Vascular cognitive impairment and dementia (VCID) is a major public health concern because of the increased incidence of vascular disease in the aging population and the impact of vascular disease on Alzheimer's disease. VCID is a heterogeneous group of diseases for which there are no proven treatments. Biomarkers can be used to select more homogeneous populations. Small vessel disease is the most prevalent form of VCID and is the optimal form for treatment trials because there is a progressive course with characteristic pathological changes. Subcortical ischemic vascular disease of the Binswanger type (SIVD-BD) has a characteristic set of features that can be used both to identify patients and to follow treatment. SIVD-BD patients have clinical, neuropsychological, cerebrospinal fluid (CSF) and imaging features that can be used as biomarkers. No one feature is diagnostic, but a multimodal approach defines the SIVD-BD spectrum disorder. The most important features are large white matter lesions with axonal damage, blood-brain barrier disruption as shown by magnetic resonance imaging and CSF, and neuropsychological evidence of executive dysfunction. We have used these features to create a Binswanger Disease Scale and a probability of SIVD-BD, using a machine-learning algorithm. The patients discussed in this review are derived from published studies. Biomarkers not only aid in early diagnosis before the disease process has progressed too far for treatment, but also can indicate response to treatment. Refining the use of biomarkers will allow dementia treatment to enter the era of precision medicine. This article is part of the Special Issue "Vascular Dementia".
Keywords: Binswanger's disease; biomarkers; matrix metalloproteinases; neuroinflammation; proton MR spectroscopy; vascular cognitive impairment.
© 2017 International Society for Neurochemistry.