AI Article Synopsis

  • The ATN scheme categorizes Alzheimer's disease biomarkers into three groups: Amyloidosis (A), Tauopathy (T), and Neurodegeneration (N), providing a framework that is relevant for both research and diagnosis.
  • In a study with 1,128 patients from various centers, the prevalence of ATN classifications showed that 47.8% fell into the Alzheimer's disease continuum, and ATN profiles were heavily influenced by factors like age and cognitive status.
  • While the ATN scheme effectively guides Alzheimer's diagnosis in real-world settings, it struggles with accuracy for other dementia types; adding markers for non-Alzheimer's conditions could improve differential diagnosis.

Article Abstract

Background: The ATN scheme was proposed as an unbiased biological characterization of the Alzheimer's disease (AD) spectrum, grouping biomarkers into three categories: brain Amyloidosis-A, Tauopathy-T, Neurodegeneration-N. Although this scheme was mainly recommended for research, it is relevant for diagnosis.

Objective: To evaluate the ATN scheme performance in real-life cohorts reflecting the inflow of patients with cognitive complaints and different underlying disorders in general neurological centers.

Methods: We included patients (n = 1,128) from six centers with their core cerebrospinal fluid-AD biomarkers analyzed centrally. A was assessed through Aβ42/Aβ40, T through pTau-181, and N through tTau. Association between demographic features, clinical diagnosis at baseline/follow-up and ATN profiles was assessed.

Results: The prevalence of ATN categories was: A-T-N-: 28.3%; AD continuum (A + T-/+N-/+): 47.8%; non-AD (A- plus T or/and N+): 23.9%. ATN profiles prevalence was strongly influenced by age, showing differences according to gender, APOE genotype, and cognitive status. At baseline, 74.6% of patients classified as AD fell in the AD continuum, decreasing to 47.4% in mild cognitive impairment and 42.3% in other neurodegenerative conditions. At follow-up, 41% of patients changed diagnosis, and 92% of patients that changed to AD were classified within the AD continuum. A + was the best individual marker for predicting a final AD diagnosis, and the combinations A + T+ (irrespective of N) and A + T+N+ had the highest overall accuracy (83%).

Conclusion: The ATN scheme is useful to guide AD diagnosis in real-life neurological centers settings. However, it shows a lack of accuracy for patients with other types of dementia. In such cases, the inclusion of other markers specific for non-AD proteinopathies could be an important aid to the differential diagnosis.

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Source
http://dx.doi.org/10.3233/JAD-220587DOI Listing

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