New study highlights urgent need for specific biomarkers in vascular cognitive impairment and dementia diagnosis

In a recent review article published in Nature Reviews Neurology, scientists discuss the role of different 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 associated with VCID

After Alzheimer’s disease (AD), VCID is the next most common cause of dementia and accounts for about 20% of known cases. Understanding what causes each subtype can help researchers develop disease-specific interventions.

Diagnostic prevision for AD has improved because of the identification of molecular biomarkers and improvements in neuroimaging; however, more research on VCID is needed. At present, diagnosing VCID is reliant on neuroimaging and patient histories.

A hallmark of VCID is that it involves a cerebrovascular disease that causes brain injuries, which subsequently leads to 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 large blood vessels where atheromatous plaques rupture and cause atherosclerosis or atherothrombosis. LVDs are distinct from arterial stiffening, which naturally occurs with age.

In the case of cerebral cardioembolism, atrial fibrillation or another cardiac condition occludes cerebral blood vessels and causes thrombus formation. VCID can also be caused by intracerebral or subarachnoid hemorrhage resulting from a cerebral aneurysm rupture.

Biomarkers to differentiate 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, as well as all other neurodegenerative diseases, thus allowing for their use alongside other biomarkers to diagnose VCID.

Markers of inflammation, such as interleukin-6 in the cerebrospinal fluid (CSF-IL-6) or plasma levels of IL-1β, may be higher in patients with VCID as compared to healthy subjects; however, the evidence supporting this association is inconclusive. Further research, mainly focusing on biomarkers with high brain specificity, 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 can be challenging to distinguish between these two diseases. However, combining NfL, amyloid beta 42 (Aβ42), and the tau protein analyses can accurately differentiate between VCID and AD. Based on four cohort studies, lipocalin-2 also appears to have the potential to differentiate 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. However, the effects may be determined by where the lesion is located. For example, patients who present with frontal lobe dysfunction may be at a greater risk of white matter hyperintensities (WMHs) and lacunar strokes.

Since IL-6, IL-18, and MR-proADM are indicators of both frontal lobe dysfunction and WMH, these biomarkers could be used to assess VCID risk and monitor the progression of the disease. Homocysteine, which is a metabolite marker, cannot be used to diagnose VCID but can be used to assess the severity of the condition.

Pharmacodynamic biomarkers for clinical trials

The study highlights the importance of using blood biomarkers as primary outcome measures rather than the current practice of utilizing blood biomarkers as surrogates in clinical trials. However, this requires further clinical validation of each biomarker to ensure their measurement is standardized and relatively inexpensive. Machine learning could also support the identification of more potential biomarkers.

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

Conclusions

Improving the diagnosis of VCID necessitates establishing biomarker-based diagnostic techniques rather than relying on neuroimaging and clinical histories. The identification of VCID-specific biomarkers may also support the development of novel disease-specific interventions.