There are over 80,000 chemicals in commerce with little data available describing their impacts on human health. Biomonitoring surveys, such as the NHANES, offer one route to identifying possible relationships between environmental chemicals and health impacts, but sparse data and the complexity of traditional models makes it difficult to leverage effectively. This study described a workflow to efficiently and comprehensively evaluate and prioritise chemical-health impact relationships from the NHANES biomonitoring survey studies. Using a frequent itemset mining (FIM) approach, chemical to health biomarker and disease relationships were identified. The FIM method identified 7,848 relationships between 219 chemicals and 93 health outcomes/ biomarkers. Two case studies used to evaluate the FIM rankings demonstrate that the FIM approach is able to identify published relationships. Since the relationships are derived from the vast majority of the chemicals monitored by NHANES, the resulting list of associations is appropriate for evaluating results from targeted data mining or identifying novel candidate relationships for more detailed investigation. Due to the computational efficiency of the FIM method, all chemicals and health effects can be considered in a single analysis. The resulting list provides a comprehensive summary of the chemical/health co-occurrences from NHANES that are higher than expected by chance. This information enables ranking and prioritisation on chemicals or health effects of interest for evaluation of published results and design of future studies.
Authors: Bell SM, Edwards SW. ;Full Source: Environmental Health Perspectives. 2015 Apr 10. [Epub ahead of print] ;