Improving the Diagnosis of Alzheimer Disease Through Predictive Biomarkers
Alzheimer disease (AD), the most prevalent cause of dementia worldwide, is a progressive, irreversible neurodegenerative disease marked by a deterioration in function, cognition, and behavior. According to 2020 data, the number of AD patients in the United States who are at least 65 years of age is likely to increase from 5.8 million to 13.8 million by 2050.1 The estimated lifetime risk for Alzheimer dementia at age 45 was approximately 20% for women and 10% for men (Figure 1).1 In addition, data indicates that mortality resulting from AD increased 146.2% from 2000 to 2018, making AD the fifth-largest cause of death in the American senior population.1
Age is a key risk factor for AD, a multifactorial disease in which genetic, behavioral, environmental, and developmental factors critically influence its pathogenesis. The slow-progressing amnestic disorder reflects the predominant early distribution of amyloid beta (Aβ) plaques and neurofibrillary tangle pathology in the medial temporal lobe structures, which ultimately evolves into an amnestic-predominant, multidomain dementia.2
Recent findings have demonstrated that beyond these traditional markers of AD, chronic oxidative stress, mitochondrial dysfunction, calcium dyshomeostasis, and genetic components also play noteworthy roles in AD pathogenesis.4 Within each of these biological domains, the ongoing discovery of novel predictive biomarkers is anticipated to drive earlier AD diagnosis and faster, more precise treatment.
Diagnosis is mostly made via the identification of symptoms, such as significant memory loss, global cognitive decline, and the impairment of daily life activities. However, AD has a long prodromal period during which early detection appears to be particularly significant in slowing down progression; therefore, earlier identification is paramount.2
A paradigm shift that acknowledges AD as a continuum of neurological deterioration that can be staged through neuropathological insights and in vivo biomarkers is driving earlier diagnoses.4,5 Driven by this advanced understanding, the National Institute on Aging (NIA) and Alzheimer’s Association (AA) updated the diagnostic criteria in acknowledgement of a biological rather than clinical definition of AD.5,6
While AD biomarkers are not considered to be as robust in comparison with biomarkers used in other diseases, research has significantly increased the accuracy with which AD pathology can be detected in the brain. Recommendations from the International Working Group on the clinical diagnosis of AD states that diagnosis should be restricted to people with specific AD phenotypes and positive biomarkers, whereas cognitively unimpaired biomarker-positive individuals should only be considered “at-risk” for progression to AD.7
Diagnostic Biomarkers for Alzheimer Disease
Numerous biomarkers are currently being used alongside neuropsychological and neurological tests to diagnose AD (Figure 2),8 with investigations expanding to include new molecules linked to unique pathophysiologies.
The production of Aβ peptides is increased in patients with AD and are widely accepted as a relatively accurate biomarker for AD diagnosis because of their reduced clearance. The most toxic isoform of the peptide, Aβ42, is quantified in the cerebrospinal fluid (CSF) and allows for the identification of AD in its preclinical stage. It has a high diagnostic value with clear specificity for AD over other neurodegenerative diseases, but interindividual differences make the quantification of absolute Aβ42 challenging.9
The ratio of Aβ42/Aβ40 in the plasma accounting for intraperson variability has emerged as a more accurate approach for the prediction of AD. In a study of 145 participants with amnestic mild cognitive impairment (a-MCI) and 83 individuals who were cognitively normal (CN), researchers found that a significantly lower Aβ42/Aβ40 ratio was present in an individual with a-MCI compared with individuals who were CN. A low Aβ42/Aβ40 ratio at baseline increased the risk of progression to dementia by 70%.10
These results and others have led to the US Food and Drug Administration (FDA) approval of the CSF Lumipulse G β-Amyloid Ratio (1-42/1-40) test.11 This is the first in vitro diagnostic test for the early detection of AD in adults at least 55 years of age with cognitive impairment. The test promises same-day results and is purported to provide the same information as a PET scan without the radiation risk.
The predictive value of plasma tau phosphorylated at threonine-217 (p-tau217) for differentiating AD from other neurodegenerative diseases is also being investigated. In the Swedish BioFINDER study (ClinicalTrials.gov Identifier: NCT01208675), p-tau217 was measured continuously for up to 6 years in 150 participants who were cognitively unimpaired and 100 patients with MCI.11 Patients with both preclinical (Aβ-positive cognitively unimpaired) and prodromal (Aβ-positive MCI) AD presented with significantly increased p-tau217 levels in comparison with Aβ-negative participants who were cognitively unimpaired. Furthermore, longitudinal increases in p-tau217 correlated with the longitudinal worsening of brain atrophy and cognition, which suggests that plasma p-tau217 can be used to monitor AD progression over time.12
A drive toward identifying new biomarkers that represent Aβ- and tau-independent AD pathology is ongoing and shows promise in not only broadly characterizing AD-associated pathological changes, but also facilitating precise selections of susceptible patients.
