ASTox Lab - Analytical and System Toxicology Laboratory, Department of Clinical Analyses, Toxicology and Food Sciences, School of Pharmaceutical Sciences of Ribeirão Preto, University of São Paulo, Avenida Do Café S/n, 14040-903, Ribeirão Preto, São Paulo, Brazil.
ASTox Lab - Analytical and System Toxicology Laboratory, Department of Clinical Analyses, Toxicology and Food Sciences, School of Pharmaceutical Sciences of Ribeirão Preto, University of São Paulo, Avenida Do Café S/n, 14040-903, Ribeirão Preto, São Paulo, Brazil.
Chemosphere. 2023 Sep;334:138975. doi: 10.1016/j.chemosphere.2023.138975. Epub 2023 May 22.
Polycyclic aromatic hydrocarbons (PAHs) and toxic metals are widely spread pollutants of public health concern. The co-contamination of these chemicals in the environment is frequent, but relatively little is known about their combined toxicities. In this context, this study aimed to evaluate the influence of the co-exposure to PAHs and toxic metals on DNA damage in Brazilian lactating women and their infants using machine learning approaches. Data were collected from an observational, cross-sectional study with 96 lactating women and 96 infants living in two cities. The exposure to these pollutants was estimated by determining urinary levels of seven mono-hydroxylated PAH metabolites and the free form of three toxic metals. 8-Hydroxydeoxyguanosine (8-OHdG) levels in the urine were used as the oxidative stress biomarker and set as the outcome. Individual sociodemographic factors were also collected using questionnaires. Sixteen machine learning algorithms were trained using 10-fold cross-validation to investigate the associations of urinary OH-PAHs and metals with 8-OHdG levels. This approach was also compared with models attained by multiple linear regression. The results showed that the urinary concentration of OH-PAHs was highly correlated between the mothers and their infants. Multiple linear regression did not show a statistically significant association between the contaminants and urinary 8OHdG levels. Machine learning models indicated that all investigated variables did not present predictive performance on 8-OHdG concentrations. In conclusion, PAHs and toxic metals were not associated with 8-OHdG levels in Brazilian lactating women and their infants. These novelty and originality results were achieved even after applying sophisticated statistical models to capture non-linear relationships. However, these findings should be interpreted cautiously because the exposure to the studied contaminants was considerably low, which may not reflect other populations at risk.
多环芳烃（PAHs）和有毒金属是广泛存在的公共健康关注污染物。这些化学物质在环境中的共同污染很常见，但对它们的联合毒性知之甚少。在这种情况下，本研究旨在使用机器学习方法评估多环芳烃和有毒金属共同暴露对巴西哺乳期妇女及其婴儿 DNA 损伤的影响。数据来自一项观察性、横断面研究，共有 96 名哺乳期妇女和 96 名生活在两个城市的婴儿。通过测定七种单羟基化多环芳烃代谢物和三种有毒金属的游离形式，来估计这些污染物的暴露情况。尿液中 8-羟基脱氧鸟苷（8-OHdG）水平被用作氧化应激生物标志物，并作为结果。还使用问卷收集了个体社会人口因素。使用 10 折交叉验证训练了 16 种机器学习算法，以调查尿液中 OH-PAHs 和金属与 8-OHdG 水平的关联。该方法还与多元线性回归模型进行了比较。结果表明，母亲和婴儿尿液中的 OH-PAH 浓度高度相关。多元线性回归模型并未显示污染物与尿液 8OHdG 水平之间存在统计学显著关联。机器学习模型表明，所有研究变量对 8-OHdG 浓度均无预测性能。总之，多环芳烃和有毒金属与巴西哺乳期妇女及其婴儿尿液中的 8-OHdG 水平无关。即使应用复杂的统计模型来捕捉非线性关系，也得到了这些新颖性和原创性结果。然而，由于研究中污染物的暴露水平相当低，这些发现应谨慎解释，因为这可能无法反映其他处于危险中的人群。
