Molecular and multimodal biomarkers in Moyamoya disease: from pathogenic mechanisms to clinical translation.

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Abstract

Moyamoya disease (MMD) is a chronic, progressive cerebrovascular condition marked by narrowing or blockage of the terminal segments of the internal carotid arteries, resulting in ischemic and hemorrhagic strokes. Its pathogenesis involves a multifactorial interplay between genetic susceptibility, immune-inflammatory dysregulation, endothelial dysfunction, and aberrant vascular remodeling, influenced by non-genetic and environmental factors. Despite considerable research progress, clinically useful biomarkers remain limited, lacking sufficient sensitivity and specificity for predicting disease onset, progression, or treatment response. Current management relies primarily on surgical revascularization, which restores cerebral perfusion but does not address underlying biological mechanisms, while pharmacological interventions remain largely empirical and nonspecific. This review systematically searched PubMed and Web of Science up to September 2025 using combinations of "Moyamoya disease" and "biomarker" with "genomics," "transcriptomics," "proteomics," "metabolomics," "neuroimaging," "artificial intelligence," and "machine learning." We summarize recent advances in genetic and molecular biomarker discovery, including the identification of RNF213 as a major susceptibility gene in East Asian populations, alongside emerging roles of variants in MTHFR, DIAPH1, and GUCY1A3. Beyond genomics, proteomic and metabolomic profiling have revealed dysregulation of vascular repair pathways, extracellular-matrix remodeling, and lipid-amino acid metabolism, offering new insights into disease heterogeneity and progression. Noncoding RNAs and exosome-derived biomarkers-such as plasma miR-512-3p, miR-328-3p, and miR-125b-5p-have shown potential as minimally invasive tools for diagnosis and monitoring, linking posttranscriptional regulation to vascular pathophysiology. Parallel advances in neuroimaging biomarkers, enhanced by artificial intelligence (AI), are enabling the integration of morphological and hemodynamic data with molecular findings. Deep learning-based models trained on digital subtraction angiography (DSA), computed tomography angiography (CTA), and retinal imaging have achieved diagnostic accuracies exceeding 90%, while multimodal integration approaches are beginning to correlate imaging phenotypes with genetic and metabolic profiles. Future research must transcend single-omics paradigms to establish integrative, multidimensional frameworks that connect genetic variation to cellular function, vascular remodeling, and clinical phenotype. Progress will depend on international multicenter collaboration, open-access biomarker databases, and the incorporation of explainable AI to bridge discovery and clinical application. Together, these developments may usher in biomarker-driven precision diagnosis and personalized therapy for MMD.

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