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A synthetic cohort was simulated using national exposure data and probabilistic techniques, employing multivariate logistic regression models to ensure precise CRC risk and controlling for lifestyle covariates. Adjusted PAF values were calculated using regression data to determine the proportion of CRC cases attributable to each water contaminant. THMs and <i>E. coli</i> showed the strongest association with CRC, with adjusted PAF values of 25.76% and 23.65%, respectively. Exposure to nitrates, arsenic, and mercury contributed less to CRC risk (1.02%, 0.52% and 2.20%, respectively). Multivariate regression confirmed that THMs and <i>E. coli</i> remained the strongest independent predictors of CRC risk (OR = 1.37, <i>p</i> = 0.001) and (OR = 1.79, <i>p</i> < 0.0001) among water contaminants, respectively. 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引用次数: 0
摘要
结直肠癌(CRC)的病因复杂,涉及遗传、生活方式和环境因素。本研究调查了黎巴嫩暴露于水污染物与发生CRC风险之间的关系。人口归因分数(PAF)是根据环境报告和水质评价中的暴露率计算的。通过多项荟萃分析和流行病学研究获得了相对危险度(RR),以量化五种水污染物对CRC发病率的贡献:硝酸盐、汞、砷、三卤甲烷(THMs)和微生物污染物(大肠杆菌)。使用国家暴露数据和概率技术模拟了一个综合队列,采用多变量逻辑回归模型来确保精确的CRC风险并控制生活方式协变量。使用回归数据计算调整后的PAF值,以确定归因于每种水污染物的CRC病例的比例。THMs和大肠杆菌与CRC的相关性最强,调整后的PAF值分别为25.76%和23.65%。暴露于硝酸盐、砷和汞对结直肠癌风险的贡献较小(分别为1.02%、0.52%和2.20%)。多因素回归证实,THMs和大肠杆菌仍然是水污染物中CRC风险最强的独立预测因子(OR = 1.37, p = 0.001)和(OR = 1.79, p < 0.0001)。由于依赖于间接暴露和风险估计,我们基于模型的方法存在局限性,这可能会引入源于数据缺口和潜在假设的不确定性。这项研究强调了水质管理在预防结直肠癌中的重要性,因为暴露于饮用水污染物会增加疾病负担。
Environmental Exposure to Waterborne Pollutants and Colorectal Cancer Risk in Lebanon.
Colorectal cancer (CRC) has a complex etiology involving genetic, lifestyle, and environmental factors. This study investigates the association between exposure to water contaminants and the risk of developing CRC in Lebanon. Population Attributable Fraction (PAF) was calculated using exposure prevalence from environmental reports and water quality assessments. Relative risks (RR) were obtained from multiple meta-analyses and epidemiological studies to quantify the contribution of five water contaminants to CRC incidence: nitrates, mercury, arsenic, trihalomethanes (THMs), and microbial pollutants (E. coli). A synthetic cohort was simulated using national exposure data and probabilistic techniques, employing multivariate logistic regression models to ensure precise CRC risk and controlling for lifestyle covariates. Adjusted PAF values were calculated using regression data to determine the proportion of CRC cases attributable to each water contaminant. THMs and E. coli showed the strongest association with CRC, with adjusted PAF values of 25.76% and 23.65%, respectively. Exposure to nitrates, arsenic, and mercury contributed less to CRC risk (1.02%, 0.52% and 2.20%, respectively). Multivariate regression confirmed that THMs and E. coli remained the strongest independent predictors of CRC risk (OR = 1.37, p = 0.001) and (OR = 1.79, p < 0.0001) among water contaminants, respectively. Our model-based approach carries limitations due to reliance on indirect exposure and risk estimates, which may introduce uncertainty stemming from data gaps and underlying assumptions. This study highlights the importance of water quality management in CRC prevention as exposure to drinking water contaminants contributes meaningfully to disease burden.
ToxicsChemical Engineering-Chemical Health and Safety
CiteScore
4.50
自引率
10.90%
发文量
681
审稿时长
6 weeks
期刊介绍:
Toxics (ISSN 2305-6304) is an international, peer-reviewed, open access journal which provides an advanced forum for studies related to all aspects of toxic chemicals and materials. It publishes reviews, regular research papers, and short communications. Our aim is to encourage scientists to publish their experimental and theoretical results in detail. There is, therefore, no restriction on the maximum length of the papers, although authors should write their papers in a clear and concise way. The full experimental details must be provided so that the results can be reproduced. Electronic files or software regarding the full details of calculations and experimental procedure can be deposited as supplementary material, if it is not possible to publish them along with the text.