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Biomarkers of disease
Metabolomics of World Trade Center-Lung Injury: a machine learning approach
  1. George Crowley1,
  2. Sophia Kwon1,
  3. Syed Hissam Haider1,
  4. Erin J Caraher1,
  5. Rachel Lam1,
  6. David E St-Jules2,
  7. Mengling Liu3,4,
  8. David J Prezant5,6 and
  9. Anna Nolan1,4,5
  1. 1 Department of Medicine, Division of Pulmonary, Critical Care and Sleep Medicine, New York University School of Medicine, New York, USA
  2. 2 Departmentof Population Health, Division of Health and Behavior, New York University School of Medicine, New York, USA
  3. 3 Department of Environmental Medicine, New York University School of Medicine, New York, USA
  4. 4 Bureau of Health Services and Office of Medical Affairs, Fire Department of New York, Brooklyn, New York, USA
  5. 5 Department of Population Health, Divison of Biostatistics, New York University School of Medicine, New York, USA
  6. 6 Department of Medicine, Pulmonary Medicine Divison, Montefiore Medical Center and Albert Einstein College of Medicine, Brooklyn, New York, USA
  1. Correspondence to Dr Anna Nolan; Anna.Nolan{at}med.nyu.edu

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.

  • systemic disease and lungs
  • occupational lung disease

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

https://doi.org/10.1136/bmjresp-2017-000274

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    Footnotes

    • Contributors All authors made substantial contributions to the study. All authors participated in study conception and design, data analysis and interpretation, and manuscript preparation. Primary investigator: AN; study design: AN, SK and GC; statistical analysis: GC, SK, ML and AN; data interpretation: GC, SK, SHH, EJC, RL, DES, ML, DJP and AN. All authors participated in writing and revision of the report and approval of the final version.

    • Funding NHLBI R01HL119326, CDC/NIOSH U01-OH011300 and Contract # 200-2011-3978. This work was also partially funded by the NYU-HHC CTSI supported by grant UL1TR000038 from the National Center for Advancing Translational Sciences of the NIH and by the Saperstein Scholars Fund.

    • Disclaimer The funding agencies did not participate in the study design; collection, analysis and interpretation of data; in the writing of the report; and in the decision to submit the article for publication.

    • Competing interests None declared.

    • Patient consent Not required.

    • Ethics approval All subjects, at the time of enrolment, consented to analysis of their information and samples for research according to Institutional Review Board approved protocols at Montefiore Medical Center (#07-09-320) and New York University (#16-01412).

    • Provenance and peer review Not commissioned; externally peer reviewed.

    • Data sharing statement AN is the primary investigator and guarantor of the content of this manuscript, including the data and analysis. Sharing of human data is governed by the World Trade Center (WTC) Clinical Center of Excellence program maintained by the Fire Department of New York (FDNY). All investigators will need to enter into a data use agreement with the FDNY WTC Clinical Center of Excellence. Additional information about this database may be obtained through DJP. He can be reached by email at prezand@fdny.nyc.gov.