Millions of people are at risk of consuming arsenic (As) contaminated drinking water in Pakistan. The current study aimed to investigate urinary arsenic species [iAsIII, iAsV, dimethylarsinic acid (DMA), methylarsonic acid (MMA)] and their potential toxicity biomarkers (based on urinary metabolome) in order to characterize the health effects in general adult male participants (n = 588) exposed to various levels of arsenic in different floodplain areas of Pakistan. The total urinary arsenic concentration (mean; 161 μg/L) of studied participants was lower and/or comparable than those values reported from other highly contaminated regions, but exceeded the Agency for Toxic Substances and Disease Registry (ATSDR) limits. For all the participants, the most excreted species was DMA accounting for 65% of the total arsenic, followed by MMA (20%) and iAs (16%). The percentage of MMA detected in this study was higher than those of previously reported data from other countries. These results suggested that studied population might have high risk of developing arsenic exposure related adverse health outcomes. Furthermore, random forest machine learning algorithm, partial correlation and binary logistic regression analysis were performed to screen the arsenic species-related urinary metabolites. A total of thirty-eight metabolites were extracted from 2776 metabolic features and identified as the potential arsenic toxicity biomarkers. The metabolites were mainly classified into xanthines, purines, and amino acids, which provided the clues linking the arsenic exposure with oxidative stress, one-carbon metabolism, purine metabolism, caffeine metabolism and hormone metabolism. These results would be helpful to develop early health warning system in context of arsenic exposure among the general populations of Pakistan.
Keywords: Arsenic; Biomarkers; Human biomonitoring; Pakistan; Urinary metabolome.
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