Machine learning-derived Alzheimer’s disease dimensions: neuroanatomical, cognitive, clinical, functional, and genetic risk heterogeneity signatures

Alzheimer’s disease (AD) is characterized by substantial clinical and biological heterogeneity, with diverse neuroanatomical, cognitive, clinical, functional, and genetic risk profiles that are poorly captured by current diagnostic frameworks. Identifying reproducible disease dimensions is critical for understanding divergent pathophysiological mechanisms and designing future stratified therapeutic interventions. Herein, we applied a semi-supervised machine learning clustering framework and robust reproducibility validation strategies to structural MRI data from 1,757 participants, comprising 1,108 cognitively normal controls (CN) and 649 patients with mild cognitive impairment (MCI) or dementia due to AD. We identified two MRI-based neuroanatomical dimensions of AD: Dimension 1 ( n = 393; prevalence ∼ 61%) and Dimension 2 ( n = 256; prevalence ∼ 39%). Dimension 1 exhibited widespread cortical and subcortical atrophy, including involvement of the hippocampus, amygdala, parahippocampal, temporal, frontal, and occipital regions, while Dimension 2 showed relatively preserved brain patterns. The two dimensions were not statistically different in demographic characteristics, including age, sex, race, and education (all p > 0.05). Compared to Dimension 2, Dimension 1 had more severe cognitive and clinical impairment, greater functional impairment, elevated APOE ε4 carrier burden, and higher polygenic risk for AD (p < 0.05). Analysis of individual-level summary neuroanatomical signature expression scores, quantifying each subject’s continuous position along two disease dimensions, revealed that Dimension 1 signature expression correlated strongly with cognitive impairment, clinical severity, functional impairment, and genetic risk, while Dimensional 2 signature expression showed relatively weaker associations. These results reveal that AD encompasses at least two biologically distinct dimensions identifiable from structural MRI profiles and their distinct associations with cognitive, clinical, functional, and genetic profiles. These findings may aid in AD patient stratification for designing targeted therapeutic approaches in the future.