RT Journal Article
SR Electronic
T1 Metabolomics of World Trade Center-Lung Injury: a machine learning approach
JF BMJ Open Respiratory Research
JO BMJ Open Resp Res
FD British Thoracic Society
SP e000274
DO 10.1136/bmjresp-2017-000274
VO 5
IS 1
A1 George Crowley
A1 Sophia Kwon
A1 Syed Hissam Haider
A1 Erin J Caraher
A1 Rachel Lam
A1 David E St-Jules
A1 Mengling Liu
A1 David J Prezant
A1 Anna Nolan
YR 2018
UL http://bmjopenrespres.bmj.com/content/5/1/e000274.abstract
AB Introduction Biomarkers of metabolic syndrome expressed soon after World Trade Center (WTC) exposure predict development of WTC Lung Injury (WTC-LI). The metabolome remains an untapped resource with potential to comprehensively characterise many aspects of WTC-LI. This case–control study identified a clinically relevant, robust subset of metabolic contributors of WTC-LI through comprehensive high-dimensional metabolic profiling and integration of machine learning techniques.Methods Never-smoking, male, WTC-exposed firefighters with normal pre-9/11 lung function were segregated by post-9/11 lung function. Cases of WTC-LI (forced expiratory volume in 1s <lower limit of normal, n=15) and controls (n=15) were identified from previous cohorts. The metabolome of serum drawn within 6 months of 9/11 was quantified. Machine learning was used for dimension reduction to identify metabolites associated with WTC-LI.Results 580 metabolites qualified for random forests (RF) analysis to identify a refined metabolite profile that yielded maximal class separation. RF of the refined profile correctly classified subjects with a 93.3% estimated success rate. 5 clusters of metabolites emerged within the refined profile. Prominent subpathways include known mediators of lung disease such as sphingolipids (elevated in cases of WTC-LI), and branched-chain amino acids (reduced in cases of WTC-LI). Principal component analysis of the refined profile explained 68.3% of variance in five components, demonstrating class separation.Conclusion Analysis of the metabolome of WTC-exposed 9/11 rescue workers has identified biologically plausible pathways associated with loss of lung function. Since metabolites are proximal markers of disease processes, metabolites could capture the complexity of past exposures and better inform treatment. These pathways warrant further mechanistic research.