In a recent review article published in Nature Reviews Neurology, scientists discuss the role of various pathophysiological processes contributing to vascular cognitive impairment and dementia (VCID).

Study: Molecular biomarkers for vascular cognitive impairment and dementia. Image Credit: sfam_photo / Shutterstock.com

Brain injuries related to VCID

After Alzheimer’s disease (AD), VCID is the subsequent commonest reason behind dementia and accounts for about 20% of known cases. Understanding what causes each subtype may also help researchers develop disease-specific interventions.

Diagnostic prevision for AD has improved due to identification of molecular biomarkers and enhancements in neuroimaging; nevertheless, more research on VCID is required. At present, diagnosing VCID is reliant on neuroimaging and patient histories.

A trademark of VCID is that it involves a cerebrovascular disease that causes brain injuries, which subsequently results in cognitive deficits and dementia. These brain injuries include small vessel disease (SVD), large vessel disease (LVD), cerebral cardioembolism, and intracranial hemorrhage.

SVD is an umbrella term for various diseases, including endothelial dysfunction, blood-brain barrier breakdown (BBB), oxidative stress, inflammation, clotting pathway dysfunction, and neuron and glial degeneration. SVDs manifest as cerebral microbleeds and lacunes in small blood vessels.

Similarly, LVD refers to several conditions in medium and huge blood vessels where atheromatous plaques rupture and cause atherosclerosis or atherothrombosis. LVDs are distinct from arterial stiffening, which naturally occurs with age.

Within the case of cerebral cardioembolism, atrial fibrillation or one other cardiac condition occludes cerebral blood vessels and causes thrombus formation. VCID will also be attributable to intracerebral or subarachnoid hemorrhage resulting from a cerebral aneurysm rupture.

Biomarkers to distinguish VCID from AD

Neurofilament light chain (NfL) and glial fibrillary acidic protein (GFAP) are two markers of neuronal and glial fibrillary degeneration. NfL and GFAP levels are elevated in VCID, in addition to all other neurodegenerative diseases, thus allowing for his or her use alongside other biomarkers to diagnose VCID.

Markers of inflammation, resembling interleukin-6 within the cerebrospinal fluid (CSF-IL-6) or plasma levels of IL-1β, could also be higher in patients with VCID as in comparison with healthy subjects; nevertheless, the evidence supporting this association is inconclusive. Further research, mainly specializing in biomarkers with high brain specificity, just like the placental growth factor (PlGF) and mid-regional pro-adrenomedullin (MR-proADM), could facilitate more specific and non-invasive approaches to diagnose VCID.

Since AD and VCID share several pathophysiological pathways, it may possibly be difficult to tell apart between these two diseases. Nonetheless, combining NfL, amyloid beta 42 (Aβ42), and the tau protein analyses can accurately differentiate between VCID and AD. Based on 4 cohort studies, lipocalin-2 also appears to have the potential to distinguish between subjects with VCID and AD.

Risk biomarkers, monitoring, and disease progression

Previous studies suggest that white matter lesions are strongly correlated with cognitive decline. Nonetheless, the consequences could also be determined by where the lesion is positioned. For instance, patients who present with frontal lobe dysfunction could also be at a greater risk of white matter hyperintensities (WMHs) and lacunar strokes.

Since IL-6, IL-18, and MR-proADM are indicators of each frontal lobe dysfunction and WMH, these biomarkers might be used to evaluate VCID risk and monitor the progression of the disease. Homocysteine, which is a metabolite marker, can’t be used to diagnose VCID but will be used to evaluate the severity of the condition.

Pharmacodynamic biomarkers for clinical trials

The study highlights the importance of using blood biomarkers as primary end result measures fairly than the present practice of utilizing blood biomarkers as surrogates in clinical trials. Nonetheless, this requires further clinical validation of every biomarker to make sure their measurement is standardized and comparatively inexpensive. Machine learning could also support the identification of more potential biomarkers.

Prognostic and diagnostic parameters have to be defined through robust cognitive and clinical assessments. Developing a framework to define biomarkers for VCID could be a vital step forward, followed by validation across various populations.

Conclusions

Improving the diagnosis of VCID necessitates establishing biomarker-based diagnostic techniques fairly than counting on neuroimaging and clinical histories. The identification of VCID-specific biomarkers might also support the event of novel disease-specific interventions.