Dementia affects millions of people worldwide, causing a serious impact on the economy and the healthcare system. Alzheimer鈥檚 disease (AD) accounts for 60%-70% of dementia cases. Early and accurate diagnosis remains complex, often occurring years after the physiological processes have begun.
Advances in diagnostic technology have allowed the detection of neuropathological hallmarks of AD, such as neurofibrillary tangles, tau and amyloid 尾 plaques, through positron emission tomography (PET) imaging or cerebrospinal fluid (CSF) sampling.
Newer research has indicated that, similarly, reductions in A尾42 and increases in tau can be detected in blood plasma. Blood-based biomarkers (BBMs) are increasingly recognized for their potential in early diagnosing and monitoring of AD. In contrast with traditional invasive and expensive methods for AD diagnosis, which rely heavily on neuroimaging and CSF analysis, blood tests offer a less costly and non-invasive alternative, making them more accessible for broad clinical application.
In the last decade, we have witnessed the development of disease modifying therapies for AD which target the accumulation of amyloid 尾. These new drug therapies have faced challenges related to the restrictive and high costs of fMRI and PET scans necessary for diagnosis and monitoring. The implementation of blood-biomarkers could simplify the process for approval of anti-amyloid 尾 immunotherapy, by facilitating the identification of participants eligible for treatment.
Beyond amyloid and tau proteins, research has focused on different BBMs of AD pathology including neuroinflammation markers (e.g., cytokines), and genetic markers (apolipoprotein E [APOE]). Cytokines like the interleukin 6 (IL6), interleukin 1尾 (IL-1尾), and tumour necrosis factor TNF-伪, not only can increase amyloid precursor protein, they can also compromise the blood-brain barrier integrity allowing peripheral immune cells to enter the central neural system and provoke neuroinflammation. These BBMs are considered to be precursors of the development of neurofibrillary tangles and amyloid plaques. Importantly, these inflammatory markers can be detected decades before dementia symptoms, which could facilitate earlier diagnosis of AD.
While not strictly blood-based, genetic testing for risk factors such as the APOE 蔚4 allele can be done using blood samples. The APOE 蔚4 gene significantly increases Alzheimer’s risk. Specifically, the 蔚4 isoform results in less effective clearing of amyloid and increased tau phosphorylation. This genetic marker represents one of the strongest known risk factors for late-onset AD.
Challenges of BBMs usage in the context of AD clinical research and practice
Although BBMs bring a range of advantages related to the reduction in costs and increased availability to patients, they also present some challenges. For instance, factors such as diet, age, genetics, and lifestyle can influence blood biomarkers levels, causing a high rate of individual variability and making it more difficult to identify markers that can be applicable universally.
Other challenges are related to the availability of BBMs analysis. As an example, mass spectrometry is a technique that allows measurements of plasma A尾42 concentrations. On a global level, access to mass spectrometry is low given the limited availability of infrastructure and expertise.
Finally, from a regulatory perspective, reproducibility and consistency of results are fundamental for considering blood-biomarkers. In parallel with a standardization of the protocols and approval of additional markers and tests, we can expect a more efficient process from biomarker discovery to clinical use.
The role of digital cognitive assessments in AD
Cognitive impairment is the most evident symptom of AD, and changes in cognition are often the first signs of the disease observed by patients and their families. While blood biomarkers provide insight into the pathophysiological underpinnings of disease, they don’t directly measure the cognitive impact of neurodegeneration which is what matters most to patients and their loved ones. Hence, digital cognitive assessments can be valuable complements to BBMs.
While paper and pencil assessments have been widely used, digital cognitive tools provide several key advantages in AD diagnosis and monitoring. For instance, they allow frequent monitoring, capturing subtle fluctuations and day-to-day variability in cognitive abilities, constituting a significant advance compared to the traditional paper and pencil-based assessments, which are typically administered infrequently and in clinic.
Compared to traditional neuropsychological tests, digital tools may be more sensitive to early cognitive changes, potentially identifying cognitive decline before it impacts daily functioning. Finally, digital assessments can be performed in real-world environments, on patients’ own devices, providing more accurate representations of functional impairment than what is assessed in controlled clinical settings.
Integrative approach: combining biomarkers and cognitive assessments
In order to overcome some of the challenges inherent to the implementation of BBMs outlined above, a combined approach including multiple biomarkers and other assessments which measure AD symptoms, including cognitive impairment, could be the solution.
As mentioned, at present, the primary function of BBMs is to detect AD pathology in individuals experiencing cognitive impairment. Digital cognitive assessments could bring several advantages to research, clinical trials and healthcare settings. These tools could be used as an initial screening step, facilitating the recruitment process and reducing site burden.
Combining cognitive assessments with BBMs can enhance risk stratification. For instance, BBMs can identify individuals at high risk of developing Alzheimer鈥檚, even when they don鈥檛 yet show cognitive decline, and when combined with cognitive assessments the accuracy in categorizing subjects into different risk groups can increase. With the advent of new disease-modifying treatments for AD, there is an imperative need for sensitive cognitive tests to facilitate the stratification of individuals who might benefit from early interventions, and to support more tailored interventions.
