Alzheimer's disease (AD) is a serious neurodegenerative condition and the major cause of dementia. The current diagnostic methods are time-consuming, expensive, and intrusive and do not represent a standardized approach for the early detection of AD. This article focuses on how sensor technology can aid in the early detection of Alzheimer's disease.
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Alzheimer’s Disease and its Epidemiology
Alzheimer’s disease is defined by the build-up of fibrillar amyloid β (Aβ) peptides and hyperphosphorylated tau protein aggregates in extracellular plaques and intracellular neurofibrillary tangles respectively. Typically, these pathological alterations take place years before the patient experiences any clinical symptoms.
Mild cognitive impairment (MCI) is the initial stage of AD. It is characterized by obscured memory episodes and impairments that are not memory related.
It progresses to dementia symptoms, including weakening cognitive processes, memory loss, difficulty performing daily tasks, and space and time disorientation.
The World health organization (WHO) and Alzheimer's disease international statistics suggest this incurable disease has already affected approximately 50 million people. By the year 2050, the figure is expected to rise to 152 million. Although it primarily affects elderly individuals, in 4% of cases, it also manifests before the age of 65, making it a multifaceted disease.
Current Detection Tools and its Limitations
Preclinical AD diagnosis is essential since treatment cannot block or reverse the disease's course once clinical symptoms appear. Recent findings indicate that early diagnosis can minimize the likelihood of developing AD by one-third.
The conventional detection techniques include imaging methods and cognitive tests. Mainly positron emission tomography (PET), near-infrared (NIR), and magnetic resonance imaging (MRI) are employed to find anomalies in patient’s brain.
Enzyme-linked immunosorbent assays (ELISA) and immunohistochemistry have also been adopted to measure cerebrospinal fluid and blood plasma biomarkers.
However, their practical applications are constrained by the radiation exposure of PET imaging, high cost, as well as the challenging and time-consuming nature of poor sensitivity immunosorbent tests. Hence, the fabrication of rapid and simple detection technologies is the need of the hour.
Sensor Technology for the Detection of Alzheimer’s Disease
Sensor technologies overcome these limitations and have led to simple, affordable, sensitive sensing-based methods for AD early detection. Sensors require less amount of the sample, which would help to minimize the extraction procedures conducted on patients.
Sensors can quickly recognize and detect biomarkers that indicate the onset of AD. A unique label-free biosensor designed with MoS2 diodes has been recently built by the Simon Fraser University (SFU) Nanodevice Fabrication Group. It can be employed to detect diseases like Alzheimer's.
An aberrant fluctuation in cytokine levels is a sign of uncontrolled inflammatory responses that have been associated with Alzheimer's disease. Tumor necrosis factor (TNF), a pro-inflammatory cytokine, can be detected by this MoS2 diode-based sensor, according to the results that were published in the journal Nature Communications.
TNF-binding aptamers have been added to the sensor device to enable the detection of TNF- at levels ~ 10 fM, which is far lower than the levels detected in healthy blood.
The fundamental biomarkers of Alzheimer's disease, including total tau protein (t-tau), beta-amyloid42 (Aβ42), beta-amyloid40 (Aβ40), and phosphorylated tau protein (p-tau181), have been measured by another study team at the ‘Korea Advanced Institute of Science and Technology’ (KAIST).
The clinically tested and accurate multi-plexed electrical biosensor is built on carbon nanotubes (CNT) with a low manufacturing cost that was produced using the Langmuir-Blodgett process.
The biosensor was able to concurrently detect the levels of Alzheimer's disease biomarkers with an overall 90% sensitivity and selectivity and 88.6% accuracy rate.
Alzheimer’s disease has a 15 to 20-year asymptomatic phase before the initial clinical symptoms manifest. Researchers have found that Alzheimer's disease can be detected in the bloodstream up to seventeen years before any observable symptoms appear.
