Silver‐Alloy Nanotube‐Enhanced Metabolic Fingerprints for the Multimodal Diagnosis of Heart Failure

ABSTRACT Clinical biomarkers and ejection fraction (EF) lack sufficient specificity for heart failure (HF) diagnosis. Metabolomics can report disease‐associated metabolic remodeling, but clinical translation is constrained by the lack of rapid, sensitive, and reproducible metabolite profiling. Here, we develop plasmonic silver‐based alloy nanotubes (AgMANTs, M = Au, Pt, Pd) as novel matrices for laser desorption/ionization mass spectrometry (LDI‐MS). Their engineered hollow‐porous architecture and tunable composition synergistically enhance ionization efficiency via near‐field effects and photothermal conversion. The optimized AgAuANTs achieve low detection limits (e.g., 0.22 pmol per spot for glucose), high reproducibility (CV < 3%), and 30‐day stability (CV < 10%). In a clinical cohort, machine‐learning models based on AgAuANTs‐enhanced serum metabolic fingerprints (SMFs) discriminate healthy controls from coronary heart disease (CHD) (AUC = 0.941) and HF (AUC = 0.951), and distinguish CHD from HF (AUC = 0.929). SMFs are acquired within seconds with minimal sample consumption and without complex pretreatment. Multimodal integration of metabolic biomarkers with routine clinical parameters further improves diagnostic performance (AUC > 0.98). Our platform provides a rapid and efficient method to acquire metabolic fingerprints as a potential diagnostic approach for HF and other diseases.