Machine Learning Helps Link Chemical Exposure and Obesity

Some chemicals found in plastic bottles are hormone disruptors like the ones investigated in this study. (Credit: Mali Maeder/pexels.com)

Obesity is a major health concern and chemical exposure is considered to contribute to this disease, along with genetic and lifestyle factors. However, real-world chemical exposure is complex and combinations of chemicals and their resulting interactions have not been studied fully. 

Scientists at Berkeley Lab and their collaborators developed a machine learning technique to discover obesity-related mixed chemical exposure patterns associated with environmental health risk in the general U.S. population. Using this technique, the researchers assessed the relationships between the specific chemical mixture patterns and obesity indicators, such as body mass index and waist circumference. They used the National Health and Nutrition Examination Survey 2005–2012 data available from the Centers for Disease Control.

Hang Chang, a staff scientist in the Biological and Systems Engineering (BSE) Division, co-led the team that found and demonstrated that mixed exposure patterns exceed the environmental risk of exposure to individual chemicals. “We studied these exposure patterns from organic chemicals that could disrupt hormones previously thought to be involved in obesity and found evidence that they could be associated with the disease. At the same time, we looked at carcinogens and didn’t find any correlation between their presence and obesity.”