The Key Laboratory of Environmental Pollution Monitoring and Disease Control, Ministry of Education, School of Public Health, Guizhou Medical University, Guiyang 550025, China.
The Key Laboratory of Environmental Pollution Monitoring and Disease Control, Ministry of Education, School of Public Health, Guizhou Medical University, Guiyang 550025, China.
Sci Total Environ. 2023 Apr 20;870:161861. doi: 10.1016/j.scitotenv.2023.161861. Epub 2023 Jan 28.
Long-term exposure to inorganic arsenic may lead to arsenicosis. There are, however, currently no validated metabolic biomarkers used for the identification of arsenicosis risk. This study aims to identify metabolites associated with arsenicosis and establish prediction models for risk assessment based on untargeted metabolomics and machine learning algorithms.
In total, 105 coal-borne arsenicosis patients, with 35 subjects in each of the mild, moderate, and severe subgroups according to their symptom severity, and 60 healthy residents were enrolled from Guizhou, China. Ultra-high performance liquid chromatography-tandem mass spectrometer (UHPLC-MS/MS) was utilized to acquire the plasma metabolic profiles of the studied subjects. Statistical analysis was used to identify disease-associated metabolites. Machine learning algorithms and the identified metabolic biomarkers were resorted to assess the arsenicosis risk.
A total of 143 metabolic biomarkers, with organic acids being the majority, were identified to be closely associated with arsenicosis, and the most involved pathway was glycine, serine, and threonine metabolism. Comparative analysis of metabolites in arsenicosis patients with different symptom severity revealed 422 altered molecules, where disrupted metabolism of beta-alanine and arginine demonstrated the most significance. For risk assessment, the model established by a single biomarker (L-carnosine) could undoubtedly discriminate arsenicosis patients from the healthy. For classifying arsenicosis patients with different severity, the model established using 52 metabolites and linear discriminate analysis (LDA) algorithm yielded an accuracy of 0.970-0.979 on calibration set (n = 132) and 0.818-0.848 on validation set (n = 33).
Altered metabolites and disrupted pathways are prevalent in arsenicosis patients; The disrupted metabolism of one carbon and dysfunction of antioxidant defense system may partially be causes of the systematic multi-organ damage and carcinogenesis in arsenicosis patients; Metabolic biomarkers, combined with machine learning algorithms, could be efficient for risk assessment and early identification of arsenicosis.
长期接触无机砷可能导致砷中毒。然而，目前尚无经证实的代谢生物标志物可用于识别砷中毒风险。本研究旨在鉴定与砷中毒相关的代谢物，并基于非靶向代谢组学和机器学习算法建立风险评估预测模型。
共纳入 105 例煤源性砷中毒患者，按症状严重程度分为轻度、中度和重度三组，每组 35 例，另纳入 60 例健康对照者。采用超高效液相色谱-串联质谱（UHPLC-MS/MS）获取研究对象的血浆代谢谱。采用统计学分析鉴定与疾病相关的代谢物。采用机器学习算法和鉴定的代谢标志物评估砷中毒风险。
共鉴定出 143 种与砷中毒密切相关的代谢标志物，其中有机酸占多数，最相关的途径是甘氨酸、丝氨酸和苏氨酸代谢。对不同症状严重程度砷中毒患者的代谢物进行比较分析，发现有 422 种代谢物发生改变，其中β-丙氨酸和精氨酸代谢紊乱最为显著。用于风险评估，单个标志物（L-肉碱）建立的模型可以明确区分砷中毒患者和健康对照者。用于分类不同严重程度的砷中毒患者，基于 52 种代谢物和线性判别分析（LDA）算法建立的模型在训练集（n=132）和验证集（n=33）上的准确率分别为 0.970-0.979 和 0.818-0.848。
砷中毒患者中存在代谢物改变和途径紊乱；一碳代谢紊乱和抗氧化防御功能障碍可能部分导致砷中毒患者的系统性多器官损伤和致癌作用；代谢标志物结合机器学习算法可有效用于风险评估和砷中毒的早期识别。
