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Studying the Elusive Environment in Large Scale

Chirag J. Patel, PhD1; John P. A. Ioannidis, MD, DSc2,3,4
[+] Author Affiliations
1Center for Biomedical Informatics, Harvard Medical School, Boston, Massachusetts
2Stanford Prevention Research Center, Department of Health Research and Policy, Department of Medicine, Stanford University School of Medicine, Stanford, California
3Department of Statistics, Stanford University School of Humanities and Sciences, Stanford, California
4Meta-Research Innovation Center at Stanford (METRICS), Stanford, California
JAMA. 2014;311(21):2173-2174. doi:10.1001/jama.2014.4129.
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It is possible that more than 50% of complex disease risk is attributed to differences in an individual’s environment.1 Air pollution, smoking, and diet are documented environmental factors affecting health, yet these factors are but a fraction of the “exposome,” the totality of the exposure load occurring throughout a person’s lifetime.1 Investigating one or a handful of exposures at a time has led to a highly fragmented literature of epidemiologic associations. Much of that literature is not reproducible, and selective reporting may be a major reason for the lack of reproducibility. A new model is required to discover environmental exposures associated with disease while mitigating possibilities of selective reporting.

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Figure.
Correlation Interdependency Globes for 4 Environmental Exposures (Cotinine, Mercury, Cadmium, Trans-β-Carotene) in National Health and Nutrition Examination Survey (NHANES) Participants, 2003-2004

Each correlation interdependency globe includes 317 environmental exposures represented by the nodes around the periphery of the globe. Pairwise correlations are depicted by edges (lines) between the node of interest (arrowhead) and other nodes. Correlations with absolute values exceeding 0.2 are shown (strongest 10%). The size of each node is proportional to the number of edges for a node, and the thickness of each edge indicates the magnitude of the correlation.

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