Novel Alzheimer Disease Biomarkers in Plasma
Synaptic dysfunction-related biomarkers have received attention as possible predictors of AD development. Synaptic degeneration is an important hallmark of early AD pathology that closely correlates with cognitive decline.
Synaptosomal-associated-protein-25 (SNAP-25) is a newly-discovered potential biomarker related to presynaptic damage, which was assessed in a study that isolated neuron-derived exosomes (NDEs) from the serum of patients with AD and healthy participants. The levels of SNAP-25 carried by NDEs were reduced in patients with AD (mean, 459.05 ng/mL) compared with healthy participants (mean, 686.42 ng/mL). Researchers found that the level of SNAP-25 carried by NDEs had the power to discriminate between the 2 cohorts.13 Research has confirmed that CSF SNAP-25 is increased in relation to amyloid pathology in the AD continuum, strengthening the potential use of SNAP-25 as a presynaptic injury-related biomarker.14
The lipid metabolism-related biomarker, apolipoprotein E (apoE), has shown strength as a biomarker for AD pathogenesis since the apoE ɛ4 allele is the strongest genetic risk factor for AD. Measurements of apoE protein levels in CSF have been performed in relation to the diagnosis of AD, but findings have been contradictory, necessitating further research.15
The Utility of New Biomarkers and Disease-Tracking in Alzheimer Disease
The staging of AD has mainly relied on clinical data, but the A/T/N system by the NIA-AA Research Framework has provided more precise AD staging based on AD-related pathological processes. Specifically, the deposition of Aβ (A), abnormal tau (T) proteins, and neurodegeneration (N).16 This new classification system groups all key AD biomarkers by the pathologic process that each one represents, which is rated as either positive or negative.
These recommendations create a common language with which researchers can characterize the pathological changes seen in people diagnosed with AD and facilitate participant selection for interventional clinical trials. For clinical application, however, the A/T/N system still requires sufficient evidence supporting the utility of CSF and neuroimaging biomarkers for successful implementation.
Frontotemporal dementia (FTD) and AD are often mistaken for each other due to the similarities in their clinical presentation, brain atrophy, cognitive domain impairment, and progressive alterations in personality, language ability, and behavior. The diagnostic accuracy remains unsatisfactory, especially when diagnosis is made with the National Institute of Neurological and Communicative Diseases and Stroke/Alzheimer Disease and Related Disorders Association (NINCDS-ADRDA) criteria. The behavioral, psychological, and medical imaging manifestations of patients with FTD and AD show significant overlap, which causes the differential diagnosis of FTD and AD to be challenging.17
Machine learning-based models to address this challenge have gained traction. In a study comprising participants with AD (n=170), FTD (n=72), and healthy participants (n=87), machine learning algorithms were able to differentially diagnose AD and FTD with an accuracy of more than 84%.17 Therefore, clinical data consisting of both structured data tables and clinical notes can be effectively used in machine learning-based approaches to model risk for AD dementia progression and diagnosis.
A study suggests that by using machine learning, a single MRI scan of the brain could be sufficient to diagnose AD. The researchers’ algorithms could accurately predict whether a patient had AD in 98% of cases and distinguish between early- and late-stage AD in 79% of patients.18
Can Alzheimer Disease Be Predicted Up to 15 Years Before Onset?
Certain health conditions are significantly associated with the development of AD up to 15 years before the onset of the disease, which was a ground-breaking discovery. This was revealed by an agnostic study using health records data from 20,214 patients with AD in the United Kingdom and 19,458 patients with AD in France. Appearing at least 9 years before the first clinical diagnosis, depression was the first comorbid condition associated with AD, followed by anxiety, constipation, and abnormal weight loss.19 The implications of this are that risk factors and early signs of AD observable at the general practitioner level could guide the implementation of new primary and secondary prevention policies.
The main objective in the detection of preclinical AD is to facilitate early therapeutic intervention. With advancing biomarker-based classification systems, the use of advanced technology and data analytics, and a greater understanding of predictive factors, research is heading in the right direction.
