Abstract
Growing evidence suggests that clinical, pathological, and genetic heterogeneity in late-onset Alzheimer's disease (LOAD) contributes to variable therapeutic outcomes, potentially explaining many trial failures. Advances in molecular subtyping through proteomic and transcriptomic profiling reveal distinct patient subgroups, highlighting disease complexity beyond amyloid-beta plaques and tau tangles. This underscores the need to expand subtyping across new molecular layers, to identify novel drug targets for different patient subgroups. In this study, we analyzed genome-wide DNA methylation (DNAm) data from three independent postmortem brain cohorts (N = 826) to identify epigenetic subtypes of LOAD. We used unsupervised clustering to define subtype-specific DNAm patterns and validated them across cohorts. We then mapped subtype signatures to brain cell types using purified-cell DNAm profiles and integrated bulk and single-nucleus RNA-seq to assess each subtype's impact on gene expression. Finally, we examined clinical and neuropathological correlates to evaluate biological and clinical significance. We identified two distinct epigenomic subtypes of LOAD, consistently observed across three cohorts. Both subtypes exhibit significant yet distinct microglial methylation enrichment. Bulk transcriptomic analyses highlighted distinct biological mechanisms underlying these subtypes: subtype 1 was enriched for immune-related processes, while subtype 2 was characterized by neuronal and synaptic pathways. Single-nucleus transcriptional profiling of microglia indicated that both subtypes share AD-associated innate-immune remodeling, with subtype differences emerging primarily as state-dependent transcriptional shifts rather than large changes in state abundance. Overall, subtype 1 showed a relative weighting toward more inflammatory microglial programs, whereas subtype 2 showed stronger transcriptional remodeling in specific microglial states alongside relatively greater engagement of regulatory and clearance-associated features. These findings reveal distinct epigenetic and functional microglial states underlying LOAD subtypes, advancing our understanding of disease heterogeneity. This work lays the groundwork for targeted therapeutic strategies tailored to specific molecular and cellular disease profiles.