TY - JOUR
T1 - Genetic and Clinical Correlates of AI-Based Brain Aging Patterns in Cognitively Unimpaired Individuals
AU - Skampardoni, Ioanna
AU - Nasrallah, Ilya M.
AU - Abdulkadir, Ahmed
AU - Wen, Junhao
AU - Melhem, Randa
AU - Mamourian, Elizabeth
AU - Erus, Guray
AU - Doshi, Jimit
AU - Singh, Ashish
AU - Yang, Zhijian
AU - Cui, Yuhan
AU - Hwang, Gyujoon
AU - Ren, Zheng
AU - Pomponio, Raymond
AU - Srinivasan, Dhivya
AU - Govindarajan, Sindhuja Tirumalai
AU - Parmpi, Paraskevi
AU - Wittfeld, Katharina
AU - Grabe, Hans J.
AU - Bülow, Robin
AU - Frenzel, Stefan
AU - Tosun, Duygu
AU - Bilgel, Murat
AU - An, Yang
AU - Marcus, Daniel S.
AU - Lamontagne, Pamela
AU - Heckbert, Susan R.
AU - Austin, Thomas R.
AU - Launer, Lenore J.
AU - Sotiras, Aristeidis
AU - Espeland, Mark A.
AU - Masters, Colin L.
AU - Maruff, Paul
AU - Fripp, Jurgen
AU - Johnson, Sterling C.
AU - Morris, John C.
AU - Albert, Marilyn S.
AU - Bryan, R. Nick
AU - Yaffe, Kristine
AU - Völzke, Henry
AU - Ferrucci, Luigi
AU - Benzinger, Tammie L.S.
AU - Ezzati, Ali
AU - Shinohara, Russell T.
AU - Fan, Yong
AU - Resnick, Susan M.
AU - Habes, Mohamad
AU - Wolk, David
AU - Shou, Haochang
AU - Nikita, Konstantina
AU - Davatzikos, Christos
N1 - Publisher Copyright:
© 2024 American Medical Association. All rights reserved.
PY - 2024/5/1
Y1 - 2024/5/1
N2 - 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 50000 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 27402 individuals (mean [SD] age, 63.0 [8.3] years; 15146 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 × 10-15and 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..
AB - 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 50000 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 27402 individuals (mean [SD] age, 63.0 [8.3] years; 15146 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 × 10-15and 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..
UR - http://www.scopus.com/inward/record.url?scp=85185283495&partnerID=8YFLogxK
U2 - 10.1001/jamapsychiatry.2023.5599
DO - 10.1001/jamapsychiatry.2023.5599
M3 - Article
C2 - 38353984
AN - SCOPUS:85185283495
SN - 2168-622X
VL - 81
SP - 456
EP - 467
JO - JAMA psychiatry
JF - JAMA psychiatry
IS - 5
ER -