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Jack CR Jr, Bennett DA, Blennow K, Carrillo MC, Feldman HH, Frisoni GB, Hampel H, Jagust WJ, Johnson KA, Knopman DS, Petersen RC, Scheltens P, Sperling RA, Dubois B. A/T/N: An unbiased descriptive classification scheme for Alzheimer disease biomarkers. Neurology. 2016 Aug 2;87(5):539-47. Epub 2016 Jul 1 PubMed.
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Neuronetrix
In Jack et al., the authors propose a broad new biomarker-based disease classification scheme that could be useful in AD and other dementing disorders. This is an important paper, where the authors encourage the whole community to step back and re-examine the scientific framework for assessing affected patients and describing the disease-related effects.
While the current IWG and NIA-AA biomarker classification systems are linked to a specific disease hypotheses, and are based on consensus diagnostic criteria, the authors propose a new descriptive system for categorizing multi-domain biomarker findings at the individual person level. In their proposed A/T/N classification system, the AD biomarkers are divided into three binary classes for Aβ and tau pathology and neurodegeneration biomarkers. This is a forward-thinking approach that relies on objective measures, is easy to understand and use, is applicable across all clinical diagnostic states, and in the long run will provide a more complete clinical picture of dementing disorders.
As suggested in the manuscript, the classification system would benefit from the inclusion of a separate category for synaptic dysfunction (S). Synaptic loss leads to a breakdown in the brain’s bioelectric neural network that is central to neurodegenerative disorders (Herrup, 2015; Fox, 1999).
While the authors suggest the use of several functional, biochemical, and anatomical biomarkers of synaptic dysfunction, it may be useful to broaden the focus and include measures of the bioelectrical activity of large-scale synaptic networks (E) such as event-related potentials (ERPs) and quantitative EEGs (QEEGs). These tests reflect the information-processing functions of the brain and are not captured using the other proposed modalities. While these electrophysiologic biomarkers historically have been difficult to capture in a practical and cost-effective way, technological advances now allow for reliable, easy-to-perform, and inexpensive ERP and QEEG testing (Cecchi et al., 2015) that can provide Information complementary to A/T/N biomarkers, leading in the end to a better, more complete classification scheme; A/T/N/S/E.
References:
Herrup K. The case for rejecting the amyloid cascade hypothesis. Nat Neurosci. 2015 Jun;18(6):794-9. PubMed.
Fox NC, Scahill RI, Crum WR, Rossor MN. Correlation between rates of brain atrophy and cognitive decline in AD. Neurology. 1999 May 12;52(8):1687-9. PubMed.
Cecchi M, Moore DK, Sadowsky CH, Solomon PR, Doraiswamy PM, Smith CD, Jicha GA, Budson AE, Arnold SE, Fadem KC. A clinical trial to validate event-related potential markers of Alzheimer's disease in outpatient settings. Alzheimers Dement (Amst). 2015 Dec;1(4):387-94. Epub 2015 Oct 2 PubMed.
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