In the context of treatment, as previously mentioned, digital cognitive tests can help monitor changes in cognitive function over time, offering the possibility to capture these changes early. This is particularly useful in clinical trials assessing the efficacy of new treatments or interventions, allowing researchers to correlate cognitive changes with BBMs levels. Understanding the cognitive and functional changes in response to treatment measures what matters to patients, building a stronger case for regulatory approval.
The combination of blood biomarkers and digital cognitive assessments constitutes a fundamental shift in the approach to research in neurodegenerative disorders. This integrated methodology paves the way for faster detection, enhanced precise monitoring, and potentially, more effective therapeutic intervention.
Cambridge Cognition
is providing digital cognitive assessments designed to enhance early detection of cognitive impairments in dementia and to track changes in different cognitive domains such as memory, attention, language, and executive function. By providing in-clinic and remote cognitive tests, they can capture real-time, objective measures that convey meaningful insights into patients鈥� lives.
As the use of digital health tools and the focus on a more patient-centred approach is emerging, Cambridge Cognition continues its work with partners such as the DiMe ADRD project () which aims to transform AD and other related dementia research and care by providing diverse digital health metrics that support patients and carers.
Cambridge Cognition is also a part of the Real-World Implementation, Deployment, and Validation of Early Detection Tools and Lifestyle Enhancement () project, supported by the EU Innovative Health Initiative (IHI) and UK Research and Innovation (UKRI), which aims to develop and assess a toolbox platform that can help overcome current obstacles to the detection and treatment of AD, thereby accelerating innovation in AD research and therapies. This will not only facilitate patients’ access to healthcare providers, but also support a more personalized therapy, including lifestyle interventions and pharmacological treatments.
Future direction and innovation
A critical advancement in this field is the development and validation of BBMs for primary care settings, making these diagnostic tools both easily accessible and time-/cost-effective in environments where most patients first seek care. This could substantially shorten the gap between disease onset and diagnosis.
Currently, artificial intelligence (AI) is beginning to contribute to the diagnostic landscape. Through advanced machine learning algorithms, AI can facilitate the integration of multimodal data from both biological and digital sources, identifying complex patterns that might go unnoticed in classical analysis. These AI systems show promise in predicting individual disease trajectories and personalizing treatment plans based on combined biomarker and cognitive profiles.
This personalized medicine approach extends to genetic risk factors as well. For instance, lifestyle interventions may be particularly beneficial for APOE 蔚4 carriers, who face significantly higher risk of developing AD. The ability to tailor prevention strategies based on genetic and biomarker profiles represents a crucial advancement in preventative neurology.
As research continues, the integration of cognitive assessments with BBMs might lead to more personalized approaches to diagnosis and treatment, improving outcomes for individuals with AD and other neurodegenerative disorders. Blood-based biomarkers have some limitations in their accuracy, and digital cognitive measures could increase the precision in identifying AD pathology and progression, especially when these tests specifically evaluate cognitive domains that show early decline during the disease course.
Overall, BBMs hold promise for the early diagnosis and monitoring of neurodegenerative conditions, potentially transforming clinical practice in this area, where intervention commences before significant neurodegeneration has occurred.
References
Ding, Z., Lee, T.-l., & Chan, A. S. (2022). Digital Cognitive Biomarker for Mild Cognitive Impairments and Dementia: A Systematic Review. Journal of Clinical Medicine, 11(14), 4191. https://doi.org/10.3390/jcm11144191
Hansson O, Blennow K, Zetterberg H, Dage J. Blood biomarkers for Alzheimer’s disease in clinical practice and trials. Nat Aging. 2023 May;3(5):506-519. doi: 10.1038/s43587-023-00403-3. Epub 2023 May 18. PMID: 37202517; PMCID: PMC10979350.
Mielke, M. M., Anderson, M., Ashford, J. W., Jeromin, A., Lin, P. J., Rosen, A., … & Udeh鈥怣omoh, C. T. (2024). Recommendations for clinical implementation of blood鈥恇ased biomarkers for Alzheimer’s disease. Alzheimer’s & Dementia, 20(11), 8216-8224.
Schindler, S.E., Galasko, D., Pereira, A.C. et al. Acceptable performance of blood biomarker tests of amyloid pathology 鈥� recommendations from the Global CEO Initiative on Alzheimer鈥檚 Disease. Nat Rev Neurol 20, 426鈥�439 (2024).
Sch枚ll, M., Verberk, I. M., Del Campo, M., Delaby, C., Therriault, J., Chong, J. R., … & Alcolea, D. (2024). Challenges in the practical implementation of blood biomarkers for Alzheimer鈥檚 disease. The Lancet Healthy Longevity, 5(10).
Tsoy E, La Joie R, VandeVrede L, Rojas JC, Yballa C, Chan B, Lago AL, Rodriguez AM, Goode CA, Erlhoff SJ, Tee BL, Windon C, Lanata S, Kramer JH, Miller BL, Dilworth-Anderson P, Boxer AL, Rabinovici GD, Possin KL. Scalable plasma and digital cognitive markers for diagnosis and prognosis of Alzheimer’s disease and related dementias. Alzheimers Dement. 2024 Mar;20(3):2089-2101. doi: 10.1002/alz.13686. Epub 2024 Jan 15. PMID: 38224278; PMCID: PMC10942726.
