Potential Uses of ATX(N) Classification of Alzheimer Disease

Alzheimer disease (AD) is a neurodegenerative disease and the most common cause of dementia, which affects around 43.8 million individuals and ranks as the fifth leading cause of death globally.¹ The epidemic growth and the current lack of cures make AD one of the most significant unmet needs in medicine. The traditional diagnosis of AD is based on the identification of typical clinical symptoms.² Diagnostic frameworks based on clinical features, however, are not suitable for multifactorial and chronic neurodegenerative diseases like AD that have long preclinical or prodromal stages—a critical time window for early intervention.³ Moreover, significant inter-individual heterogeneity exists in terms of the onset and progression of the disease, owing to its biologic complexity and multi-faceted nature.

ATX(N) Is Unbiased and Descriptive

The last 3 decades have seen significant progress in the development and validation of fluid and neuroimaging biomarkers charting distinct AD pathophysiologic alterations. The integration of biomarkers in clinical research in the past decade has catalyzed the conceptual transformation of AD to a clinical-biologic construct.⁴ As such, the definition of AD has shifted from a symptom-based framework to a biologic continuum spanning preclinical, prodromal, and dementia stages. In this context, a biomarker-driven classification system called the AT(N), ie, amyloid-β deposition (A), tau (T), and neurodegeneration/neuronal injury (N), system was formulated in 2016 that provides a unified conceptual approach to categorize individuals by AD pathophysiology as reflected by the biomarker panel.⁵

The AT(N) biomarker-based system classifies individuals in an unbiased “symptoms-agnostic” fashion based on the presence of AD-related pathophysiologic changes. This system is flexible to integrate new biomarkers reflecting additional pathophysiologic mechanisms as knowledge on disease biology progresses. As such, the system will evolve toward ATX(N), where X stands for novel candidate biomarkers for additional AD-related pathophysiologic processes.

Potential Uses of the ATX(N) System

The ATX(N) framework provides a guide to the biologic investigation of AD, and its potential contexts of use span from screening, diagnostic confirmation, prognosis, target engagement (in clinical trials), disease modification, and treatment monitoring.

For example, studies showed that a significant portion of individuals with clinical presentation of mild cognitive impairment due to AD or mild AD dementia did not have amyloid-β (Aβ) pathology, which is essential to define the AD biological continuum.⁶ Such individuals should not be included in AD therapy trials because other non-AD pathologies likely underlie the clinical symptoms. This is just one of the examples of how the ATX(N) system can be used to better design and conduct clinical trials.

As another example, in older adults without dementia, those with positive A biomarkers combined with either T or N biomarkers (or both) exhibited significantly faster memory decline in a 5-year period than those with other ATX(N) profiles.⁷ This study suggests that the ATX(N) system could have prognostic value in predicting memory decline.

The Promise of Blood-Based Biomarkers

A particularly exciting direction of AD biomarker research is the development of blood-based biomarkers, such as plasma Aβ_42/40,⁸ p-tau181,^9-11 and p-tau217.¹² Once developed and validated in relevant clinical populations, a blood-based ATX(N) system could enable large-scale biologic screening and diagnostic-therapeutic decision-making and represents a globally accessible and sustainable solution for the next-generation AD patient journey and for a precision medicine-oriented approach.

Acknowledgment

This article is based on “The Development of the ATX(N) Classification System for Different Contexts of Use Across the Alzheimer’s Disease Continuum: State-of-the-art and Future Perspectives for Clinical Practice and Therapy Development,” a medical symposium hosted by Eisai at the virtual Alzheimer’s Association International Conference 2020 (AAIC). Faculty included Dr. Jeffrey Cummings, Dr. Clifford Jack, and Dr. Kaj Blennow