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Characterization of Genetic Risk Factors of Cerebral Small Vessel Disease Based on Large-scale Transcriptome
Fengyu Wang1, Jingyao Zeng2,3, Qiheng Qian2,3,4
1Department of Neurology, Henan Provincial People's Hospital, People's Hospital of Zhengzhou University, Zhengzhou 450003, China.
Insights
Cerebral small vessel disease (CSVD) impacts older adults, increasing stroke and dementia risk. This study maps CSVD
Area of Science:
- Neuroscience
- Genomics
- Biomarker Discovery
Background:
- Cerebral small vessel disease (CSVD) significantly contributes to stroke and dementia in individuals over 50.
- Current diagnosis relies heavily on radiography, with limited omics data and unclear pathogenesis.
- Effective biomarkers for CSVD remain scarce, hindering comprehensive understanding and treatment.
Purpose of the Study:
- To deepen the molecular understanding of CSVD.
- To identify potential risk factors and biomarkers for CSVD.
- To develop predictive models for CSVD and associated dementia.
Main Methods:
- Conducted transcriptome studies on peripheral blood samples from 91 Chinese CSVD patients.
- Compared transcriptome profiles between CSVD patients and healthy controls.
- Validated differentially expressed genes using quantitative real-time PCR.
- Utilized machine learning to construct prediction and forecasting models.
Main Results:
- Established a comprehensive transcriptome map of CSVD.
- Identified statistically significant potential biomarkers for CSVD.
- Highlighted key clinical and neuroimaging differences between dementia and non-dementia CSVD subgroups (e.g., white matter hyperintensity, neutrophil counts).
- Developed CSVD-prediction (F1=0.93) and dementia-forecasting (F1=0.73) models.
Conclusions:
- The study delineates CSVD transcriptome characteristics, offering molecular insights.
- Identified potential biomarkers and clinical features associated with CSVD and dementia.
- Developed machine learning models to aid in clinical prediction and diagnosis of CSVD and dementia.
Abstract:
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.
