Genetic and Clinical Correlates of AI-Based Brain Aging Patterns in Cognitively Unimpaired Individuals
Authors: Skampardoni Ioanna , Nasrallah Ilya M. , Abdulkadir Ahmed , Wen Junhao , Melhem Randa , Mamourian Elizabeth , Erus Guray , Doshi Jimit , Singh Ashish , Yang Zhijian , Cui Yuhan , Hwang Gyujoon , Ren Zheng , Pomponio Raymond , Srinivasan Dhivya , Govindarajan Sindhuja Tirumalai , Parmpi Paraskevi , Wittfeld Katharina , Grabe Hans J. , Bülow Robin , Frenzel Stefan , Tosun Duygu , Bilgel Murat , An Yang , Marcus Daniel S. , LaMontagne Pamela , Heckbert Susan R. , Austin Thomas R. , Launer Lenore J. , Sotiras Aristeidis , Espeland Mark A. , Masters Colin L. , Maruff Paul , Fripp Jurgen , Johnson Sterling C. , Morris John C. , Albert Marilyn S. , Bryan R. Nick , Yaffe Kristine , Völzke Henry , Ferrucci Luigi , Benzinger Tammie L.S. , Ezzati Ali , Shinohara Russell T. , Fan Yong , Resnick Susan M. , Habes Mohamad , Wolk David , Shou Haochang , Nikita Konstantina , Davatzikos Christos 
Published: February 2024

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.