{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,12,4]],"date-time":"2024-12-04T14:58:27Z","timestamp":1733324307357,"version":"3.30.1"},"update-to":[{"updated":{"date-parts":[[2021,8,2]],"date-time":"2021-08-02T00:00:00Z","timestamp":1627862400000},"DOI":"10.1371\/journal.pcbi.1009144","type":"new_version","label":"New version"}],"reference-count":86,"publisher":"Public Library of Science (PLoS)","issue":"7","license":[{"start":{"date-parts":[[2021,7,21]],"date-time":"2021-07-21T00:00:00Z","timestamp":1626825600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/publicdomain\/zero\/1.0\/"}],"funder":[{"DOI":"10.13039\/100000050","name":"NHLBI","doi-asserted-by":"crossref","award":["R01HL119326"],"id":[{"id":"10.13039\/100000050","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Saperstein Scholars Fund"},{"name":"CDC\/NIOSH","award":["U01-OH011300"]},{"name":"Clinical Center of Excellence","award":["200-2017-93426"]},{"name":"Data Center","award":["200-2017-93326"]}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"Biomarkers predict World Trade Center-Lung Injury (WTC-LI); however, there remains unaddressed multicollinearity in our serum cytokines, chemokines, and high-throughput platform datasets used to phenotype WTC-disease. To address this concern, we used automated, machine-learning, high-dimensional data pruning, and validated identified biomarkers. The parent cohort consisted of male, never-smoking firefighters with WTC-LI (FEV1, %Pred<\/jats:sub>< lower limit of normal (LLN); n = 100) and controls (n = 127) and had their biomarkers assessed. Cases and controls (n = 15\/group) underwent untargeted metabolomics, then feature selection performed on metabolites, cytokines, chemokines, and clinical data. Cytokines, chemokines, and clinical biomarkers were validated in the non-overlapping parent-cohort via binary logistic regression with 5-fold cross validation. Random forests of metabolites (n = 580), clinical biomarkers (n = 5), and previously assayed cytokines, chemokines (n = 106) identified that the top 5% of biomarkers important to class separation included pigment epithelium-derived factor (PEDF), macrophage derived chemokine (MDC), systolic blood pressure, macrophage inflammatory protein-4 (MIP-4), growth-regulated oncogene protein (GRO), monocyte chemoattractant protein-1 (MCP-1), apolipoprotein-AII (Apo-AII), cell membrane metabolites (sphingolipids, phospholipids), and branched-chain amino acids. Validated models via confounder-adjusted (age on 9\/11, BMI, exposure, and pre-9\/11 FEV1, %Pred<\/jats:sub>) binary logistic regression had AUCROC<\/jats:sub>[0.90(0.84\u20130.96)]. Decreased PEDF and MIP-4, and increased Apo-AII were associated with increased odds of WTC-LI. Increased GRO, MCP-1, and simultaneously decreased MDC were associated with decreased odds of WTC-LI. In conclusion, automated data pruning identified novel WTC-LI biomarkers; performance was validated in an independent cohort. One biomarker\u2014PEDF, an antiangiogenic agent\u2014is a novel, predictive biomarker of particulate-matter-related lung disease. Other biomarkers\u2014GRO, MCP-1, MDC, MIP-4\u2014reveal immune cell involvement in WTC-LI pathogenesis. Findings of our automated biomarker identification warrant further investigation into these potential pharmacotherapy targets.<\/jats:p>","DOI":"10.1371\/journal.pcbi.1009144","type":"journal-article","created":{"date-parts":[[2021,7,21]],"date-time":"2021-07-21T17:50:13Z","timestamp":1626889813000},"page":"e1009144","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":11,"title":["PEDF, a pleiotropic WTC-LI biomarker: Machine learning biomarker identification and validation"],"prefix":"10.1371","volume":"17","author":[{"given":"George","family":"Crowley","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9257-2801","authenticated-orcid":true,"given":"James","family":"Kim","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3639-5107","authenticated-orcid":true,"given":"Sophia","family":"Kwon","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8731-9481","authenticated-orcid":true,"given":"Rachel","family":"Lam","sequence":"additional","affiliation":[]},{"given":"David J.","family":"Prezant","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9758-8522","authenticated-orcid":true,"given":"Mengling","family":"Liu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0631-1171","authenticated-orcid":true,"given":"Anna","family":"Nolan","sequence":"additional","affiliation":[]}],"member":"340","published-online":{"date-parts":[[2021,7,21]]},"reference":[{"issue":"23","key":"pcbi.1009144.ref001","doi-asserted-by":"crossref","first-page":"2810","DOI":"10.1161\/01.CIR.103.23.2810","article-title":"Increased particulate air pollution and the triggering of myocardial infarction","volume":"103","author":"A Peters","year":"2001","journal-title":"Circulation"},{"issue":"17","key":"pcbi.1009144.ref002","doi-asserted-by":"crossref","first-page":"1721","DOI":"10.1056\/NEJMoa040203","article-title":"Exposure to traffic and the onset of myocardial infarction","volume":"351","author":"A Peters","year":"2004","journal-title":"N Engl J Med"},{"issue":"12","key":"pcbi.1009144.ref003","doi-asserted-by":"crossref","first-page":"2549","DOI":"10.1161\/01.STR.0000189687.78760.47","article-title":"Air pollution and hospital admissions for ischemic and hemorrhagic stroke among medicare beneficiaries","volume":"36","author":"GA Wellenius","year":"2005","journal-title":"Stroke; 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