Researchers from the Institute for Exposomic Research at Mount Sinai use a novel machine learning algorithm to discover that early exposure to a variety of toxic air pollutants can lead to poor asthma outcomes
Researchers at the Icahn School of Medicine at Mount Sinai and the Institute for Exposomic Research have developed a novel machine learning algorithm and used it to identify previously unknown mixtures of toxic air pollutants that appear to be linked to poor asthma outcomes later in a child’s life.
The study examined early exposure to dozens of pollutants potentially experienced by 151 children with mild to severe forms of the disease. While some cases could be linked to an individual, established air pollutant, others appeared to be linked to mixtures of pollutants that had never been associated with asthma. The results and a description of the new algorithm were described in an article in the Journal of Clinical Investigation.
“Asthma is one the most prevalent diseases affecting children in the United States. In this study, we developed a list of air pollutants a young child may be exposed to that can lead to longer-term problems with asthma,” said Supinda Bunyavanich, MD, MPH, MPhil, Professor of Pediatrics, and Genetics and Genomic Sciences, at Icahn Mount Sinai and a senior author of the study. “Our results show how breathing individual and combinations of pollutants may lead to poor asthma outcomes. We hope that having a more comprehensive, holistic view of air pollution may one day be able to reduce the chances that children will be burdened by asthma.”
Affecting about seven percent of children in the United States, asthma is a lung disease that can cause people to wheeze, suffer chest tightness, and bouts of coughing. Although several studies have shown that breathing individual toxic air pollutants, or “air toxics”, raises the chances a child may suffer from asthma, little is known about what happens when the pollutants mix.
In this study, the researchers used a novel machine learning algorithm to find that 18 individual chemicals may be linked to poor asthma outcomes later in life. Specifically, they looked at whether a child needed daily asthma-controlling medication or had to visit an emergency room or spend time in the hospital as a result of their condition. However, they also found new associations between the outcomes and 20 different pollutant mixtures. Several of the chemicals in the mixtures had never been linked to long-term asthma risk.
“Like many scientists, we wanted to provide a more comprehensive picture of how air toxics contribute to childhood asthma,” said Gaurav Pandey, PhD, Assistant Professor of Genetics and Genomic Sciences and a senior author of the study. “Traditionally, for technical reasons, it has been difficult to study the health effects of more than one toxic at a time. We overcame this by tapping into the power of machine learning algorithms.”
The study and development of the algorithm was led by Yan-Chak Li, MPhil, a bioinformatician in the Pandey lab, and Hsiao-Hsien Leon Hsu, ScD, Assistant Professor of Environmental Medicine and Public Health at Icahn Mount Sinai.
Li, Y.C., Hsu, H.H.L., et al., Machine learning-driven identification of early-life air toxic combinations associated with childhood asthma outcomes, Journal of Clinical Investigation, October 5, 2021, DOI: 10.1172/JCI152088.