A Literature Review on Early Detection and Management of Alzheimer’s Disease

Early Detection and Management of Alzheimer’s Disease: A Literature Review

Alzheimer’s Disease (AD) is one of the leading causes of morbidity in contemporary times. In America alone, more than 5 million people live with AD and other related dementias (ADRD), and the prevalence is expected to double by 2050 (Olivari, French, & McGuire, 2020). Due to the prevalence of the disease, many scholars have researched the causes and possible management of AD, and the consensus is that early detection can reduce the pathogenesis associated with AD. This literature review section explores the findings of various scholars on AD progression and early detection, technologies for the detection of AD, and the future perspectives on the management of AD.

Disease Progression and Early Detection

Based on the population estimates collected in 2015, dementia is one of the most feared diseases, especially among the aging population, even more than cancer and stroke. Among the aging population, AD and other cognitive disorders are a significant health concern (Kelley, Ulin, & McGuire, 2018). At the same time, AD is described as the most common cause of dementia. It is linked to more than 60-80% of the cases of dementia, and 5.5 million Americans suffered from AD as of 2018 (Kelley et al., 2018). These numbers indicate the need for the identification and addressing AD during its early prognosis. Furthermore, no effective treatment has been found for AD; all the available treatments, including pharmacological treatments, are suitable for sustainable management of the disease over the long-term (Kelley et al., 2018). When detected early, however, the treatment can start early, delaying the disease prognosis.

The objective of early detection of AD is often to improve the health-related quality of life. Olivari et al. (2020) report that the critical components of the early detection processes increase the availability of use of timely data towards reducing AD risk, fostering early diagnosis, and promoting a reduction in cognitive decline (Li, Rui, Chen, Li, Schulz, & Zhang, 2018). According to Li et al. (2018), the pathology of AD is suspected to begin long before the patients begin exhibiting symptoms. As such, diagnosing the disease in the early stages makes a significant clinical impact. The early detection of AD is also discussed by Folch et al. (2016), who emphasize the multifactorial nature of the condition and the need for complex treatment approaches. With early detection, it becomes possible to use a combination of therapies and make lifestyle choices that can contribute to its successful mitigation (Folch et al., 2016). The recognition of the impacts of early detection on patients with AD has influenced studies on the approaches that can be used in the detection and treatment of AD during the onset.

Some of the barriers to early detection of AD are limited intentional access to testing by individuals at risk and lack of awareness about potential risk factors. Olivari et al. (2020) discussed the impacts of education on changing the attitudes of the public towards mental health diseases and the intention of at-risk individuals to pursue diagnostic procedures for AD randomly. This is particularly important when population-appropriate approaches are used in public education. The need for education for early detection of AD is also discussed by Folch et al. (2016), who focus on the use of nutrition as an abatement measure for AD. The argument presented is that various nutritional practices are important towards eliminating the risk factors for those who are not yet affected by the disease and for abatement of symptoms following early detection of AD. The objective of any planned educational intervention in this context should be to increase awareness of AD and thus enhance the willingness of at-risk individuals to be tested and guide those who are already tested on the lifestyle choices to make following early detection of AD or prevent the onset of AD. Education, therefore, compounds the effectiveness of measures taken following the early detection of AD.

Technologies for Early Detection of Alzheimer’s Disease

Early detection of AD has been difficult to attain consistently due to the limitations of most commonly used detection measures. The most common approaches to the detection and diagnosis of AD rely mainly on the medical history and neuropsychological examinations of patients as well as the clinical rating scores of patients measured through various scales, such as the clinical dementia rating (CDR) (Li et al., 2018). These approaches are not sufficiently accurate or effective for early detection, which mostly takes place when AD is still asymptomatic. The alternative approach is to use biomarker identification to improve prognostic outcomes through earlier detection relative to that realized through the more traditional approaches. The currently used biomarker identification approaches are based on the detection of Aβ (1-42) and tau level in the brain as the indicators of AD; however, these indicators only increase to detection limits as AD takes hold, hence cannot be considered effective indicators for early detection (Folch et al., 2016). Newer technologies are, therefore, being developed to facilitate earlier detection.

AD is characterized by functional impairment for which biomarkers include the deposition of proteins and neuronal loss. Neuroimaging techniques, such as fMRI and PET, can be used to detect these biomarkers, as reported by Frisoni et al. (2017). CSF analysis is also increasingly being used for the diagnosis of AD in research and clinical practice (Frisoni et al., 2017). Despite their growing popularity, there is still incomplete validation of the clinical effectiveness of all three approaches, and their reimbursement by health insurance providers is still hampered (Frisoni et al., 2017). The limitations of the methods have motivated the development of newer technologies for early AD detection, such as fNIRS.

