Integrated Diagnostic and Mechanistic Profiling of Moyamoya and Intracranial Atherosclerotic Disease via Serum Metabolic Fingerprinting Based on SCs-ZIF-8@Au Nanomaterials

Abstract Accurate differentiation between moyamoya disease (MMD) and intracranial atherosclerotic disease (ICAD) remains clinically challenging yet crucial for guiding effective therapeutic strategies. Here, we developed a novel nanomaterial platform (SCs-ZIF-8@Au) to perform high-resolution serum metabolic fingerprinting using nanoparticle-assisted laser desorption/ionization mass spectrometry. By integrating machine learning algorithms with metabolic profiling, we established a robust diagnostic model that accurately distinguished MMD from ICAD and healthy controls (HCs). In a cohort comprising 178 MMD patients, 75 ICAD patients, and 103 HCs, our classifier achieved area under the curve (AUC) values of 0.97 (MMD vs HCs), 0.98 (ICAD vs HCs), and 0.88 (MMD vs ICAD). Beyond diagnostics, mechanistic exploration of key metabolic biomarkers revealed that reduced mevalonic acid in MMD impairs endothelial tube formation and migration by disrupting tight junction integrity, attenuating VEGF signaling, and altering calcium homeostasis in human endothelial cells. These findings link dysregulated mevalonate metabolism to pathological angiogenesis in MMD and support its role in endothelial dysfunction. Together, our study provides a clinically applicable, noninvasive diagnostic tool for MMD and ICAD, while uncovering a previously unrecognized pathogenic mechanism that connects systemic metabolic alterations to cerebrovascular remodeling in MMD.