Epigenomic subtypes of late-onset Alzheimer's disease reveal distinct microglial signatures.

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 = 831) to identify epigenetic subtypes of LOAD. Unsupervised clustering approaches were employed to identify distinct DNAm patterns, with subsequent cross-cohort validation. We assessed how subtype-specific methylation signatures map onto individual brain cell types by comparing them with DNAm profiles from purified cells. Next, we integrated bulk and single-cell RNA-seq data to determine each subtype's functional impact on gene expression. Finally, we explored clinical and neuropathological correlates of the identified subtypes to elucidate 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 further 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-cell transcriptional profiling of microglia revealed subtype-specific inflammatory states: subtype 1 displayed chronic innate immune hyperactivation with impaired resolution, whereas subtype 2 exhibited a more dynamic inflammatory profile, balancing pro-inflammatory signaling with reparative and regulatory mechanisms. 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.