Functional near-infrared spectroscopy (fNIRS) has been reported as a developing technology for the early detection of AD. Li et al. (2018) report that the technology has been getting into increasing utilization over the past few years and is relatively effective in detecting biomarkers for AD at low concentrations. A high correlation has been observed between fNIRS and fMRI results, although fNIRS comes with multiple benefits compared to both fMRI and PET, which have also been in use for early detection of AD. According to Li et al. (2018), fNIRS is linked with a high temporal resolution, low cost, and high portability of results, among other benefits. These benefits and the potential for early AD detection make the process an important consideration for AD diagnosis.

Future Perspectives on Alzheimer’s Disease

The challenges in the early detection and treatment of AD have resulted in a growing interest in various subjects for future trials on AD prevention. Wang, Tan, and Yu (2016), for instance, report the implications of on-going advances in pathogenesis awareness and knowledge, which can be applied in the identification of novel diagnostic targets. Such targets as more responsive biomarkers can help in improving intervention outcomes and the effectiveness of preventive measures. Another study by Folch et al. (2016) has shown a growing interest in the impacts of growth factors (GF) on the improvement of AD pathology. There has specifically been interest in the transformation of the growth factor β and other growth factors to participate in neurogenesis and neurodevelopment as a strategy towards AD treatment (Folch et al., 2016). Such novel approaches promise positive outcomes not only for early detection of AD but also for the application of the diagnoses in developing treatment plans for patients.

One of the areas in which there is currently intense focus concerning advances in AD studies and diagnostic procedures is on the selection of participants and the design of clinical trials. The selection of participants and appropriate research design plays an essential role in determining trial outcomes (Wang et al., 2016). With the current emphasis on the development of preventive measures for AD, working with individuals who are at risk of AD seems to be the most justifiable approach to clinical trials. In alignment with this, Folch et al. (2016) suggest that various approaches can be used to combine clinical trials with nutritional trials involving a variety of diets and diet restrictions to determine the impacts of nutritional choices on the risk of disease development. Such trials are bound to give results that would be useful in future clinical practice relating to AD prevention and mitigation.

Various studies have shown that the combination of nutritional regimens and pharmacotherapy can result in better outcomes for AD patients. Folch et al. (2016), for instance, recommend careful development of nutritional regimens and pharmacotherapies that target the many pathways associated with AD. Li et al. (2018) recommend increasing the number of participants for clinical trials for diagnostic processes to improve diagnostic processes for early detection. The diverse recommendations for future studies and clinical practice in AD diagnosis and management confirm the multifactorial nature of AD and the need for more robust and collaborative approaches to education, technology development, and clinical practice relating to AD.

 

References

Folch, J., Petrov, D., Ettcheto, M., Abad, S., Sanchez-Lopez, E., Garcia, M. L., et al. (2016). Current research therapeutic strategies for Alzheimer’s disease treatment. Neural Plasticity. Retrieved from http://downloads.hindawi.com/journals/np/2016/8501693.pdf

Frisoni, G. B., Boccardi, M., Barkhof, F., Blennow, K., Cappa, S., Chiotis, K., et al. (2017). Strategic roadmap for an early diagnosis of Alzheimer’s disease based on biomarkers. The Lancet, 16(8), P661-P676. Retrieved from https://www.thelancet.com/journals/laneur/article/PIIS1474-4422(17)30159-X/fulltext

Kelley, M., Ulin, B., & McGuire, L. C. (2018). Reducing the risk of Alzheimer’s disease and maintaining brain health in an aging society. Public Health Reports, 133(3), 225-229. Retrieved from https://journals.sagepub.com/doi/10.1177/0033354918763599

Li, R., Rui, G., Chen, W., Li, S., Schulz, P. E., & Zhang, Y. (2018). Early Detection of Alzheimer’s disease Using Non-invasive Near-Infrared Spectroscopy. Frontiers in Aging Neuroscience, 10, 366. Retrieved from https://www.frontiersin.org/articles/10.3389/fnagi.2018.00366/full

Olivari, B. S., French, M. E., McGuire, L. C. (2020). The public health roadmap to respond to the growing dementia crisis. Innovation in Aging, 4(1), 1-11.

Wang, J., Tan, L., & Yu, J-T. (2016). Prevention trials in Alzheimer ’s disease: Current status and future perspectives. Journal of Alzheimer’s Disease, 50(4), 927-945. Retrieved from https://pubmed.ncbi.nlm.nih.gov/26836177/