Nuclear and Radiological Engineering and Medical Physics Programs, George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, 770 State Street, Atlanta, GA, 30332, USA.
Advanced Computing for Health Sciences Section, Oak Ridge National Laboratory, 1 Bethel Valley Road, Oak Ridge, TN, 37830, USA.
Environ Geochem Health. 2024 Feb 17;46(3):82. doi: 10.1007/s10653-023-01820-4.
Characterizing the interplay between exposures shaping the human exposome is vital for uncovering the etiology of complex diseases. For example, cancer risk is modified by a range of multifactorial external environmental exposures. Environmental, socioeconomic, and lifestyle factors all shape lung cancer risk. However, epidemiological studies of radon aimed at identifying populations at high risk for lung cancer often fail to consider multiple exposures simultaneously. For example, moderating factors, such as PM, may affect the transport of radon progeny to lung tissue. This ecological analysis leveraged a population-level dataset from the National Cancer Institute's Surveillance, Epidemiology, and End-Results data (2013-17) to simultaneously investigate the effect of multiple sources of low-dose radiation (gross [Formula: see text] activity and indoor radon) and PM on lung cancer incidence rates in the USA. County-level factors (environmental, sociodemographic, lifestyle) were controlled for, and Poisson regression and random forest models were used to assess the association between radon exposure and lung and bronchus cancer incidence rates. Tree-based machine learning (ML) method perform better than traditional regression: Poisson regression: 6.29/7.13 (mean absolute percentage error, MAPE), 12.70/12.77 (root mean square error, RMSE); Poisson random forest regression: 1.22/1.16 (MAPE), 8.01/8.15 (RMSE). The effect of PM increased with the concentration of environmental radon, thereby confirming findings from previous studies that investigated the possible synergistic effect of radon and PM on health outcomes. In summary, the results demonstrated (1) a need to consider multiple environmental exposures when assessing radon exposure's association with lung cancer risk, thereby highlighting (1) the importance of an exposomics framework and (2) that employing ML models may capture the complex interplay between environmental exposures and health, as in the case of indoor radon exposure and lung cancer incidence.
描述塑造人类暴露组的各种暴露因素之间的相互作用对于揭示复杂疾病的病因至关重要。例如，癌症风险受到多种多因素环境暴露的影响。环境、社会经济和生活方式因素都会影响肺癌的风险。然而，旨在确定肺癌高危人群的氡流行病学研究往往未能同时考虑多种暴露因素。例如，PM 等调节因素可能会影响氡子体向肺部组织的输送。这项生态分析利用了美国国家癌症研究所监测、流行病学和最终结果数据（2013-17 年）的人群水平数据集，同时研究了多种低剂量辐射源（总[公式：见文本]活性和室内氡）和 PM 对美国肺癌发病率的影响。控制了县级因素（环境、社会人口统计学、生活方式），并使用泊松回归和随机森林模型评估了氡暴露与肺癌和支气管癌发病率之间的关联。基于树的机器学习（ML）方法比传统回归表现更好：泊松回归：6.29/7.13（平均绝对百分比误差，MAPE），12.70/12.77（均方根误差，RMSE）；泊松随机森林回归：1.22/1.16（MAPE），8.01/8.15（RMSE）。PM 的影响随着环境氡浓度的增加而增加，从而证实了之前研究的结果，即研究氡和 PM 对健康结果的可能协同作用。总之，结果表明（1）在评估氡暴露与肺癌风险之间的关联时，需要考虑多种环境暴露因素，从而突出（1）暴露组学框架的重要性和（2）采用 ML 模型可能捕捉到环境暴露与健康之间的复杂相互作用，就像室内氡暴露与肺癌发病率之间的关系一样。
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