Metabolic biomarker validation and clinomics of World Trade Center-Lung injury

Introduction: Metabolic biomarkers predict World Trade Center lung injury (WTC-LI), FEV 1 less than the lower limit of normal (LLN), in a pilot study. We now validate these findings in the larger cohort. We identify distinct phenotypic clusters to further isolate endotypes that would benefit different treatment strategies. Methods: Using hierarchical clustering and principal component analysis we identified clinically relevant patterns in the larger cohort. Firefighters had FEV 1 ≥LLN prior to 9/11 and database was closed as of March 2015. Of the remaining N=7711, N=787 developed WTC-LI and N=6813 had FEV≥LLN. We modeled the ability of biomarkers at the first post-9/11 exam to predict FEV below LLN at the most recent PFT with binary logistic regression. Results: Two-step clustering identified one group with high triglycerides, glucose, and exposure intensity as strongly associated with WTC-LI, Figure 1A. Systolic blood pressure, dyslipidemia, glucose, and smoking are significant predictors in a regression after adjusting for age and exposure intensity, Figure 1B. Conclusion: We have validated biomarkers of metabolic syndrome also seen in our prior pilot to predict WTC-lung injury in a larger population of exposed individuals. These biomarkers are associated with dyslipidemia, insulin resistance, and cardiovascular disease, and suggest that systemic inflammation can contribute to future lung function loss.