Characterization of Genetic Risk Factors of Cerebral Small Vessel Disease Based on Large-scale Transcriptome...

Cerebral small vessel disease (CSVD) is a major contributor to stroke and dementia, and it endangers the health of older individuals (>50 years old). Nevertheless, its clinical diagnosis predominantly depends on radiography. Moreover, omics studies and effective biomarkers for CSVD are still limited, and their pathogenesis has not been comprehensively clarified. To facilitate an in-depth understanding of CSVD at the molecular level and to characterize potential risk factors for this disease, we conducted a series of systematic transcriptome studies using peripheral blood samples from 91 Chinese patients with CSVD. By profiling the transcriptome heterogeneity between patients with CSVD and healthy individuals and conducting quantitative real-time PCR to validate differentially expressed genes, we established a comprehensive transcriptome map of CSVD and identified several statistically significant potential biomarkers for this disease. Our comparative analysis between dementia and non-dementia subgroups in CSVD patients highlights several differentially distributed neuroimaging and clinical features, such as total white matter hyperintensity severity, CSVD burden, neutrophil counts, and triglyceride levels, among others. More importantly, a CSVD-prediction model and a dementia-forecasting model have been constructed through machine learning methods, which achieve the average F1 scores of 0.93 and 0.73, respectively. Consequently, both models are anticipated to provide effective support for clinical predictions and diagnoses. In summary, this study delineates the transcriptome characteristics of CSVD, laying a foundation for research and clinical insights pertaining to CSVD.