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FRIDAY, Sept. 7, 2018 (HealthDay News) — Machine learning approaches to metabolite analysis can predict key pathways contributing to lung function loss associated with World Trade Center Lung Injury (WTC-LI), according to a small study published online Sept. 3 in BMJ Open Respiratory Research<\/i>.<\/p>\n
George Crowley, from New York University in New York City, and colleagues quantified the metabolome of serum for never-smoking, male, WTC-exposed firefighters with normal pre-9\/11 lung function, drawn within six months after 9\/11. Cases of WTC-LI (forced expiratory volume in 1s The researchers found that 580 metabolites qualified for random forests analysis. A refined metabolite profile correctly classified subjects with a 93.3 percent success rate. Within the refined profile, five clusters of metabolites emerged with prominent subpathways, including 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). Two-thirds (68.3 percent) of the variance in the five components was explained with principal component analysis of the refined profile, demonstrating class separation.<\/p>\n “Since metabolites are proximal markers of disease processes, metabolites could capture the complexity of past exposures and better inform treatment,” the authors write.<\/p>\n