A generalizable data‐driven model of atrophy heterogeneity and progression in a memory clinic setting

Abstract Background Memory clinic patients are a heterogeneous population representing various aetiologies of pathological aging. It is unknown if divergent spatiotemporal progression patterns of brain atrophy, as previously described in Alzheimer’s disease (AD) patients, are prevalent and clinically meaningful in this group of older adults. Method To uncover atrophy subtypes, we applied the Subtype and Stage Inference (SuStaIn) algorithm to structural MRI data from 813 participants (mean ± SD age = 70.67 ± 6.07 years, 52% females) from the DELCODE cohort. Participants were cognitively unimpaired (CU; n = 285) or patients with subjective cognitive decline (SCD; n = 342), mild cognitive impairment (MCI; n = 118), or dementia of the Alzheimer’s type (n = 68). Atrophy subtypes were compared in baseline demographics, fluid AD biomarkers, and domain‐specific cognitive performance. PACC‐5 trajectories over up to 240 weeks were examined. Clinical trajectories (PACC‐5 scores and MCI conversion rates) in only CU and SCD participants were analysed. SuStaIn modelling was repeated in participants from the Swedish BioFINDER‐2 study for replication and generalizability testing. Result Limbic‐predominant and hippocampal‐sparing atrophy subtypes were identified (Figure 1). Limbic‐predominant atrophy first affected the medial temporal lobes, followed by further temporal and, finally, the remaining cortical regions. This subtype was related to older age, more pathological AD biomarkers, APOE ε4 carriership, and an amnestic cognitive impairment. Hippocampal‐sparing atrophy initially occurred outside the temporal lobe and spared the medial temporal lobe until advanced stages. This atrophy pattern also affected individuals with positive AD biomarkers and was associated with more generalised cognitive impairment. Limbic‐predominant atrophy, in all and in only unimpaired participants, was linked to more negative longitudinal PACC‐5 slopes than observed in participants without or with hippocampal‐sparing atrophy (Figure 2) and increased the risk of MCI conversion. In BioFINDER‐2, analogous atrophy subtypes and cognitive correlates were identified. Group‐ and subject‐level model generalizability were excellent, indicating reliable performance in novel data (Figure 3). Conclusion The proposed model is a promising tool for capturing heterogeneity among older adults at early at‐risk states for AD in applied settings. The implementation of atrophy subtype‐ and stage‐specific end‐points may increase the statistical power of pharmacological trials targeting early AD.

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Publication Details

Journal
Alzheimer s & Dementia
Published
2024-12-01
DOI
https://doi.org/10.1002/alz.092184
Primary Topic
Machine Learning in Healthcare
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article
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article

A generalizable data‐driven model of atrophy heterogeneity and progression in a memory clinic setting

Emrah Düzel, Andrea Lohse, Silka Dawn Freiesleben, Jens Wiltfang et al.
Alzheimer s & Dementia
Machine Learning in Healthcare
article

A generalizable data‐driven model of atrophy heterogeneity and progression in a memory clinic setting

Emrah Düzel, Andrea Lohse, Silka Dawn Freiesleben, Jens Wiltfang, Ayda Rostamzadeh, Lukas Preis, Robert Perneczky, Annika Spottke, Oliver Peters, Stefan Johannes Teipel, David Berron, Sebastian Palmqvist, Erik Stomrud, Gabriel Ziegler, Frederic Brosseron, Alfredo Ramı́rez, Matthias Schmid, Olof T. Strandberg, Boris‐Stephan Rauchmann, Klaus Scheffler, Christoph Laske, Renat Yakupov, Helena M. Gellersen, Luca Kleineidam, Michael Ewers, Niklas Mattsson, Michael Wagner, Katharina Büerger, Oskar Hansson, Claudia Bartels, Klaus Fließbach, Hannah Baumeister, Philip S. Insel, Enise Irem Incesoy, Josef Priller, Steffen Wolfsgruber, Anika Wuestefeld, Frank Jessen, Stefan Hetzer, Daniel Janowitz, Danielle van Westen, Ingo Kilimann, Anja Schneider, Michaela Butryn, Michael T. Heneka, Slawek Altenstein, Luisa‐Sophie Schneider, Ina R. Vogt, Wenzel Glanz, Shorena Janelidze, Melina Stark, Jacob W. Vogel, Björn H. Schott, Falk Lüsebrink, Peter Dechent, Eike Jakob Spruth, Matthias H. Munk, Doreen Göerß
article en

