Abstract
Objectives: Age-related hearing loss (ARHL) is highly prevalent among older adults, but the potential mechanisms and predictive markers for ARHL are lacking. Epigenetic age acceleration has been shown to be predictive of many age-associated diseases and mortality. However, the association between epigenetic age acceleration and hearing remains unknown. Our study aims to investigate the relationship between epigenetic age acceleration and audiometric hearing in the Baltimore Longitudinal Study of Aging (BLSA).Methods: Participants with both DNA methylation and audiometric hearing measurements were included. The main independent variables are epigenetic age acceleration measures, including intrinsic epigenetic age acceleration—“IEAA,” Hannum age acceleration—“AgeAccelerationResidualHannum,” PhenoAge acceleration—“AgeAccelPheno,” GrimAge acceleration—“AgeAccelGrim,” and methylation-based pace of aging estimation—“DunedinPoAm.” The main dependent variable is speech-frequency pure tone average. Linear regression was used to assess the association between epigenetic age acceleration and hearing.Results: Among the 236 participants (52.5% female), after adjusting for age, sex, race, time difference between measurements, cardiovascular factors, and smoking history, the effect sizes were 0.11 995% CI: (–0.00, 0.23), p = 0.054] for Hannum’s clock, 0.08 [95% CI: (–0.03, 0.19), p = 0.143] for Horvath’s clock, 0.10 [95% CI: (–0.01, 0.21), p = 0.089] for PhenoAge, 0.20 [95% CI: (0.06, 0.33), p = 0.004] for GrimAge, and 0.21 [95% CI: (0.09, 0.33), p = 0.001] for DunedinPoAm.Discussion: The present study suggests that some epigenetic age acceleration measurements are associated with hearing. Future research is needed to study the potential subclinical cardiovascular causes of hearing and to investigate the longitudinal relationship between DNA methylation and hearing.
Highlights
Age is one of the strongest risk factors for hearing loss which is prevalent in nearly two-thirds of adults over 70 years (Goman and Lin, 2016; Kuo et al, 2021)
Levine et al (2018) developed an algorithm using an aggregate measure of phenotypic aging (“PhenoAge”) that included biomarkers that are commonly measured in a clinical setting trained by predicting mortality, and train the epigenetic clock on this aggregate measure of phenotypic aging
We examine the relationship between several epigenetic clocks and audiometric hearing in the Baltimore Longitudinal Study of Aging (BLSA)
Summary
Age is one of the strongest risk factors for hearing loss which is prevalent in nearly two-thirds of adults over 70 years (Goman and Lin, 2016; Kuo et al, 2021). According to the geroscience paradigm, most age-related chronic diseases are caused by the shared biological mechanisms of aging (Ferrucci et al, 2018; Kuo et al, 2020). Among these hallmarks of aging, epigenetic change and DNA methylation have been associated with chronic disease (Salameh et al, 2020; Oblak et al, 2021). There have been several recent refinements of methods first suggested by Bocklandt et al (2011); Hannum et al (2013), and Horvath (2013). Levine et al (2018) developed an algorithm using an aggregate measure of phenotypic aging (“PhenoAge”) that included biomarkers that are commonly measured in a clinical setting trained by predicting mortality, and train the epigenetic clock on this aggregate measure of phenotypic aging. Lu et al (2019) used an alternate strategy first building epigenetics-based measures for informative aging markers and smoking pack-years, and summarize these measures into a composite score (“GrimAge”). Belsky et al (2020) proposed to first summarize the longitudinal rate of changes across several available phenotypes to create a summarized epigenetic score (“DunedinPoAm”) to predict the summarized rate of changes
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