1. Alzheimer’s Association. 2020 Alzheimer’s disease facts and figures. Alzheimers Dement. 2020;16:391-460. doi:10.1002/alz.12068
2. Porsteinsson AP, Isaacson RS, Knox S, Sabbagh MN, Rubino I. Diagnosis of early Alzheimer’s disease: clinical practice in 2021. J Prev Alzheimers Dis. 2021;8(3): 371-386. doi:10.14283/jpad.2021.23
3. Wang X, Wang W, Li L, Perry G, Lee H-G, Zhu X. Oxidative stress and mitochondrial dysfunction in Alzheimer’s disease. Biochim Biophys Acta. 2014;1842(8):1240-1247. doi:10.1016/j.bbadis.2013.10.015
4. Alegret M, Muñoz N, Roberto N, et al. A computerized version of the Short Form of the Face-Name Associative Memory Exam (FACEmemory®) for the early detection of Alzheimer’s disease. Alzheimers Res Ther. 2020;12(1):25. doi:10.1186/s13195-020-00594-6
5. Khoury R, Ghossoub E. Diagnostic biomarkers of Alzheimer’s disease: a state-of-the-art review. Biomarkers in Neuropsychiatry. 2019;1(3):100005. doi:10.1016/j.bionps.2019.100005
6. Jack CR Jr, Bennett DA, Blennow K, et al. NIA-AA research framework: toward a biological definition of Alzheimer’s disease. Alzheimers Dement. 2018;14(4):535-562. doi:10.1016/j.jalz.2018.02.018
7. Dubois B, Villain N, Frisoni GB, et al. Clinical diagnosis of Alzheimer’s disease: recommendations of the International Working Group. Lancet Neurol. 2021;20(6):484-496. doi:10.1016/S1474-4422(21)00066-1
8. van Oostveen WM, de Lange ECM. Imaging techniques in Alzheimer’s disease: a review of applications in early diagnosis and longitudinal monitoring. Int J Mol Sci. 2021;22(4):2110. doi:10.3390/ijms22042110
9. Alawode DOT, Heslegrave AJ, Ashton NJ, et al. Transitioning from cerebrospinal fluid to blood tests to facilitate diagnosis and disease monitoring in Alzheimer’s disease. J Intern Med. 2021;290(3):583-601. Published online June 26, 2021. doi:10.1111/joim.13332
10. Pérez-Grijalba V, Romero J, Pesini P, et al. Plasma Aβ42/40 ratio detects early stages of Alzheimer’s disease and correlates with CSF and neuroimaging biomarkers in the AB255 study. J Prev Alzheimers Dis. 2019;6(1):34-41. doi:10.14283/jpad.2018.41
11. U.S. Food and Drug Administration. FDA permits marketing for new test to improve diagnosis of Alzheimer’s disease. Published May 4, 2022. Accessed August 18, 2022. https://www.fda.gov/news-events/press-announcements/fda-permits-marketing-new-test-improve-diagnosis-alzheimers-disease
12. Mattsson-Carlgren N, Janelidze S, Palmqvist S, et al. Longitudinal plasma p-tau217 is increased in early stages of Alzheimer’s disease. Brain. 2020;143(11):3234-3241. doi:10.1093/brain/awaa286
13. Agliardi C, Guerini FR, Zanzottera M, Bianchi A, Nemni R, Clerici M. SNAP-25 in serum is carried by exosomes of neuronal origin and is a potential biomarker of Alzheimer’s disease. Mol Neurobiol. 2019;56(8):5792-5798. doi:10.1007/s12035-019-1501-x
14. Nilsson J, Ashton NJ, Benedet AL, et al. Quantification of SNAP-25 with mass spectrometry and Simoa: a method comparison in Alzheimer’s disease. Alzheimers Res Ther. 2022;14(1):78. doi:10.1186/s13195-022-01021-8
15. Therriault J, Pascoal TA, Benedet AL, et al. Frequency of biologically defined Alzheimer disease in relation to age, sex, APOE ε4, and cognitive impairment. Neurology. 2021;96(7):e975-e985. doi:10.1212/WNL.0000000000011416
16. Knopman DS, Haeberlein SB, Carrillo MC, et al. The National Institute on Aging and the Alzheimer’s Association research framework for Alzheimer’s disease: perspectives from the research roundtable. Alzheimers Dement. 2018;14(4):563-575. doi:10.1016/j.jalz.2018.03.002
17. Garcia-Gutierrez F, Delgado-Alvarez A, Delgado-Alonso C, et al. Diagnosis of Alzheimer’s disease and behavioural variant frontotemporal dementia with machine learning-aided neuropsychological assessment using feature engineering and genetic algorithms. Int J Geriatr Psychiatry. 2021;37(2). doi:10.1002/gps.5667
18. Inglese M, Patel N, Linton-Reid, et al. A predictive model using the mesoscopic architecture of the living brain to detect Alzheimer’s disease. Commun Med (Lond). 2022;2:70. doi:10.1038/s43856-022-00133-4
19. Nedelec T, Couvy-Duchesne B, Monnet F, et al. Identifying health conditions associated with Alzheimer’s disease up to 15 years before diagnosis: an agnostic study of French and British health records. Lancet Digit Health. 2022;4(3):e169-e178. doi:10.1016/S2589-7500(21)00275-2
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Reviewed August 2022