Research Paper Volume 14, Issue 14 pp 5641—5668
Aging the brain: multi-region methylation principal component based clock in the context of Alzheimer’s disease
- 1 Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06511, USA
- 2 Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL 60612, USA
- 3 Department of Human Genetics, David Geffen School of Medicine, UCLA, Los Angeles, CA 90095, USA
- 4 Department of Biostatistics, Fielding School of Public Health, UCLA, Los Angeles, CA 90095, USA
- 5 Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06511, USA
- 6 Alzheimer’s Disease Research Center, Yale University School of Medicine, New Haven, CT 06510, USA
- 7 VA Connecticut Healthcare System, West Haven, CT 06516, USA
- 8 Department of Pathology, Yale University School of Medicine, New Haven, CT 06519, USA
- 9 Altos Labs, San Diego Institute of Science, San Diego, CA 92114, USA
Received: April 4, 2022 Accepted: July 5, 2022 Published: July 30, 2022https://doi.org/10.18632/aging.204196
How to Cite
Copyright: © 2022 Thrush et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Alzheimer’s disease (AD) risk increases exponentially with age and is associated with multiple molecular hallmarks of aging, one of which is epigenetic alterations. Epigenetic age predictors based on 5’ cytosine methylation (DNAm), or epigenetic clocks, have previously suggested that epigenetic age acceleration may occur in AD brain tissue. Epigenetic clocks are promising tools for the quantification of biological aging, yet we hypothesize that investigation of brain aging in AD will be assisted by the development of brain-specific epigenetic clocks. Therefore, we generated a novel age predictor termed PCBrainAge that was trained solely in cortical samples. This predictor utilizes a combination of principal components analysis and regularized regression, which reduces technical noise and greatly improves test-retest reliability. To characterize the scope of PCBrainAge’s utility, we generated DNAm data from multiple brain regions in a sample from the Religious Orders Study and Rush Memory and Aging Project. PCBrainAge captures meaningful heterogeneity of aging: Its acceleration demonstrates stronger associations with clinical AD dementia, pathologic AD, and APOE ε4 carrier status compared to extant epigenetic age predictors. It further does so across multiple cortical and subcortical regions. Overall, PCBrainAge’s increased reliability and specificity makes it a particularly promising tool for investigating heterogeneity in brain aging, as well as epigenetic alterations underlying AD risk and resilience.