The Role of Biomarkers in Diagnosing Alzheimer’s Disease

Cure America of Anosognosia

The Role of Biomarkers in Diagnosing Alzheimer’s Disease

Contributor: Yosefa Allegra Ehrlich, M.Phil., Ph.D. Candidate in Clinical Psychology, Queens College, City University of New York (CUNY)
 
Overview:
The use of biomarkers in diagnosing Alzheimer’s disease (AD) in vivo is gaining popularity. Biomarkers refer to measureable characteristics that indicate the presence of biologic and/or pathologic processes (1). This shift is based on an expanding body of research indicating that in vivo methods validly estimate post-mortem AD pathologic changes that characterize the disease (2–5). The goals of incorporating biomarkers into the diagnosis include: clarifying the multifaceted etiology/pathophysiology (5), standardizing research terms (4,6), identifying individuals in preclinical phases (6,7), improving diagnostic accuracy (8), and developing more precise interventions (9).
 
Over the past decade, the National Institute on Aging and Alzheimer’s Association (NIAA-AA) and the International Working Group (IWG) have continually proposed new criteria for the diagnosis of AD. There are points of overlap as well as divergence between their suggested frameworks. In a position paper still under review (5), the NIAA-AA propose a definition of AD based on evidence of three biological markers of pathologic processes: (a) b-amyloid plaques, (b) phosphorylated tau (P-tau), (c) markers of neuronal injury (e.g., elevated CSF total tau (T-tau)) and cerebral hypometabolism and atrophy (10). Presence of b-amyloid deposition alone (with normal tau and no neurodegeneration) is considered Alzheimer’s pathologic change. A diagnosis of AD indicates evidence of both b-amyloid and P-tau. Neuronal injury is understood to emerge as a consequence of b-amyloid and tau and is not specific to AD pathology (11); accordingly, those markers are conceptualized to emerge in the later stages of the disease continuum and are not essential for a diagnosis. Importantly, these diagnostic states exist independently of clinical symptoms. The authors recognize that cognitive impairment generally corresponds with increased presence of biomarkers and suggest that clinical changes should be measured along six stages of increasing impairment with the first stage beginning with positive evidence of biomarkers. This framework reflects an effort to disentangle the presence of AD neuropathology (disease process) from the clinical syndrome (signs/symptoms).
 
The IWG-2 criteria for AD (9,3,2,12,13) also require in vivo evidence of pathology ((a)decreased CSF b-amyloid and increased CSF P- and T-tau, (b) increased amyloid PET, (c) AD autosomal mutation) for an AD diagnosis. Similar to the NIA-AA, the IWG-2 agrees that biomarkers can be detectable in pre-clinical (asymptomatic) states and calls for a continuum-based understanding of disease course. However, the groups’ definitions diverge regarding clinical phenotypic expression. The IWG-2 criteria call for evidence of cognitive disturbance, primarily episodic memory impairment, in issuing a diagnosis. In the preclinical stage, patients with MCI and positive biomarkers receive a diagnosis of MCI due to AD or, interchangeably, prodromal AD (3,12,13). A diagnosis of typical AD is only distinguished by the degree of cognitive impairment (2). By including the cognitive criterion in the diagnosis, the authors are conceptualizing AD as both biological and syndromic (clinico-pathological).
 
While the definition of an AD diagnosis remains unresolved, the utility and validity of biomarkers measured via imaging and CSF markers have been well demonstrated (14). Fibrillary b-amyloid deposition associated with AD can be validly and reliably measured in vivo via increased amyloid PET binding and low CSF Ab42 (15). Pathologic tau deposition in AD can be assessed through newly developed PET ligands that show elevated cortical tau binding as well as elevated P-tau CSF markers (15,16). Neuronal injury is measured via cortical atrophy (on MRI) and/or hypometabolism (on FDG PET) generally in medial temporal, medial parietal, and lateral temporal-parietal cortices (17).
 
Limitations of incorporating biomarkers in AD diagnoses apply in both research and clinical settings. Despite increased validity, none of the in vivo biomarker tools are as sensitive as histology (18). The lack of consensus among researchers on diagnostic criteria limits standardization and generalizability of findings. There are also no clear numeric cut-offs by which to categorize biomarker levels; while some have proposed continuous measurements (5), this complicates researchstandardization as well as clinical translation. The primary clinical concern surrounds poor specificity and sensitivity of biomarkers to clinical symptoms associated with AD (19). Between 30 to 40% of individuals with no cognitive impairment (asymptomatic) show biological abnormalities in vivo and on autopsy (20,21), while 10 to 30% of individuals with clinical signs of AD-related dementia have clean autopsies (22). This has led to the proposal of including additional factors (e.g., vascular) as biomarkers (23) to increase prognostic reliability. Further clinical challenges include prohibitive costs, limited accessibility, inconsistent regulatory approval, and uncertain insurance reimbursement (12). Finally, as in many areas of research, more population-based studies are needed to validate the utility of biomarkers in diverse ethnic groups (24).
 
