Arsenic detection in water and type 2 diabetes association

Separation techniques will be key in future research following the discovery of a link between arsenic in drinking water and type 2 diabetes. The study, published in the Journal of American Medical Association (JAMA) showed elevated levels of arsenic in urine and increased risk of type 2 diabetes in a group of 788 adult subjects in the USA.

Steve Wilbur, senior applications scientist for Agilent Technologies' chemical analysis solutions unit said, "The next step in this research will involve establishing a clearer relationship between the actual source of the exposure, such as drinking water, food or workplace exposure, the form or species of arsenic and the incidence of type 2 diabetes. This will require testing methods that can distinguish between arsenic species."

"To do this researchers can use a separation technique, such as liquid chromatography, which is capable of separating the species. Then they could join that with a sensitive arsenic detection system, such as inductively-coupled plasma mass spectrometry (ICP-MS). This allows us to determine the actual form of arsenic present at extremely low levels, to understand the real risk associated with food and water and to generate new regulations which better reflect that risk."

In a further development published in Chemical Biology metabolic markers for studying diabetes in urine and tissue samples from mice deficient in the hormone protein leptin are being analysed using nuclear magnetic resonance (NMR) and LC–MS methods.

Geoffrey Gipson at Drexel University, Philadelphia, USA, and colleagues in the US and UK have compared the metabolite levels with those in control mice and confirmed that many pathways, including fatty acid metabolism, are altered in "diabetic" mice. Gipson says that these pathways could potentially be targeted for diabetes treatments.

Jules Griffin, an expert in metabolomics at the University of Cambridge, UK, commends the team's integrated approach and told Chemical Biology, "While NMR has been used widely in mammalian disease models the use of LC–MS based approaches has lagged behind, in part because of the greater challenges in technical reproducibility. By picking a well-characterized model of type 2 diabetes the team has been able to validate its approach and then extend its analysis to new metabolite changes not previously described."