Abstract

Abstract Background Memory clinic patients are a heterogeneous population representing various aetiologies of pathological aging. It is unknown if divergent spatiotemporal progression patterns of brain atrophy, as previously described in Alzheimer’s disease (AD) patients, are prevalent and clinically meaningful in this group of older adults. Method To uncover atrophy subtypes, we applied the Subtype and Stage Inference (SuStaIn) algorithm to structural MRI data from 813 participants (mean ± SD age = 70.67 ± 6.07 years, 52% females) from the DELCODE cohort. Participants were cognitively unimpaired (CU; n = 285) or patients with subjective cognitive decline (SCD; n = 342), mild cognitive impairment (MCI; n = 118), or dementia of the Alzheimer’s type (n = 68). Atrophy subtypes were compared in baseline demographics, fluid AD biomarkers, and domain‐specific cognitive performance. PACC‐5 trajectories over up to 240 weeks were examined. Clinical trajectories (PACC‐5 scores and MCI conversion rates) in only CU and SCD participants were analysed. SuStaIn modelling was repeated in participants from the Swedish BioFINDER‐2 study for replication and generalizability testing. Result Limbic‐predominant and hippocampal‐sparing atrophy subtypes were identified (Figure 1). Limbic‐predominant atrophy first affected the medial temporal lobes, followed by further temporal and, finally, the remaining cortical regions. This subtype was related to older age, more pathological AD biomarkers, APOE ε4 carriership, and an amnestic cognitive impairment. Hippocampal‐sparing atrophy initially occurred outside the temporal lobe and spared the medial temporal lobe until advanced stages. This atrophy pattern also affected individuals with positive AD biomarkers and was associated with more generalised cognitive impairment. Limbic‐predominant atrophy, in all and in only unimpaired participants, was linked to more negative longitudinal PACC‐5 slopes than observed in participants without or with hippocampal‐sparing atrophy (Figure 2) and increased the risk of MCI conversion. In BioFINDER‐2, analogous atrophy subtypes and cognitive correlates were identified. Group‐ and subject‐level model generalizability were excellent, indicating reliable performance in novel data (Figure 3). Conclusion The proposed model is a promising tool for capturing heterogeneity among older adults at early at‐risk states for AD in applied settings. The implementation of atrophy subtype‐ and stage‐specific end‐points may increase the statistical power of pharmacological trials targeting early AD.

Alzheimer s & DementiaVol. 20(S2)
University of Bonn (DE), University of California, San Francisco (US), University of Cologne (DE), University of Luxembourg (LU), Lund University (SE), University Hospital Bonn (DE), German Center for Neurodegenerative Diseases (DE), LMU Klinikum (DE), Humboldt-Universität zu Berlin (DE), Zimmer Biomet (Germany) (DE), Institute for Neurodegenerative Disorders (US), Munich Cluster for Systems Neurology (DE), UK Dementia Research Institute (GB), Center for Behavioral Brain Sciences (DE), Skåne University Hospital (SE), Cologne Excellence Cluster on Cellular Stress Responses in Aging Associated Diseases (DE), Hertie Institute for Clinical Brain Research (DE), University Hospital Cologne (DE), University of Rostock (DE), University of Aveiro (PT), Technical University of Munich (DE), University of Göttingen (DE), Freie Universität Berlin (DE), Charité - Universitätsmedizin Berlin (DE), University of Tübingen (DE), Ludwig-Maximilians-Universität München (DE), University of Sheffield (GB), Otto-von-Guericke-Universität Magdeburg (DE), University of Edinburgh (GB)
Openalex Percentile: Top 77%
Machine Learning in Healthcare
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