Highlighted Abstract: Fluid and imaging biomarkers for Alzheimer’s disease: Where we stand and where to head to.
 
There is increasing evidence that a number of potentially informative biomarkers for Alzheimer disease (AD) can improve the accuracy of diagnosing this form of dementia, especially when used as a panel of diagnostic assays and interpreted in the context of neuroimaging and clinical data. Moreover, by combining the power of CSF biomarkers with neuroimaging techniques to visualize Aβ deposits (or neurodegenerative lesions), it might be possible to better identify individuals at greatest risk for developing MCI and converting to AD. The objective of this article was to review recent progress in selected imaging and chemical biomarkers for prediction, early diagnosis and progression of AD. We present our view point of a scenario that places CSF and imaging markers on the verge of general utility based on accuracy levels that already match (or even surpass) current clinical precision.
 
Henriques, A. D., Benedet, A. L., Camargos, E. F., Rosa-Neto, P., & Nóbrega, O. T. (2018). Fluid and imaging biomarkers for Alzheimer’s disease: Where we stand and where to head to. Experimental Gerontology, (January), 1–9. http://doi.org/10.1016/j.exger.2018.01.002
 
Other Media and Resources:
Webinar- Hear Clifford Jack, MD present his conceptualization of biomarker stages
https://www.alzforum.org/webinars/together-last-top-five-biomarkers-model-stages-ad
 
Webinar- Learn about the development and uses of the AlzBiomarker database
https://www.alzforum.org/webinars/learn-about-ad-biomarker-meta-analysis-alzbiomarker-database
 
Webinar- Biomarkers, cognition, and cognitive reserve in AD
https://www.labroots.com/webinar/biomarkers-cognition-and-cognitive-reserve-in-alzheimers-disease
 
Further Reading:
Bondi, M.W., Edmonds, E.C., Salmon, D.P., 2017. Alzheimer’s Disease: Past, Present, and Future. J. Int. Neuropsychol. Soc. 23, 818–831.
 
Frisoni, G. B., Boccardi, M., Barkhof, F., Blennow, K., Cappa, S., Chiotis, K., … Winblad, B. (n.d.). A Strategic Research Agenda to the Biomarker-Based Diagnosis of Prodromal Alzheimer’s Disease, 1–39. http://discovery.ucl.ac.uk/1567593/1/Frisoni_Strategic_roadmap_early_diagnosis.pdf
 
Frisoni, G. B., Boccardi, M., Barkhof, F., Blennow, K., Cappa, S., Chiotis, K., … Winblad, B. (2017). Strategic roadmap for an early diagnosis of Alzheimer’s disease based on biomarkers. The Lancet Neurology16(8), 661–676. http://doi.org/10.1016/S1474-4422(17)30159-X
 
Jack, C. R. J., Bennet, D. A., Blennow, K., Carrillo, M. C., Dunn, B., Elliot, C., … Sperling, R. (n.d.). NIA-AA Research Framework: Towards a Biological Definition of Alzheimer’s Disease. https://alz.org/aaic/_downloads/draft-nia-aa-7-18-17.pdf
 
Vanderschaeghe, G., Dierickx, K., Vandenberghe, R., 2018. Review of the Ethical Issues of a Biomarker-Based Diagnoses in the Early Stage of Alzheimer’s Disease. J. Bioeth. Inq. 1–12.
 
References:
1. Atkinson AJJ, Colburn WA, DeGruttola VG, DeMets DL, Downing GJ, Hoth DF, et al. Biomarkers and surrogate endpoints: preferred definitions and conceptual framework. Clin Pharmacol Ther. Nature Publishing Group; 2001;69(3):89–95.

2. Dubois B, Feldman HH, Jacova C, Hampel H, Molinuevo JL, Blennow K, et al. Advancing research diagnostic criteria for Alzheimer’s disease: the IWG-2 criteria. Lancet Neurol. Elsevier; 2014;13(6):614–29.

3. Dubois B, Hampel H, Feldman HH, Scheltens P, Aisen P, Andrieu S, et al. Preclinical Alzheimer’s disease: Definition, natural history, and diagnostic criteria. Alzheimer’s and Dementia. 2016. 292-323 p.

4. Jack CR, Albert MS, Knopman DS, McKhann GM, Sperling RA, Carrillo MC, et al. Introduction to the recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimer’s Dement J Alzheimer’s Assoc. Elsevier; 2011;7(3):257–62.

5. Jack CRJ, Bennet DA, Blennow K, Carrillo MC, Dunn B, Elliot C, et al. NIA-AA Research Framework: Towards a Biological Definition of Alzheimer’s Disease.

6. Sperling RA, Aisen PS, Beckett LA, Bennett DA, Craft S, Fagan AM, et al. Toward defining the preclinical stages of Alzheimer’s disease: Recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimer’s Dement J Alzheimer’s Assoc. Elsevier; 2011;7(3):280–92.