Misfolding, which causes characteristic plaque-like deposits in the brain, marks the disease's progression. In a recent article published in the journal ‘Alzheimer's & Dementia: The Journal of the Alzheimer's Association’ these researchers described the data curated from their use of the immuno-infrared sensor.
The sensor detects the amount of the glial fiber protein (a hallmark of astrogliosis) and the protein biomarker amyloid-beta, which is responsible for the misfolding.
Fluorescence sensors have recently achieved wide use as a potent tool for screening biomarkers in-vitro / in-vivo. This is because of their high signal alterations when exposed to certain analytes.
Yi and colleagues developed a water-soluble, quinoline-malononitrile-based near-infrared (NIR) sensor with intramolecular charge transfer that showed fluorescence "turn-on" after specifically attaching to Aβ aggregates.
Wearable biosensors are also utilized to detect lower limb mobility in AD patients for early disease diagnosis.
A wearable internet-of-things device was developed by Varatharajan et al. to detect Alzheimer's disease. Real-time tracking of the leg movements of AD patients is done using a force sensor-based device. Despite simply measuring foot mobility in this study, it has been shown to be a reliable method for detecting Alzheimer's disease.
The wearable biosensor technology has various benefits, including portability, comfort, and simplicity, enabling ongoing point-of-care (POC) testing.
Still, these developments come with a few disadvantages, such as the fact that some of these devices are uncomfortable to wear, disorientating, and stressful. Hence, non-wearable biosensors (as previously described) are introduced with minimal disruption to individuals.
Early diagnosis of AD is essential to postpone the onset of degenerative symptoms to the maximum extent possible and to give family members and caretakers enough time to adjust to the related changes.
Thus, the development of a novel diagnostic tool for the identification of early AD biomarkers is necessary. Although blood serum and CSF are complex samples that may interfere with the measured values, achieving adequate sensitivity and selectivity is the key challenge to be solved in this situation.
Sensor technologies can quickly identify Alzheimer's disease and offer good sensitivity and selectivity.
The transferability of the developed biosensors is hampered by the fact that long-term stability and efficiency are often neglected in many investigations, despite the fact that sensitivity and selectivity problems appearing to be resolved once fM and pM limits of detection are achieved.
Consequently, clinical trials can be conducted in the future to validate the long-term efficacy of sensors with the goal of making them commercially available.
References and Further Reading
Beyer, L., Stocker, H., Rujescu, D., Holleczek, B., Stockmann, J., Nabers, A., Brenner, H., & Gerwert, K. (2022). Amyloid-beta misfolding and GFAP predict risk of clinical Alzheimer’s disease diagnosis within 17 years. Alzheimer’s & Dementia. doi.org/10.1002/ALZ.12745
De Silva, T., Fawzy, M., Hasani, A., Ghanbari, H., Abnavi, A., Askar, A., Ling, Y., Mohammadzadeh, M. R., Kabir, F., Ahmadi, R., Rosin, M., Kavanagh, K. L., & Adachi, M. M. (2022). Ultrasensitive rapid cytokine sensors based on asymmetric geometry two-dimensional MoS2 diodes. Nature Communications, 13(1), pp. 1–10. https://doi.org/10.1038/s41467-022-35278-2
Early Alzheimer’s disease detection sensor in development. (n.d.). Availablt at: https://www.openaccessgovernment.org/early-alzheimers-disease-detection-sensor-brain/151644/.
Li, F., Stewart, C., Yang, S., Shi, F., Cui, W., Zhang, S., Wang, H., Huang, H., Chen, M., & Han, J. (2017). Optical Sensor Array for the Early Diagnosis of Alzheimer’s Disease. doi.org/10.3389/fchem.2022.874864
Toyos-Rodríguez, C., García-Alonso, F. J., & de la Escosura-Muñiz, A. (2020). Electrochemical Biosensors Based on Nanomaterials for Early Detection of Alzheimer’s Disease. Sensors, 20(17), p. 4748. https://doi.org/10.3390/S20174748
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