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.
Highlights
Inherent in the data, providing a better overview of the relevant metabolites
We hypothesize that implementing high-dimensional data analysis and dimension reduction techniques on the metabolomic fingerprints of World Trade Center (WTC)-particulate matter (PM) exposure in the serum of firefighters will further refine biologically relevant pathways of resistance to WTC-Lung Injury (WTC-LI)
ResistantWTC-LI with metabolome assessed did not differ from their parent cohort in spirometry, body mass index (BMI), age at exposure at the WTC site, exposure intensity, lipid profiles, blood pressure, heart rate, leukocyte differential percents, or serum sodium, chloride, potassium, glucose, uric acid, total protein, calcium, phosphorous, iron, CO2, albumin, blood urea nitrogen (BUN), creatinine, albumin/creatinine, or BUN/creatinine
Summary
Inherent in the data, providing a better overview of the relevant metabolites. We rely on Random Forests (RF) for this initial selection of a refined metabolite profile maximally relevant to class differentiation. RF records an unbiased measure of each variable’s importance to classification success rate, called mean-decrease-accuracy (MDA), and we can select the highest-MDA metabolites for our refined profile. To further our data interpretation, dimension reduction techniques were employed. Principal components analysis (PCA) of the refined profile provided a low-dimensional view that captures maximal variance. We uncovered structure in the refined profile of the metabolome through agglomerative, hierarchical clustering of the data and correlation matrices. We hypothesize that implementing high-dimensional data analysis and dimension reduction techniques on the metabolomic fingerprints of WTC-PM exposure in the serum of firefighters will further refine biologically relevant pathways of resistance to WTC-LI
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