High burden of clonal hematopoiesis in first responders exposed to the World Trade Center disaster.

The terrorist attacks on the World Trade Center (WTC) created an unprecedented environmental exposure to aerosolized dust, gases and potential carcinogens. Clonal hematopoiesis (CH) is defined as the acquisition of somatic mutations in blood cells and is associated with smoking and exposure to genotoxic stimuli. Here we show that deep targeted sequencing of blood samples identified a significantly higher proportion of WTC-exposed first responders with CH (10%; 48 out of 481) when compared with non-WTC-exposed firefighters (6.7%; 17 out of 255; odds ratio, 3.14; 95% confidence interval, 1.64-6.03; P = 0.0006) after controlling for age, sex and race/ethnicity. The frequency of somatic mutations in WTC-exposed first responders showed an age-related increase and predominantly affected DNMT3A, TET2 and other CH-associated genes. Exposure of lymphoblastoid cells to WTC particulate matter led to dysregulation of DNA replication at common fragile sites in vitro. Moreover, mice treated with WTC particulate matter developed an increased burden of mutations in hematopoietic stem and progenitor cell compartments. In summary, the high burden of CH in WTC-exposed first responders provides a rationale for enhanced screening and preventative efforts in this population.

Quantitative lung morphology: semi-automated measurement of mean linear intercept.

BACKGROUND: Quantifying morphologic changes is critical to our understanding of the pathophysiology of the lung. Mean linear intercept (MLI) measures are important in the assessment of clinically relevant pathology, such as emphysema. However, qualitative measures are prone to error and bias, while quantitative methods such as mean linear intercept (MLI) are manually time consuming. Furthermore, a fully automated, reliable method of assessment is nontrivial and resource-intensive. METHODS: We propose a semi-automated method to quantify MLI that does not require specialized computer knowledge and uses a free, open-source image-processor (Fiji). We tested the method with a computer-generated, idealized dataset, derived an MLI usage guide, and successfully applied this method to a murine model of particulate matter (PM) exposure. Fields of randomly placed, uniform-radius circles were analyzed. Optimal numbers of chords to assess based on MLI were found via receiver-operator-characteristic (ROC)-area under the curve (AUC) analysis. Intraclass correlation coefficient (ICC) measured reliability. RESULTS: We demonstrate high accuracy (AUCROC > 0.8 for MLIactual > 63.83 pixels) and excellent reliability (ICC = 0.9998, p < 0.0001). We provide a guide to optimize the number of chords to sample based on MLI. Processing time was 0.03 s/image. We showed elevated MLI in PM-exposed mice compared to PBS-exposed controls. We have also provided the macros that were used and have made an ImageJ plugin available free for academic research use at https://med.nyu.edu/nolanlab. CONCLUSIONS: Our semi-automated method is reliable, equally fast as fully automated methods, and uses free, open-source software. Additionally, we quantified the optimal number of chords that should be measured per lung field.

Assessing the Protective Metabolome Using Machine Learning in World Trade Center Particulate Exposed Firefighters at Risk for Lung Injury.

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.

Validation of Predictive Metabolic Syndrome Biomarkers of World Trade Center Lung Injury: A 16-Year Longitudinal Study.

BACKGROUND: Metabolic syndrome (MetSyn) predicted future development of World Trade Center lung injury (WTC-LI) in a subgroup of firefighters who never smoked and were male. An intracohort validation of MetSyn as a predictor of WTC-LI is examined in the cohort exposed to the World Trade Center (WTC) that has been followed longitudinally for 16 years. METHODS: Results of pulmonary function tests (n = 98,221) in workers exposed to the WTC (n = 9,566) were evaluated. A baseline cohort of firefighters who had normal FEV1 before 9/11 and who had had serum drawn before site closure on July 24, 2002 (n = 7,487) was investigated. Case subjects with WTC-LI (n = 1,208) were identified if they had at least two measured instances of FEV1 less than the lower limit of normal (LLN). Cox proportional hazards modeled early MetSyn biomarker ability to predict development of FEV1 less than the LLN. RESULTS: Case subjects were more likely to smoke, be highly exposed, and have MetSyn. There was a significant exposure dose response; the individuals most highly exposed had a 30.1% increased risk of developing WTC-LI, having MetSyn increased risk of developing WTC-LI by 55.7%, and smoking increased risk by 15.2%. There was significant interaction between smoking and exposure. CONCLUSIONS: We validated the usefulness of MetSyn to predict future WTC-LI in a larger population of individuals who were exposed. MetSyn defined by dyslipidemia, insulin resistance, and cardiovascular disease suggests that systemic inflammation can contribute to future lung function loss.

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

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

Non-Cardiac Chest Pain: A Review of Environmental Exposure-Associated Comorbidities and Biomarkers.

The prevalence of non-cardiac chest pain (NCCP) ranges from 13-33%. A majority of those presenting with a chief complaint of chest pain are found to have a diagnosis of NCCP. Aerodigestive diseases are a cause of NCCP, and billions of dollars are spent annually on the treatment of NCCP. Furthermore, NCCP can cause significant psychological stress. NCCP is commonly diagnosed when patients have chest pain despite a normal cardiac evaluation. The leading cause of NCCP is gastro-oesophageal reflux disease (GORD). GORD should be suspected in patients who report a history of acid regurgitation, cough, dysphagia, and bloating. Another common cause of NCCP is obstructive airway disease (OAD). A thorough history and review of the symptoms should be performed for those with suspected NCCP, especially because of the contributing end organs. It is known that environmental exposures can commonly cause GORD and OAD; however, NCCP has not been fully explored in the context of environmental exposure. Patients with a history of exposure to particulate matter can develop environmental-exposure-associated GORD and coexisting OAD. This narrative review aims to provide a practical overview of NCCP, its causes, their relation to environmental exposure, and associated biomarkers. The authors used a PubMed search that spanned 2003-2018 to accomplish this. Additionally, this review provides a broad overview of biomarkers of GORD-associated NCCP and OAD-associated NCCP due to environmental exposure.