Cerebral small vessel disease (CSVD) is a common, chronic disorder of the small vessels of the brain that leads to gradual loss of their elasticity, fibrosis, and, finally, to brain tissue damage. This often results in cognitive decline and dementia at older ages, however, signs of injury are visible on imaging studies years in advance. CSVD in the general population is thought to result mainly from cardiovascular risk factors, such as hypertension and diabetes, but not all individuals with hypertension manifest imaging signs, which suggests that many variables contribute to disease development. We are especially interested in understanding the association of genetic risk with biological mediators of VCID. In this project, we seek to combine electronic health records with imaging, genetics, and proteomics to create machine learning models that will predict transition of asymptomatic cSVD to dementia. We will investigate monogenic as well as polygenic risk of disease.
