Genetic and Clinical Correlates of AI-Based Brain Aging Patterns in Cognitively Unimpaired Individuals.

Autor:	Skampardoni I; Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia.; School of Electrical and Computer Engineering, National Technical University of Athens, Greece., Nasrallah IM; Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia.; Department of Radiology, University of Pennsylvania, Philadelphia., Abdulkadir A; Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia.; Laboratory for Research in Neuroimaging, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland., Wen J; Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia.; Laboratory of AI and Biomedical Science, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles., Melhem R; Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia., Mamourian E; Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia., Erus G; Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia., Doshi J; Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia., Singh A; Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia., Yang Z; Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia., Cui Y; Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia., Hwang G; Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia., Ren Z; Laboratory of AI and Biomedical Science, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles., Pomponio R; Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia., Srinivasan D; Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia., Govindarajan ST; Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia., Parmpi P; Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia., Wittfeld K; Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Germany.; German Centre for Neurodegenerative Diseases, Site Greifswald, Greifswald, Germany., Grabe HJ; Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Germany.; German Centre for Neurodegenerative Diseases, Site Greifswald, Greifswald, Germany., Bülow R; Institute of Diagnostic Radiology and Neuroradiology, University Medicine Greifswald, Greifswald, Germany., Frenzel S; Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Germany., Tosun D; Department of Radiology and Biomedical Imaging, University of California, San Francisco., Bilgel M; Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, Maryland., An Y; Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, Maryland., Marcus DS; Department of Radiology, Washington University School of Medicine, St Louis, Missouri., LaMontagne P; Department of Radiology, Washington University School of Medicine, St Louis, Missouri., Heckbert SR; Cardiovascular Health Research Unit, University of Washington, Seattle.; Department of Epidemiology, University of Washington, Seattle., Austin TR; Cardiovascular Health Research Unit, University of Washington, Seattle.; Department of Epidemiology, University of Washington, Seattle., Launer LJ; Neuroepidemiology Section, Intramural Research Program, National Institute on Aging, Bethesda, Maryland., Sotiras A; Department of Radiology and Institute of Informatics, Washington University in St Louis, St Louis, Missouri., Espeland MA; Sticht Centre for Healthy Aging and Alzheimer's Prevention, Wake Forest School of Medicine, Winston-Salem, North Carolina.; Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, North Carolina., Masters CL; Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, Victoria, Australia., Maruff P; Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, Victoria, Australia., Fripp J; CSIRO Health and Biosecurity, Australian e-Health Research Centre CSIRO, Brisbane, Queensland, Australia., Johnson SC; Wisconsin Alzheimer's Institute, University of Wisconsin School of Medicine and Public Health, Madison., Morris JC; Knight Alzheimer Disease Research Centre, Washington University in St Louis, St Louis, Missouri., Albert MS; Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland., Bryan RN; Department of Radiology, University of Pennsylvania, Philadelphia., Yaffe K; Departments of Neurology, Psychiatry and Epidemiology and Biostatistics, University of California San Francisco, San Francisco., Völzke H; Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany., Ferrucci L; Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, Maryland., Benzinger TLS; Mallinckrodt Institute of Radiology, Washington University School of Medicine in St Louis, St Louis, Missouri., Ezzati A; Department of Neurology, University of California, Irvine., Shinohara RT; Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia.; Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, & Informatics, University of Pennsylvania, Philadelphia., Fan Y; Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia., Resnick SM; Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, Maryland., Habes M; Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia.; Neuroimage Analytics Laboratory and the Biggs Institute Neuroimaging Core, Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Science Center San Antonio, San Antonio., Wolk D; Department of Neurology, University of Pennsylvania, Philadelphia., Shou H; Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia.; Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, & Informatics, University of Pennsylvania, Philadelphia., Nikita K; School of Electrical and Computer Engineering, National Technical University of Athens, Greece., Davatzikos C; Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia.
Jazyk:	angličtina
Zdroj:	JAMA psychiatry [JAMA Psychiatry] 2024 May 01; Vol. 81 (5), pp. 456-467.
DOI:	10.1001/jamapsychiatry.2023.5599
Abstrakt:	Importance: Brain aging elicits complex neuroanatomical changes influenced by multiple age-related pathologies. Understanding the heterogeneity of structural brain changes in aging may provide insights into preclinical stages of neurodegenerative diseases. Objective: To derive subgroups with common patterns of variation in participants without diagnosed cognitive impairment (WODCI) in a data-driven manner and relate them to genetics, biomedical measures, and cognitive decline trajectories. Design, Setting, and Participants: Data acquisition for this cohort study was performed from 1999 to 2020. Data consolidation and harmonization were conducted from July 2017 to July 2021. Age-specific subgroups of structural brain measures were modeled in 4 decade-long intervals spanning ages 45 to 85 years using a deep learning, semisupervised clustering method leveraging generative adversarial networks. Data were analyzed from July 2021 to February 2023 and were drawn from the Imaging-Based Coordinate System for Aging and Neurodegenerative Diseases (iSTAGING) international consortium. Individuals WODCI at baseline spanning ages 45 to 85 years were included, with greater than 50 000 data time points. Exposures: Individuals WODCI at baseline scan. Main Outcomes and Measures: Three subgroups, consistent across decades, were identified within the WODCI population. Associations with genetics, cardiovascular risk factors (CVRFs), amyloid β (Aβ), and future cognitive decline were assessed. Results: In a sample of 27 402 individuals (mean [SD] age, 63.0 [8.3] years; 15 146 female [55%]) WODCI, 3 subgroups were identified in contrast with the reference group: a typical aging subgroup, A1, with a specific pattern of modest atrophy and white matter hyperintensity (WMH) load, and 2 accelerated aging subgroups, A2 and A3, with characteristics that were more distinct at age 65 years and older. A2 was associated with hypertension, WMH, and vascular disease-related genetic variants and was enriched for Aβ positivity (ages ≥65 years) and apolipoprotein E (APOE) ε4 carriers. A3 showed severe, widespread atrophy, moderate presence of CVRFs, and greater cognitive decline. Genetic variants associated with A1 were protective for WMH (rs7209235: mean [SD] B = -0.07 [0.01]; P value = 2.31 × 10-9) and Alzheimer disease (rs72932727: mean [SD] B = 0.1 [0.02]; P value = 6.49 × 10-9), whereas the converse was observed for A2 (rs7209235: mean [SD] B = 0.1 [0.01]; P value = 1.73 × 10-15 and rs72932727: mean [SD] B = -0.09 [0.02]; P value = 4.05 × 10-7, respectively); variants in A3 were associated with regional atrophy (rs167684: mean [SD] B = 0.08 [0.01]; P value = 7.22 × 10-12) and white matter integrity measures (rs1636250: mean [SD] B = 0.06 [0.01]; P value = 4.90 × 10-7). Conclusions and Relevance: The 3 subgroups showed distinct associations with CVRFs, genetics, and subsequent cognitive decline. These subgroups likely reflect multiple underlying neuropathologic processes and affect susceptibility to Alzheimer disease, paving pathways toward patient stratification at early asymptomatic stages and promoting precision medicine in clinical trials and health care.
Databáze:	MEDLINE
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