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Contribution of diet and other factors for urinary concentrations of total arsenic and arsenic species: data for US children, adolescents, and adults.

Ram Baboo Jain
Published in: Environmental science and pollution research international (2021)
A comprehensive analysis of the associations between the consumptions of 17 food products with urinary concentrations of arsenobetaine, total arsenic, arsenous acid, dimethylarsinic acid (UDMA), monomethylarsonic acid (UMMA), and total inorganic arsenic for US children aged 3-5 years (N = 439), children aged 6-11 years (N = 2139), adolescents aged 12-19 years (N = 2434), and adults aged >= 20 years (N = 10902) was conducted. Data from National Health and Nutrition Examination Survey for 2005-2016 were used for this study. Concentrations of arsenobetaine were as much as > 15 times higher among consumers of fish/shellfish than non-consumers for children aged 6-11 years, > 12 times higher for children aged 3-5 years, > 13 times higher for adolescents, and > 7 times higher for adults. Consumption of rice as opposed to non-consumption of rice was associated with as much as 36.5% higher concentrations of total arsenic, 12.7% higher concentrations of arsenous acid, 43.9% higher concentrations of UDMA, 18.2% higher concentrations of UMMA, and 14.1% higher concentrations of total inorganic arsenic. Thus, consumption of fish/shell fish and rice was associated with higher concentrations of organic/inorganic arsenic. In addition, consumption of alcohol was also found to be associated with higher concentrations of organic/inorganic arsenic. However, consumption of milk and milk products, vegetables, organ and other meats, and nutritional drinks was found to be associated with lower concentrations of organic/inorganic arsenic. Thus, while consumption of several foods is associated with higher concentrations of arsenic, there are also foods whose consumption is associated with decreased concentrations of arsenic. Further studies are needed to identify foods that may lead to decreased concentrations of arsenic and as such arsenic toxicity.
Keyphrases
  • drinking water
  • young adults
  • heavy metals
  • physical activity
  • machine learning
  • health risk
  • risk assessment
  • artificial intelligence
  • climate change
  • deep learning
  • case control