Randomized trial identifies imaging subtypes of cerebral small vessel disease, suggesting tailored diagnosis and treatment strategies.
Background and aims To establish artificial intelligence-derived, Magnetic Resonance Imaging (MRI)-based etiological subtypes of cerebral small vascular disease (CSVD). Methods A multicenter, cohort study that enrolled 1,036 adults with mild cognitive impairment before September 31, 2025 with the first month follow-up was completed. MRI radiomic features were extracted using an artificial intelligence driven segmentation and clustering framework for etiological subtyping. The primary outcome was the clinical risks of the imaging-driven MCI subtypes. Results The analysis revealed three CSVD subtypes based on seven imaging features: a lacune-specific subtype, characterized by severe lacunes, was associated with lower high-density lipoprotein cholesterol (HDL-C) (mean [SD], 0.9 [0.2] mmol/L; OR, 3.27; 95% CI, 1.41-5.60), higher systolic blood pressure (SBP), and lower apolipoprotein A1 (mean [SD], 1.2 [0.3] g/L; OR, 1.01; 95% CI, 1.01-1.71); a white matter hyperintensities (WMHs)-specific subtype, marked by more severe WMHs, was associated with elevated SBP, normal HDL-C (mean [SD], 1.1 [0.2] mmol/L), and apolipoprotein A1 (mean [SD], 1.3 [0.2] g/L) levels; and a hippocampal-atrophy subtype, marked by significant hippocampal atrophy, was associated with fewer vascular risk factors (HDL-C, mean [SD], 1.2 [0.4] mmol/L; apolipoprotein A1, mean [SD], 1.3 [0.3] g/L). Visuospatial and language deficits were prominent in the lacune- and WMHs-specific subtypes. These clusters were validated in 1130 patients from the ADNI database. Abnormal HDL-C metabolism was linked to specific gene mutations. Conclusions This study identified and validated three distinct imaging subtypes of CSVD, providing a framework for precise diagnosis and targeted therapeutic strategies. Conflict of interest Jinjie Liu. nothing to disclose Figure 1 - belongs to Methods Table 1 - belongs to Results
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