Multiplex Sensing and Molecular Classification of Osteoarthritis via a Biomarker-Responsive Dual-Nanozyme Activity Sensor Array

Precision diagnosis and molecular subtyping of arthritis call for analytical tools capable of providing objective, dynamic, and multiparametric readouts of disease progression and severity. Current clinical assessments mainly depend on symptom evaluation, imaging, and nonspecific biochemical indicators, lacking highly specific biomarkers and suffering from subjective interpretation and inconsistent multiparameter integration. To address this challenge, we designed a dual-nanozyme activity sensor array using metal-oxide-functionalized porphyrinic metal-organic frameworks (MOFs) for simultaneous and parallel multibiomarker detection and molecular classification of osteoarthritis (OA). Three types of metal oxide@PCN-224 nanomaterials with distinct metal centers were synthesized, and their peroxidase (POD)- and laccase (LAC)-mimicking activities were systematically investigated. Leveraging their differential dual-mimic activities, a multichannel sensing array for the simultaneous and discriminative detection of key OA-related biomarkers including C-reactive protein (CRP), hyaluronidase (HAase), matrix metalloproteinase-13 (MMP-13), tumor necrosis factor-alpha (TNF-α), interleukin-1beta (IL-1β), and interleukin-17 (IL-17) was constructed. Each biomarker interacts with the nanozymes to generate unique catalytic activity modulation "fingerprints" due to the difference in the nanozyme-biomarker interaction. Depending on their size, charge, and functional groups, biomarkers may undergo adsorption on the surface or alter the steric hindrance, local charge distribution, or interfacial electron transfer, leading to either inhibition or promotion of the activity of nanozymes. Integrated with pattern recognition algorithms, this sensing array not only addresses the specificity shortfall of single biomarker analysis but also achieves high-accuracy classification of synovial fluid samples from clinically staged OA patients (mild and serious). By integrating three materials with dual-enzyme activities, the sensor array can generate more diverse and distinguishable "fingerprint" signals, significantly improving classification accuracy and robustness. By correlating clinical staging and multifactor molecular fingerprints, this work offers an innovative integrated sensing strategy for the objective diagnosis, dynamic monitoring, and precise management of OA.