Pulmonary Fibrosis Diagnosis and Disease Progression Detected Via Hair Metabolome Analysis.

backgroundFibrotic interstitial lung disease is often identified late due to non-specific symptoms, inadequate access to specialist care, and clinical unawareness precluding proper and timely treatment. Biopsy histological analysis is definitive but rarely performed due to its invasiveness. Diagnosis typically relies on high-resolution computed tomography, while disease progression is evaluated via frequent pulmonary function testing. This study tested the hypothesis that pulmonary fibrosis diagnosis and progression could be non-invasively and accurately evaluated from the hair metabolome, with the longer-term goal to minimize patient discomfort.

methodsHair specimens collected from pulmonary fibrosis patients (n = 56) and healthy subjects (n = 14) were processed for metabolite extraction using 2DLC/MS-MS, and data were analyzed via machine learning. Metabolomic data were used to train machine learning classification models tuned via a rigorous combination of cross validation, feature selection, and testing with a hold-out dataset to evaluate classifications of diseased vs. healthy subjects and stable vs. progressed disease.

resultsPrediction of pulmonary fibrosis vs. healthy achieved AUROC

conclusionThis study provides novel evidence that pulmonary fibrosis diagnosis and progression may in principle be evaluated from the hair metabolome. Longer term, this approach may facilitate non-invasive and accurate detection and monitoring of fibrotic lung diseases.