{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T14:52:28Z","timestamp":1767192748258,"version":"build-2065373602"},"reference-count":86,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2022,12,8]],"date-time":"2022-12-08T00:00:00Z","timestamp":1670457600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"US National Institute of Health","award":["R21 ES029686-01A1"],"award-info":[{"award-number":["R21 ES029686-01A1"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Toxics"],"abstract":"<jats:p>Chemical exposures from diverse sources merge on a limited number of molecular pathways described as toxicity pathways. Changes in the same set of molecular pathways in different cell and tissue types may generate seemingly unrelated health conditions. Today, no approaches are available to predict in an unbiased way sensitivities of different disease states and their combinations to multi-chemical exposures across the exposome. We propose an inductive in-silico workflow where sensitivities of genes to chemical exposures are identified based on the overlap of existing genomic datasets, and data on sensitivities of individual genes is further used to sequentially derive predictions on sensitivities of molecular pathways, disease states, and groups of disease states (syndromes). Our analysis predicts that conditions representing the most significant public health problems are among the most sensitive to cumulative chemical exposures. These conditions include six leading types of cancer in the world (prostatic, breast, stomach, lung, colorectal neoplasms, and hepatocellular carcinoma), obesity, type 2 diabetes, non-alcoholic fatty liver disease, autistic disorder, Alzheimer\u2019s disease, hypertension, heart failure, brain and myocardial ischemia, and myocardial infarction. Overall, our predictions suggest that environmental risk factors may be underestimated for the most significant public health problems.<\/jats:p>","DOI":"10.3390\/toxics10120764","type":"journal-article","created":{"date-parts":[[2022,12,8]],"date-time":"2022-12-08T02:51:56Z","timestamp":1670467916000},"page":"764","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Towards Whole Health Toxicology: In-Silico Prediction of Diseases Sensitive to Multi-Chemical Exposures"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8556-7468","authenticated-orcid":false,"given":"Olatunbosun","family":"Arowolo","sequence":"first","affiliation":[{"name":"Department of Environmental Health Sciences, School of Public Health and Health Sciences, University of Massachusetts, 686 North Pleasant Street, Amherst, MA 01003, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Victoria","family":"Salemme","sequence":"additional","affiliation":[{"name":"Department of Pharmacology, University of California, 1275 Med Science, Davis, CA 95616, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2757-5897","authenticated-orcid":false,"given":"Alexander","family":"Suvorov","sequence":"additional","affiliation":[{"name":"Department of Environmental Health Sciences, School of Public Health and Health Sciences, University of Massachusetts, 686 North Pleasant Street, Amherst, MA 01003, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Jonas, W.B., and Rosenbaum, E. 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