Cross‐Referencing Multifluid Metabolic Profiles on Hollow Dodecahedral Nanocages for Enhanced Disease Status Identification

Abstract The development of matrices has shown great potential for fluid metabolic analysis in disease detection. However, single‐fluid metabolomic analysis has been recognized as insufficient to fully capture the complexities of diseases such as liver disease, which limits detection accuracy. To this end, the hollow dodecahedral nanocages‐based analytical tool is developed, featuring four‐high characteristics of speed, throughput, efficiency, and patient compliance, to enhance extraction of multifluid metabolic profiles. The cross‐referencing of these profiles among different liver diseases, including hepatocellular carcinoma (HCC), chronic liver disease (CLD), and healthy controls, enhances the diagnosis of liver diseases, particularly achieving near‐perfect discrimination for HCC with an AUC value of 0.990, significantly outperforming any single fluid analysis. Additionally, the dynamic changes in expression levels of the key biomarkers throughout disease progression are explored, providing insights into their temporal evolution, and highlighting their role in monitoring disease status. This work highlights that multifluid metabolic analysis can comprehensively and sensitively reflect the disease status, enabling precise identification of complex diseases and facilitating personalized treatment.