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. 2018 Sep 3;5(1):e000274.
doi: 10.1136/bmjresp-2017-000274. eCollection 2018.

Metabolomics of World Trade Center-Lung Injury: a machine learning approach

George Crowley  1 , Sophia Kwon  1 , Syed Hissam Haider  1 , Erin J Caraher  1 , Rachel Lam  1 , David E St-Jules  2 , Mengling Liu  3   4 , David J Prezant  5   6 , Anna Nolan  1   4   5
Affiliations

Metabolomics of World Trade Center-Lung Injury: a machine learning approach

George Crowley et al. BMJ Open Respir Res. .

Erratum in

Abstract

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.

Keywords: occupational lung disease; systemic disease and lungs.

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Conflict of interest statement

Competing interests: None declared.

Figures

Figure 1
Figure 1
Study design. Metabolome assessment cases of World Trade Center Lung Injury (WTC-LI) and controls were selected. FEV, forced expiratory volume; LLN, lower limit of normal; MMTP, monitoring and treatment programme; NHANES PFT, National Health and Nutrition Examination Survey Pulmonary Function Test.
Figure 2
Figure 2
Demonstration of model optimisation: principal component analysis (PCA) scores plot. (A) PCA of the qualified profile reveals heterogeneity in the data. (B) PCA of the refined profile demonstrates improved class separation produced by the refined profile compared with initial PCA (A). WTC-LI, World Trade Center Lung Injury.
Figure 3
Figure 3
Random forests (RF) variable importance in projection. RF variable importance in projection is measured by mean decrease accuracy; the top 5% of metabolites important to class separation are shown. The confusion matrix shows classification accuracy of the refined profile. PUFA, polyunsaturated fatty acids.
Figure 4
Figure 4
(A) Refined profile principal component analysis loading weights plot was used to derive insight into possible association of biomarkers. (B) Correlation heatmap. Correlation matrix of refined profile subjected to hierarchical clustering using Pearson correlation as a distance metric.
Figure 5
Figure 5
Pathway visualisation. Metabolic pathways of sphingolipids and phospholipids reveal pathways involving key metabolites, and that sphingolipid metabolism is linked with phospholipid metabolism by long-chain fatty acid metabolism. Node size correlates to fold change (red—up, blue—down, cases of World Trade Center Lung Injury/control).
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