Assessing the Protective Metabolome Using Machine Learning in World Trade Center Particulate Exposed Firefighters at Risk for Lung Injury

George Crowley, Sophia Kwon, Dean F. Ostrofsky, Emily A. Clementi, Syed Hissam Haider, Erin J. Caraher, Rachel Lam, David E. St-Jules, Mengling Liu, David J. Prezant, Anna Nolan

Research output: Contribution to journalArticle

Abstract

The metabolome of World Trade Center (WTC) particulate matter (PM) exposure has yet to be fully defined and may yield information that will further define bioactive pathways relevant to lung injury. A subset of Fire Department of New York firefighters demonstrated resistance to subsequent loss of lung function. We intend to characterize the metabolome of never smoking WTC-exposed firefighters, stratified by resistance to WTC-Lung Injury (WTC-LI) to determine metabolite pathways significant in subjects resistant to the loss of lung function. The global serum metabolome was determined in those resistant to WTC-LI and controls (n = 15 in each). Metabolites most important to class separation (top 5% by Random Forest (RF) of 594 qualified metabolites) included elevated amino acid and long-chain fatty acid metabolites, and reduced hexose monophosphate shunt metabolites in the resistant cohort. RF using the refined metabolic profile was able to classify cases and controls with an estimated success rate of 93.3%, and performed similarly upon cross-validation. Agglomerative hierarchical clustering identified potential influential pathways of resistance to the development of WTC-LI. These pathways represent potential therapeutic targets and warrant further research.

Original languageEnglish (US)
Article number11939
JournalScientific Reports
Volume9
Issue number1
DOIs
StatePublished - Dec 1 2019

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Firefighters
Metabolome
Lung Injury
Pentose Phosphate Pathway
Lung
Particulate Matter
Cluster Analysis
Fatty Acids
Smoking
Amino Acids
Machine Learning
Serum
Research
Forests

ASJC Scopus subject areas

  • General

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Assessing the Protective Metabolome Using Machine Learning in World Trade Center Particulate Exposed Firefighters at Risk for Lung Injury. / Crowley, George; Kwon, Sophia; Ostrofsky, Dean F.; Clementi, Emily A.; Haider, Syed Hissam; Caraher, Erin J.; Lam, Rachel; St-Jules, David E.; Liu, Mengling; Prezant, David J.; Nolan, Anna.

In: Scientific Reports, Vol. 9, No. 1, 11939, 01.12.2019.

Research output: Contribution to journalArticle

Crowley, G, Kwon, S, Ostrofsky, DF, Clementi, EA, Haider, SH, Caraher, EJ, Lam, R, St-Jules, DE, Liu, M, Prezant, DJ & Nolan, A 2019, 'Assessing the Protective Metabolome Using Machine Learning in World Trade Center Particulate Exposed Firefighters at Risk for Lung Injury', Scientific Reports, vol. 9, no. 1, 11939. https://doi.org/10.1038/s41598-019-48458-w
Crowley, George ; Kwon, Sophia ; Ostrofsky, Dean F. ; Clementi, Emily A. ; Haider, Syed Hissam ; Caraher, Erin J. ; Lam, Rachel ; St-Jules, David E. ; Liu, Mengling ; Prezant, David J. ; Nolan, Anna. / Assessing the Protective Metabolome Using Machine Learning in World Trade Center Particulate Exposed Firefighters at Risk for Lung Injury. In: Scientific Reports. 2019 ; Vol. 9, No. 1.
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