Exploring the ATN classification system using brain morphology

2023-03-13 | journal article. A publication with affiliation to the University of Göttingen.

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​Exploring the ATN classification system using brain morphology​
Heinzinger, N.; Maass, A.; Berron, D.; Yakupov, R.; Peters, O.; Fiebach, J. & Villringer, K. et al.​ (2023) 
Alzheimer's Research & Therapy15(1) art. 50​.​ DOI: https://doi.org/10.1186/s13195-023-01185-x 

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Authors
Heinzinger, Nils; Maass, Anne; Berron, David; Yakupov, Renat; Peters, Oliver; Fiebach, Jochen; Villringer, Kersten; Preis, Lukas; Priller, Josef; Spruth, Eike J.; Altenstein, Slawek; Schneider, Anja; Fliessbach, Klaus; Wiltfang, Jens; Bartels, Claudia; Jessen, Frank; Maier, Franziska; Glanz, Wenzel; Buerger, Katharina; Janowitz, Daniel; Perneczky, Robert; Rauchmann, Boris-Stephan; Teipel, Stefan; Killimann, Ingo; Göerß, Doreen; Laske, Christoph; Munk, Matthias H.; Spottke, Annika; Roy, Nina; Heneka, Michael T.; Brosseron, Frederic; Dobisch, Laura; Ewers, Michael; Dechent, Peter; Haynes, John D.; Scheffler, Klaus; Wolfsgruber, Steffen; Kleineidam, Luca; Schmid, Matthias; Berger, Moritz; Düzel, Emrah; Ziegler, Gabriel
Abstract
Abstract Background The NIA-AA proposed amyloid-tau-neurodegeneration (ATN) as a classification system for AD biomarkers. The amyloid cascade hypothesis (ACH) implies a sequence across ATN groups that patients might undergo during transition from healthy towards AD: A−T−N−➔A+T−N−➔A+T+N−➔A+T+N+. Here we assess the evidence for monotonic brain volume decline for this particular (amyloid-conversion first, tau-conversion second, N-conversion last) and alternative progressions using voxel-based morphometry (VBM) in a large cross-sectional MRI cohort. Methods We used baseline data of the DELCODE cohort of 437 subjects (127 controls, 168 SCD, 87 MCI, 55 AD patients) which underwent lumbar puncture, MRI scanning, and neuropsychological assessment. ATN classification was performed using CSF-Aβ42/Aβ40 (A+/−), CSF phospho-tau (T+/−), and adjusted hippocampal volume or CSF total-tau (N+/−). We compared voxel-wise model evidence for monotonic decline of gray matter volume across various sequences over ATN groups using the Bayesian Information Criterion (including also ROIs of Braak stages). First, face validity of the ACH transition sequence A−T−N−➔A+T−N−➔A+T+N−➔A+T+N+ was compared against biologically less plausible (permuted) sequences among AD continuum ATN groups. Second, we evaluated evidence for 6 monotonic brain volume progressions from A−T−N− towards A+T+N+ including also non-AD continuum ATN groups. Results The ACH-based progression A−T−N−➔A+T−N−➔A+T+N−➔A+T+N+ was consistent with cognitive decline and clinical diagnosis. Using hippocampal volume for operationalization of neurodegeneration (N), ACH was most evident in 9% of gray matter predominantly in the medial temporal lobe. Many cortical regions suggested alternative non-monotonic volume progressions over ACH progression groups, which is compatible with an early amyloid-related tissue expansion or sampling effects, e.g., due to brain reserve. Volume decline in 65% of gray matter was consistent with a progression where A status converts before T or N status (i.e., ACH/ANT) when compared to alternative sequences (TAN/TNA/NAT/NTA). Brain regions earlier affected by tau tangle deposition (Braak stage I-IV, MTL, limbic system) present stronger evidence for volume decline than late Braak stage ROIs (V/VI, cortical regions). Similar findings were observed when using CSF total-tau for N instead. Conclusion Using the ATN classification system, early amyloid status conversion (before tau and neurodegeneration) is associated with brain volume loss observed during AD progression. The ATN system and the ACH are compatible with monotonic progression of MTL atrophy. Trial registration DRKS00007966, 04/05/2015, retrospectively registered.
Issue Date
13-March-2023
Journal
Alzheimer's Research & Therapy 
Organization
Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE) ; Klinik für Psychiatrie und Psychotherapie ; MR‐Forschung in den Neurowissenschaften 
Language
English

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