Abstract
AbstractBackgroundOur group previously developed PCBrainAge, a novel predictor of brain tissue measured DNA methylation (DNAm) aging. We find that this predictor is correlated with a variety of components of Alzheimer’s disease (AD), including neuropathological criterion, clinical diagnoses, and APOE ε4 carrier status.MethodWe collected publicly available paired blood and brain tissue DNAm from cohorts annotated with AD diagnostic criterion. This includes the gene expression omnibus (GEO) dataset GSE59685 (n = 48). Where possible, we elected to compare blood measures to cortical measures due to the training parameters of PCBrainAge. PCBrainAge scores were calculated: In brief, PCBrainAge uses our method of principal component projection as defined by the original training datasets, followed by pretrained weighted regression to provide a single valued age prediction for the sample of interest. PCBrainAge was calculated and its acceleration, as an age residual, was determined separately for blood and brain samples.ResultWhile PCBrainAge Acceleration retains our previously discussed significant difference between AD and control samples in brain, it does not demonstrate any such distinction in blood tissue. We find that the principal components selected for regression in the PCBrainAge model training most highly weight CpGs that tend to be associated with brain specific pathways and development.ConclusionTherefore, despite recent evidence supporting systemic changes or even pathogenesis of AD, PCBrainAge capture of neuropathological changes and elevated AD risk associated with APOE ε4 carrier status appears reliant upon brain‐specific DNAm. This indicates that PCBrainAge has the most potential to be used as an investigative tool to track stages of AD development and interrogate underlying molecular changes. However, more work must be done to generate a harmonized brain and blood clock that captures neuropathology and disease to PCBrainAge’s current extent.
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