Engineered\nBimetallic MOF-Crafted Bullet Aids in Penetrating\nSerum Metabolic Traits of Chronic Obstructive Pulmonary Disease

Metabolomics analysis based on body fluids, combined\nwith high-throughput\nlaser desorption and ionization mass spectrometry (LDI-MS), holds\ngreat potential and promising prospects for disease diagnosis and\nscreening. On the other hand, chronic obstructive pulmonary disease\n(COPD) currently lacks innovative and powerful diagnostic and screening\nmethods. In this work, CoFeNMOF-D, a metal–organic framework\n(MOF)-derived metal oxide nanomaterial, was synthesized and utilized\nas a matrix to assist LDI-MS for extracting serum metabolic fingerprints\nof COPD patients and healthy controls (HC). Through machine learning\nalgorithms, successful discrimination between the COPD and HC was\nachieved. Furthermore, four potential biomarkers significantly downregulated\nin COPD were screened out. The disease diagnostic models based on\nthe biomarkers demonstrated excellent diagnostic performance across\ndifferent algorithms, with area under the curve (AUC) values reaching\n0.931 and 0.978 in the training and validation sets, respectively.\nFinally, the potential metabolic pathways and disease mechanisms associated\nwith the identified markers were explored. This work advances the\napplication of LDI-based molecular diagnostics in clinical settings.