ArticleBMJ open respiratory research2018
Metabolomics of World Trade Center-Lung Injury: a machine learning approach.
Article in BMJ open respiratory research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 21 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Receptor for advanced glycation end-products and environmental exposure related obstructive airways disease: a systematic review.European respiratory review : an official journal of the European Respiratory Society · 2019Pooled it
- Single-Cell Metabolic Profiling in a Glioblastoma Coculture Model Using AP-MALDI-Based Mass Spectrometry Imaging.Analytical chemistry · 2026Article
- Biomarkers of Airway Disease, Barrett's and Underdiagnosed Reflux Noninvasively (BAD-BURN) in World Trade Center exposed firefighters: a case-control observational study protocol.BMC gastroenterology · 2024Article
- Biomarkers of Airway Disease, Barrett's and Underdiagnosed Reflux Noninvasively (BAD-BURN): a Case-Control Observational Study Protocol.Research square · 2024Article
- Metabolomic Signatures Associated with Radiation-Induced Lung Injury by Correlating Lung Tissue to Plasma in a Rat Model.Metabolites · 2023Article
- Metabolomics of Respiratory Diseases.Handbook of experimental pharmacology · 2023Review
- Molecular Clustering Analysis of Blood Biomarkers in World Trade Center Exposed Community Members with Persistent Lower Respiratory Symptoms.International journal of environmental research and public health · 2022Article
- Health effects following exposure to dust from the World Trade Center disaster: An update.Life sciences · 2022Review
- Review
- Dynamic Metabolic Risk Profiling of World Trade Center Lung Disease: A Longitudinal Cohort Study.American journal of respiratory and critical care medicine · 2021Article
- PEDF, a pleiotropic WTC-LI biomarker: Machine learning biomarker identification and validation.PLoS computational biology · 2021Article
- Dietary phenotype and advanced glycation end-products predict WTC-obstructive airways disease: a longitudinal observational study.Respiratory research · 2021Observational
- Food Intake REstriction for Health OUtcome Support and Education (FIREHOUSE) Protocol: A Randomized Clinical Trial.International journal of environmental research and public health · 2020Article
- Multiomics of World Trade Center Particulate Matter-induced Persistent Airway Hyperreactivity. Role of Receptor for Advanced Glycation End Products.American journal of respiratory cell and molecular biology · 2020Article
- Synergistic Effect of WTC-Particulate Matter and Lysophosphatidic Acid Exposure and the Role of RAGE: In-Vitro and Translational Assessment.International journal of environmental research and public health · 2020Article
- World Trade Center-Cardiorespiratory and Vascular Dysfunction: Assessing the Phenotype and Metabolome of a Murine Particulate Matter Exposure Model.Scientific reports · 2020Article
- Assessing the Protective Metabolome Using Machine Learning in World Trade Center Particulate Exposed Firefighters at Risk for Lung Injury.Scientific reports · 2019Article
- Article
- Metabolic Syndrome Biomarkers of World Trade Center Airway Hyperreactivity: A 16-Year Prospective Cohort Study.International journal of environmental research and public health · 2019Article
- Metabolomics Identifies Novel Blood Biomarkers of Pulmonary Function and COPD in the General Population.Metabolites · 2019Article
Corrections and comments
- Erratum issuedCorrection:2018
Authors and funding
9 authors.
Funding
Abstract
introductionBiomarkers 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.
methodsNever-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.
results580 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.
conclusionAnalysis 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.