Distinct Microglial Profiles in Epigenomic Subtypes of Late-Onset Alzheimer’s Disease

Overview

This study identified distinct epigenomic subtypes of late-onset Alzheimer’s disease (LOAD) by analyzing genome-wide DNA methylation patterns in postmortem brain samples from three independent cohorts. The findings reveal molecular heterogeneity in LOAD linked to specific microglial profiles and provide insights into the biological mechanisms underlying disease variability.

Background

Late-onset Alzheimer’s disease (LOAD) is the most common dementia form, characterized by amyloid-beta plaques and neurofibrillary tangles leading to neuronal loss. Despite common pathological features, LOAD exhibits significant heterogeneity in onset, progression, and symptomatology. Molecular subtyping using omics data, including proteomics and transcriptomics, has uncovered distinct AD subtypes associated with immune activation, synaptic dysfunction, and metabolic alterations. Epigenetic modifications such as DNA methylation offer a promising avenue to further dissect LOAD heterogeneity by capturing gene-environment interactions influencing disease mechanisms.

Data Highlights

The study analyzed 835 cortical postmortem brain samples from three cohorts: 244 prefrontal cortex samples from UK Brain Banks Network (UKBBN), 220 prefrontal cortex samples from University of Pittsburgh Alzheimer’s Disease Research Center (PITT-ADRC), and 371 dorsolateral prefrontal cortex samples from the Religious Orders Study and Memory and Aging Project (ROSMAP). DNA methylation was profiled using Illumina Infinium MethylationEPIC and 450K arrays, covering over 850,000 CpG sites. Rigorous quality control and normalization were applied to harmonize data across cohorts.

Key Findings

• Distinct epigenomic subtypes of LOAD were identified based on DNA methylation similarity within and across three independent brain cohorts.
• These subtypes exhibited unique microglial DNA methylation profiles, suggesting differential immune-related mechanisms in LOAD heterogeneity.
• Subtype-specific methylation patterns correlated with known AD risk genes, implicating genetic and epigenetic interplay in disease pathogenesis.
• Transcriptomic analyses, including bulk and single-nucleus RNA sequencing, revealed subtype-specific gene expression signatures linked to microglial activation and other cellular pathways.
• Cross-cohort validation confirmed the robustness of the identified epigenomic subtypes and their biological relevance.

Clinical Implications

Recognizing distinct epigenomic subtypes of LOAD enhances understanding of the disease’s molecular heterogeneity, which may inform personalized therapeutic strategies targeting microglial function. Epigenetic markers could serve as potential biomarkers for subtype classification and prognosis. Furthermore, lifestyle factors influencing DNA methylation profiles highlight opportunities for modifiable interventions to delay or alter disease progression.

Conclusion

This study provides compelling evidence for epigenomic heterogeneity in late-onset Alzheimer’s disease, characterized by distinct microglial profiles and molecular signatures. These findings advance the molecular classification of LOAD and open avenues for targeted research and precision medicine approaches.

References

1. UK Brain Banks Network, University of Pittsburgh ADRC, ROSMAP Cohorts -- Epigenomic Subtyping in LOAD
2. Previous Studies on Molecular Subtypes of AD -- Proteomics and Transcriptomics
3. Lifestyle and Epigenetics in Dementia -- Impact of Physical Activity and Education