Vacancy-driven High-performance Metabolic Assay for Diagnosis and Therapeutic Evaluation of Depression.

Depression is one of the most common mental illnesses and is a well-known risk factor for suicide, characterized by low overall efficacy (< 50%) and high relapse rate (40%). A rapid and objective approach for screening and prognosis of depression is highly desirable but still awaits further development. Herein, we present a high-performance metabolite-based assay to aid the diagnosis and therapeutic evaluation of depression by developing a vacancy-engineered cobalt oxide (Vo-Co3 O4 ) assisted laser desorption/ionization mass spectrometer platform. The nanoparticles with optimal vacancy achieve a signal enhancement (up to 20 folds than the commercialized products), characterized by favorable charge transfer and increased photothermal conversion. The optimized Vo-Co3 O4 allows for a direct and robust record of plasma metabolic fingerprints (PMFs). Through machine learning of PMFs, high-performance depression diagnosis is achieved, with the areas under the curve (AUC) of 0.941-0.980 and an accuracy of over 92%. Further, a simplified diagnostic panel for depression is established, with a desirable AUC value of 0.933. Finally, we quantify proline levels in a follow-up cohort of depressive patients, highlighting the potential of metabolite quantification in the therapeutic evaluation of depression. Our work promotes the progression of advanced matrixes and brings insights into the management of depression. This article is protected by copyright. All rights reserved.