Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY, USA.
Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Chemosphere. 2023 Apr;320:137998. doi: 10.1016/j.chemosphere.2023.137998. Epub 2023 Feb 4.
Chronic exposure to arsenic (As) remains a global public health concern and our understanding of the biological mechanisms underlying the adverse effects of As exposure remains incomplete. Here, we used a high-resolution metabolomics approach to examine how As affects metabolic pathways in humans. We selected 60 non-smoking adults from the Folic Acid and Creatine Trial (FACT). Inorganic (As, As) and organic (monomethylarsonous acid [MMAs], dimethylarsinous Acid [DMAs]) As species were measured in blood and urine collected at baseline and at 12 weeks. Plasma metabolome profiles were measured using untargeted high-resolution mass spectrometry. Associations of blood and urinary As with 170 confirmed metabolites and >26,000 untargeted spectral features were modeled using a metabolome-wide association study (MWAS) approach. Models were adjusted for age, sex, visit, and BMI and corrected for false discovery rate (FDR). In the MWAS screening of confirmed metabolites, 17 were associated with ≥1 blood As species (FDR<0.05), including fatty acids, neurotransmitter metabolites, and amino acids. These results were consistent across blood As species and between blood and urine As. Untargeted MWAS identified 423 spectral features associated with ≥1 blood As species. Unlike the confirmed metabolites, untargeted model results were not consistent across As species, with As and DMAs showing distinct association patterns. Mummichog pathway analysis revealed 12 enriched metabolic pathways that overlapped with the 17 identified metabolites, including one carbon metabolism, tricarboxylic acid cycle, fatty acid metabolism, and purine metabolism. Exposure to As may affect numerous essential pathways that underlie the well-characterized associations of As with multiple chronic diseases.
慢性砷暴露仍然是一个全球性的公共卫生问题，我们对砷暴露引起的不良影响的生物学机制的理解仍然不完整。在这里，我们使用高分辨率代谢组学方法来研究砷如何影响人类的代谢途径。我们从叶酸和肌酸试验（FACT）中选择了 60 名不吸烟的成年人。在基线和 12 周时采集血液和尿液，测量无机（砷、砷）和有机（一甲基胂酸[MMA]、二甲基胂酸[DMA]）砷物种。使用非靶向高分辨率质谱法测量血浆代谢组谱。使用代谢组学全关联研究（MWAS）方法，对血液和尿液中砷与 170 种已确认代谢物和>26000 种非靶向谱特征的关联进行建模。模型根据年龄、性别、访问和 BMI 进行调整，并对假发现率（FDR）进行校正。在确认代谢物的 MWAS 筛选中，有 17 种与≥1 种血液砷物种相关（FDR<0.05），包括脂肪酸、神经递质代谢物和氨基酸。这些结果在血液砷物种之间以及血液和尿液砷之间是一致的。非靶向 MWAS 确定了 423 个与≥1 种血液砷物种相关的谱特征。与确认代谢物不同，非靶向模型结果在砷物种之间不一致，砷和 DMA 表现出不同的关联模式。Mummichog 途径分析揭示了 12 个与 17 种鉴定代谢物重叠的丰富代谢途径，包括一碳代谢、三羧酸循环、脂肪酸代谢和嘌呤代谢。砷暴露可能会影响许多基本途径，这些途径是砷与多种慢性疾病之间已确定关联的基础。