7. Jessen F, Amariglio RE, Van Boxtel M, Breteler M, Ceccaldi M, Chételat G, et al. A conceptual framework for research on subjective cognitive decline in preclinical Alzheimer’s disease. Alzheimer’s Dement J Alzheimer’s Assoc. Elsevier; 2014;10(6):844–52.

8. Zwan MD, Bouwman FH, Konijnenberg E, van der Flier WM, Lammertsma AA, Verhey FRJ, et al. Diagnostic impact of [18 F] flutemetamol PET in early-onset dementia. Alzheimers Res Ther. BioMed Central; 2017;9(1):2.

9. Cavedo E, Lista S, Khachaturian Z, Aisen P, Amouyel K, Herholz K, et al. The Road Ahead to Cure Alzheimer’s Disease: Development of Biological Markers and Neuroimaging Methods for Prevention Trials Across all Stages and Target Populations. J Pre Alzherims Dis. 2014;1(3):181–202.

10. Jack CR, Bennet DA, Blennow K, Carrillo MC, Feldman HH, Frisoni GB, et al. A new classification system for AD , independent of cognition A / T / N : An unbiased descriptive classification scheme for Alzheimer disease biomarkers. Neurology. 2016;87:539–47.

11. Wirth M, Madison CM, Rabinovici GD, Oh H, Landau SM, Jagust WJ. Alzheimer’s disease neurodegenerative biomarkers are associated with decreased cognitive function but not β-amyloid in cognitively normal older individuals. J Neurosci. Soc Neuroscience; 2013;33(13):5553–63.

12. Frisoni GB, Boccardi M, Barkhof F, Blennow K, Cappa S, Chiotis K, et al. Strategic roadmap for an early diagnosis of Alzheimer’s disease based on biomarkers. Lancet Neurol. 2017;16(8):661–76.

13. Frisoni GB, Boccardi M, Barkhof F, Blennow K, Cappa S, Chiotis K, et al. A Strategic Research Agenda to the Biomarker-Based Diagnosis of Prodromal Alzheimer’s Disease. :1–39.

14. Henriques AD, Benedet AL, Camargos EF, Rosa-Neto P, Nóbrega OT. Fluid and imaging biomarkers for Alzheimer’s disease: Where we stand and where to head to. Exp Gerontol [Internet]. Elsevier; 2018;(January):1–9. Available from: http://dx.doi.org/10.1016/j.exger.2018.01.002

15. Palmqvist S, Zetterberg H, Mattsson N, Johansson P, Minthon L, Blennow K, et al. Detailed comparison of amyloid PET and CSF biomarkers for identifying early Alzheimer disease. Neurology. 2015;85(14):1240–9.

16. Villemagne VL, Fodero-Tavoletti MT, Masters CL, Rowe CC. Tau imaging: early progress and future directions. Lancet Neurol. Elsevier; 2015;14(1):114–24.

17. Jack CRJ, Holtzman DM. Biomarker modeling of Alzheimer’s disease. Neuron. Elsevier; 2013;80(6):1347–58.

18. Roberts BR, Lind M, Wagen A. Biochemically-defined pools of Aβ-amyloid in 1185 Alzheimer’s disease: correlation with Aβ-PET imaging and a first approximation of 1186 accumulation rates of Aβ. Brain. 2017;

19. Aisen PS, Cummings J, Jack CR, Morris JC, Sperling R, Frölich L, et al. On the path to 2025: Understanding the Alzheimer’s disease continuum. Alzheimer’s Res Ther. Alzheimer’s Research & Therapy; 2017;9(1):1–10.

20. Bennett DA, Schneider JA, Arvanitakis Z, Kelly JF, Aggarwal NT, Shah RC, et al. Neuropathology of older persons without cognitive impairment from two community-based studies. Neurology. AAN Enterprises; 2006;66(12):1837–44.

21. Knopman DS, Parisi JE, Salviati A, Floriach-Robert M, Boeve BF, Ivnik RJ, et al. Neuropathology of cognitively normal elderly. J Neuropathol Exp Neurol. American Association of Neuropathologists, Inc.; 2003;62(11):1087–95.

22. Nelson PT, Head E, Schmitt FA, Davis PR, Neltner JH, Jicha GA, et al. Alzheimer’s disease is not “brain aging”: neuropathological, genetic, and epidemiological human studies. Acta Neuropathol. Springer; 2011;121(5):571–87.

23. Lee S, Viqar F, Zimmerman ME, Narkhede A, Tosto G, Benzinger TLS, et al. White matter hyperintensities are a core feature of Alzheimer’s disease: evidence from the dominantly inherited Alzheimer network. Ann Neurol. Wiley Online Library; 2016;79(6):929–39.

24. Montine TJ, Koroshetz WJ, Babcock D, Dickson DW, Galpern WR, Maria Glymour M, et al. Recommendations of the alzheimer’s disease-related dementias conference. Neurology. 2014;83(9):851–60.

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