Aging the brain: multi-region methylation principal component based clock in the context of Alzheimer’s disease

Abstract

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.

Epigenetic clocks in neurodegenerative diseases: a systematic review

“…The original detailed information of 11 epigenetic clocks are listed in table 1. In AD, three-quarters (9/12) of articles measured DNAm age using blood,7 9 37–43 and the remaining 3 articles using brain tissues 28 44 45. Furthermore, six out of the nine blood-based articles obtained data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database,38–43 while the samples of the three brain-based studies all came from the Religious Orders Study and the Rush Memory and Aging Project (ROSMAP) 28 44 45.…”

“…In AD, three-quarters (9/12) of articles measured DNAm age using blood,7 9 37–43 and the remaining 3 articles using brain tissues 28 44 45. Furthermore, six out of the nine blood-based articles obtained data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database,38–43 while the samples of the three brain-based studies all came from the Religious Orders Study and the Rush Memory and Aging Project (ROSMAP) 28 44 45. All four studies focused on PD were based on peripheral blood,5 10 31 46 and two of the studies obtained data from the Parkinson’s disease, Environment and Genes study 31 46.…”

“…Some other newer clocks have since been designed for specific utilisation. For example, the PhenoAge clock25 predicted ageing outcomes, especially for all-cause mortality; the GrimAge clock26 incorporating cigarette smoking-related DNAm changes was good at predicting lifespan and healthspan; the Cortical clock27 was the first brain tissue-based epigenetic clock for predicting DNAm age of human cortex, which might be more representative in neurodegenerative diseases; the recent PCBrainAge clock28 with increased reliability and specificity was trained for investigating heterogeneity in brain ageing and AD pathology. Many epigenetic clocks (Horvath,15 Hannum,24 PhenoAge,25 GrimAge,26 etc) could be calculated with the well-established open source online calculator at https://dnamage.genetics.ucla.edu/, and the codes for other clocks were usually provided by authors in the original text.…”

Sixteen-year longitudinal evaluation of blood-based DNA methylation biomarkers for early prediction of Alzheimer’s disease

“…It should be noted that in our study, as in previous ones, the correlations between epigenetic ages (Horvath, Hannum, PhenoAge) and chronological age were consistently high across both AD cases and controls [73], demonstrating the validity of our data and epigenetic age estimations in general. The possibility also remains that EAA in brain tissue signi cantly differentiates AD from controls [85], albeit not useful as a predictive biomarker due to its invasiveness, or that novel computational solutions improve the reliability of epigenetic clocks and thereby their predictive potential [98].…”

Twenty-year longitudinal evaluation of blood-based DNA methylation biomarkers for early prediction of Alzheimer’s disease

“…demonstrating the validity of our data and epigenetic age estimations in general. The possibility also remains that epigenetic age acceleration in brain tissue signi cantly differentiates AD from controls [85], albeit not useful as a predictive biomarker due to its invasiveness.…”