Using Technology to Help Alleviate Race and Ethnic Disparities in Alzheimer’s Disease Research

by | Oct 24, 2020 | Alzheimer's Disease | 0 comments

Due to rising life expectancy, Alzheimer’s disease is an ever growing economic and health concern. In fact, Alzheimer’s disease and other related dementias are predicted to be the greatest challenge facing healthcare and medical systems across the world (1). To provide care for Alzheimer’s disease and other dementia patients, America’s total annual payments are projected to increase from 405 billion dollars in 2020 to more than 1.1 trillion dollars in 2050 (2). While our understanding of this illness has dramatically increased over recent years, there is currently no available cure for Alzheimer’s disease. Moreover, much of the current literature fails to address how the intersections of race, ethnicities, and other factors alter the pathology and thus diagnosis and care of patients with this disease. 

Alzheimer’s disease is progressive neurodegenerative disorder characterized by brain atrophy associated with amyloid plaques and neurofibrillary tangles. These abnormalities in proteins can be detected with structural imaging such as MRI and PET scans; cerebrospinal fluid (CSF) and other blood-based biomarkers examinations are also used to aid in positive molecular diagnosis of this disease (3). However, these aforementioned tools are often expensive, require advanced laboratory equipment and expertise, or invasive procedures, and thus are made inaccessible to certain communities (1). Additionally, racial differences in quantification of molecular biomarkers of Alzheimer’s disease suggest race-dependent mechanisms, making it even more challenging for physicians to accurately characterize Alzheimer’s diseases; for instance, Morris et al. found significant differences in concentrations of tau and phosphorylated tau181 in CSF of African American individuals in comparison to white individuals, and thus they suggest that biomarkers used to diagnose should be adjusted for race (4).

Ethnic and racial factors also alter the degree of which researchers and physicians quantify the patient’s cognitive change, another factor of Alzehimer’s disease pathology (5). At the clinical level, Mini-mental State Examinations (MMSE) and Montreal Cognitive Assessment (MoCA) are widely used screening tools for mild cognitive impairment and Alzheimer’s, though they are weak as a single tool for monitoring progression of MCI towards possible AD (6,7). Nonetheless, there have been concerns whether they are objective screening tools, since their scoring can be flawed with several biases including interpretation, educational level of the patient and cultural factors (8). Since these cognitive measures are influenced by linguistic, education or cultural factors, it becomes challenging to assess the decline in ethnoracial groups that have limited formal education and literacy rates. Here, race plays an important role in dementia research because it may serve as a proxy measure of the complex web of interrelated social, cultural, economic, and behavioral characteristics present over the life course that may be associated with dementia risk (9). In fact, while it has been found that Black and white individuals have similar rates of cognitive decline, since Black individuals on average tend to have poorer cognitive baseline function they are disproportionately diagnosed with dementia (9). 

Interestingly, eye movement patterns can be elicited by physical, noninvasive examinations, and changes within these patterns can be linked with Alzheimer’s. More specifically, patients with Alzheimer’s disease have increased saccade latency and reduced peak velocity of the saccades (10,11). The two most consistent impairments in saccades that have emerged from Alzheimer’s disease research are an increased frequency of saccadic intrusions during fixation of a visual target, and errors in the antisaccade process (10,11). Esurgi is developing Eye AD that utilizes the aforementioned specific patterns of saccadic eye movement to monitor and diagnose for the disease (12). It will also serve as a more objective and accessible tool that healthcare providers can use to hopefully achieve more accurate diagnosis and alleviate disparities (12). With upcoming technology such as the Eye AD, healthcare providers can work to increase quality of care and decrease burden in lives of Alzheimer’s patients within marginalized communities (9,12). What do you see as the utility of Eye AD in early detection and ongoing monitoring of AD?

Sources: 

  1. Dehghani C, Frost S, Jayasena R, Masters CL, Kanagasingam Y. Ocular Biomarkers of Alzheimer’s Disease: The Role of Anterior Eye and Potential Future Directions. Invest Ophthalmol Vis Sci. 2018;59(8):3554-3563. doi:10.1167/iovs.18-24694
  2. alzheimers-facts-and-figures.pdf. Accessed September 6, 2020. https://www.alz.org/media/Documents/alzheimers-facts-and-figures.pdf
  3. Lane CA, Hardy J, Schott JM. Alzheimer’s disease. Eur J Neurol. 2018;25(1):59-70. doi:10.1111/ene.13439
  4. Morris JC, Schindler SE, McCue LM, et al. Assessment of Racial Disparities in Biomarkers for Alzheimer Disease. JAMA Neurol. 2019;76(3):264-273. doi:10.1001/jamaneurol.2018.4249
  5. Babulal GM, Quiroz YT, Albensi BC, et al. Perspectives on Ethnic and Racial Disparities in Alzheimer’s Disease and Related Dementias: Update and Areas of Immediate Need. Alzheimers Dement J Alzheimers Assoc. 2019;15(2):292-312. doi:10.1016/j.jalz.2018.09.009
  6. O’Caoimh, Rónán et al. “Screening for Mild Cognitive Impairment: Comparison of “MCI Specific” Screening Instruments.” Journal of Alzheimer’s disease : JAD vol. 51,2 (2016): 619-29. doi:10.3233/JAD-150881.
  7. Ismail, Zahinoor et al. “Brief cognitive screening instruments: an update.” International journal of geriatric psychiatry vol. 25,2 (2010): 111-20. doi:10.1002/gps.2306.
  8. Bonnie Slavych, “Pros and Cons of Various Screening Tools for Dementia. Ashawire.” May 2019.
  9. Graff-Radford NR, Besser LM, Crook JE, Kukull WA, Dickson DW. Neuropathological differences by race from the National Alzheimer’s Coordinating Center. Alzheimers Dement J Alzheimers Assoc. 2016;12(6):669-677. doi:10.1016/j.jalz.2016.03.004
  10. Shafiq-Antonacci, Ruxsana et al. “Spectrum of saccade system function in Alzheimer disease.” Archives of neurology vol. 60,9 (2003): 1272-8. doi:10.1001/archneur.60.9.1272
  11. Yang, Qing et al. “Specific saccade deficits in patients with Alzheimer’s disease at mild to moderate stage and in patients with amnestic mild cognitive impairment.” Age (Dordrecht, Netherlands) vol. 35,4 (2013): 1287-98. doi:10.1007/s11357- 012-9420-z.
  12. Panchal, Harsh et al. International Journal of Advances in Science Engineering and Technology, ISSN(p): 2321 –8991, ISSN(e): 2321 –9009 Volume-8, Issue-3, Jul.-2020, 

What do you think?