted for age, the adjusted odds ratios (ORs) were 2.22 (95% confidence interval CI = 0.77-6.44) for habitual cooking at home and 3.05 (95% CI = 1.06-8.84) for indoor incense burning. In females, the adjusted ORs were 5.39 (95% CI = 1.11-26.20) for habitual cooking at home and 6.01 (95% CI = 1.14-31.66) for indoor incense burning. In pleural fluid, the most important exposure biomarkers for lung cancer were naphthalene, ethylbenzene, and o-xylene. (4) Conclusions: Habitual cooking and indoor incense burning increased the risk of lung adenocarcinoma.

Keywords: bioaccumulation; combustion sources; exposure assessment; indoor air pollutants; machine learning; pleural fluid.

Figure 1

Research theoretical framework. The above figure shows the research hypothesis to find the cause of the rationale between indoor air pollutants and lung adenocarcinoma. PAHs: polycyclic aromatic hydrocarbons; BTEX: benzene, toluene, ethylbenzene, and xylene.

Figure 2

Flow diagram depicting the inclusion and exclusion of the study subjects.

Figure 3

Heatmap of concentrations of exposure biomarkers (PAHs and BTEX in pleural fluid) in lung cancer patients and controls.

Figure 4

Receiver operating characteristic curves for lung cancer predicted by environmental exposure histories and exposure biomarkers in pleural fluid. We included the prediction variables of environmental exposure histories (lived near the main street, distance to the main street, cigarette smoking, incense burning, essential oil) and exposure biomarkers (PAHs and BTEX in pleural fluid) and used four machine learning algorithms to establish a prediction algorithm. The machine learning algorithms used R packages of the decision tree (rpart), random forests (rf), generalized linear models (glm), and neural networks (nnet). The ROCs ranged from 0.84 to 1.00.

Figure 5

The important environmental factors and exposure biomarkers of lung cancer are determined by (a) decision tree and (b) random forest models showing the variable importance (VIP) score. By the decision tree, the most important factor in predicting lung cancer is the concentration of naphthalene (≥0.61 ppb) in pleural fluid, followed by the distance between the residence and the main street and habitual cooking at home. By random forest, the most important factor for predicting lung cancer is habitual cooking at home, followed by the concentrations of ethylbenzene, o-xylene, naphthalene in pleural fluid, and then habitual indoor incense burning.