Exploring the ATN classification system using brain morphology

Medientyp: E-Artikel
Titel: Exploring the ATN classification system using brain morphology
Beteiligte: Heinzinger, Nils; Maass, Anne; Yakupov, Renat; Schütze, Hartmut; Spottke, Annika; Ramirez, Alfredo; Schneider, Anja; Metzger, Coraline D.; Laske, Christoph; Bittner, Daniel; Brosseron, Frederic; Priller, Josef; Wiltfang, Jens; Buerger, Katharina; Fließbach, Klaus; Heneka, Michael T.; Peters, Oliver; Speck, Oliver; Nestor, Peter J.; Teipel, Stefan J.; Pross, Verena; Glanz, Wenzel; Wagner, Michael; Jessen, Frank; [...]
Erschienen: Wiley, 2021
Erschienen in: Alzheimer's & Dementia
Sprache: Englisch
DOI: 10.1002/alz.052958
ISSN: 1552-5260; 1552-5279
Schlagwörter: Psychiatry and Mental health ; Cellular and Molecular Neuroscience ; Geriatrics and Gerontology ; Neurology (clinical) ; Developmental Neuroscience ; Health Policy ; Epidemiology
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Beschreibung: <jats:title>Abstract</jats:title><jats:sec><jats:title>Background</jats:title><jats:p>The NIA‐AA proposed ATN (Amyloid/Tau/Neurodegeneration) as a classification system for AD pathology. 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; therefore ‘ATN’) and alternative progressions (ANT, TAN, TNA, NAT, NTA) using Voxel‐based Morphometry (VBM) of brain anatomy in a large MRI sample.</jats:p></jats:sec><jats:sec><jats:title>Method</jats:title><jats:p>We used the DELCODE cohort of 437 subjects (49% female) which underwent lumbar puncture, MRI scanning and neuropsychological assessment. ATN classification was performed using (A+/‐) CSF‐Abeta42over40, (T+/‐) CSF‐phospho‐Tau, and (N+/‐) adjusted hippocampal volume. We compared voxel‐based model evidence for monotonic decline of gray matter volume across various sequences over ATN groups accounting for age, sex, education, TIV and WMH. The evidence of each progression was assessed using the Bayesian Information Criterion on voxel‐ and ROI‐level. First, face validity of the ACH transition trajectory A‐T‐N‐→A+T‐N‐→A+T+N‐→A+T+N+ for VBM was compared against 23 biologically less plausible (permuted) sequences among AD‐continuum ATN groups. Then we evaluated the evidence for 6 brain volume progressions from A‐T‐N‐ towards A+T+N+ (ATN, ANT, TAN, TNA, NAT, NTA) including also non‐AD continuum ATN groups.</jats:p></jats:sec><jats:sec><jats:title>Result</jats:title><jats:p>The ACH‐based progression A‐T‐N‐→A+T‐N‐→A+T+N‐→A+T+N+ is in line with cognitive decline and clinical diagnosis (Figure 1&2). It also has highest evidence in 9% of the gray matter voxels (especially MTL; Figure 3&4). Many (especially cortical) regions were compatible with alternative non‐monotonic volume progressions (‘AP 1’: 16%, ‘AP 2’: 14%; see Figure 3) over ACH progression sequence, compatible with early amyloid‐related tissue expansion or sampling effects due to brain‐reserve (Figure 5). Volume decline in 65% of voxels was more compatible with ATN/ANT progression (A flips first) when compared to alternative sequences (TAN, TNA, NAT, NTA).</jats:p></jats:sec><jats:sec><jats:title>Conclusion</jats:title><jats:p>Early Amyloid status conversion (before Tau and Neurodegeneration) is compatible with brain volume loss observed during AD progression. The ATN classification and the ACH are compatible with monotonic progress of MTL atrophy.</jats:p></jats:sec>

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