Ocular biomarkers may aid diagnosis of neurocognitive disorders
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Patients with neurocognitive disorders often receive a late diagnosis due to the lack of affordable and time-efficient screening methods. Researchers are considering ocular biomarkers as a solution to this issue.
According to a review published in the European Journal of Ophthalmology, many diagnoses rely on imaging techniques like MRIs, but the high expense and invasiveness prevents physicians from using these as early screening methods.
“Given the fact that different types of dementia can be diagnosed only postmortem or when the disease has progressed enough to cause irreversible damage to certain brain areas, there has been an increasing need for the development of sensitive and reliable methods that can detect early preclinical forms of dementia, before the symptoms have even appeared,” Ioannis-Nikolaos Chalkias, professor, department of ophthalmology, School of Medicine, and Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, Makedonia Thraki, Greece, and colleagues wrote.
“Ideally, such a method would have the following characteristics: to be inexpensive, sensitive and specific, noninvasive, fast and easily accessible,” the authors added.
In a review paper, Chalkias and colleagues analyzed the ocular findings in patients with cognitive impairment disorders and the role ophthalmologic examinations can play in diagnoses of these disorders.
Visual changes, such as visual acuity, contrast sensitivity and color vision, have been used in studies to identify signs of neurocognitive disorders (NCDs), but they have yet to be used to provide early diagnoses. Ocular biomarkers have also proven to be useful, and tear film has been “promising.”
Chalkias and colleagues discussed a study in which 14 patients with Alzheimer’s disease (AD) and nine age-matched control patients allowed researchers to measure their tear protein concentration and tear production rate. The results revealed that the AD group had a higher concentration of miRNA content in their tears. With more research, researchers believe tear film could become an early screening method for neurocognitive impairment.
Artificial intelligence (AI) also may have a future in diagnosing AD specifically with retina research. The researchers referenced a paper in which machine learning classified AD patients based on images from retinal vasculature with an accuracy of 82.44%. However, while AI could be helpful in diagnosing NCDs, it is expensive and time consuming.
Similarly, OCT offers a noninvasive screening method by using light to take cross-section scans of the retina. Because patients with AD often experience thinning of the peripapillary retinal nerve fiber layer, an OCT can catch the thinning in a scan. OCT-angiography (OCT-A) also has potential as a screening method of NCDs. It detects the movement of flowing red blood cells by taking OCT scans and turning them into structural images.
“Many studies have provided various biomarkers for the diagnosis of these disorders,” Chalkias and colleagues wrote. “The results are promising, however more studies are needed in order to find a biomarker that will be inexpensive, noninvasive, highly sensitive and specific and will have the ability to diagnose the disease early before symptoms even begin. If we manage to find a biomarker with these characteristics, we will be able to early identify patients at high-risk for NCDs.”
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This review highlights the unmet clinical need for the identification of repeatable, specific, noninvasive ocular biomarkers of neurocognitive disease diagnosis and progression with a particular focus on one cause of dementia, Alzheimer’s disease.
As cognitive decline occurs late in the Alzheimer’s disease spectrum, there has been a shift toward identification of preclinical structural and functional biomarkers. Ocular parameters, including retinal nerve fiber layer, ganglion cell layer-inner plexiform layer and retinal capillary density measurements, are of particular interest due to their objective, noninvasive and repeatable nature.
The challenge of clinical application of OCT-based biomarkers is that they are not specific to a particular disease process, but rather are indicators of damage that may be derived from a variety of etiologies. The complexity of the pathophysiology of neurocognitive conditions, coupled with individual anatomic variation and confounding systemic and ocular conditions, make ocular parameters alone inappropriate screening tools for neurocognitive disease diagnosis or progression.
Rather than attempting to isolate a specific ocular biomarker, which may be impacted in a neurocognitive disease process, identification of a biomarker signature composed of multiple functional and structural biomarkers, which may be enhanced by machine-learning, may be most useful for early diagnosis, detection of progression and to serve as targets for therapeutic intervention.
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