The more data that is accumulated on the behavior of aging clocks in response to interventions and lifestyle choices known to correlate with life expectancy in humans, the more useful these clocks become. The challenge in the use of the aging clocks established to date is that there is no good understanding of how the measurements making up the clock algorithm, such as the methylation status at specific CpG sites on the genome, relate to mechanisms of aging and disease. Without a great deal more data, researchers cannot predict whether or not a clock will perform well in assessing the effects of a novel intervention intended to slow aging or produce rejuvenation. In the worst case, the only way to calibrate a clock against a specific intervention is to run long studies to assess mortality risk.
Physical inactivity and sedentary behavior are associated with higher risks of age-related morbidity and mortality. However, whether they causally contribute to accelerating biological aging has not been fully elucidated. Utilizing the largest available genome-wide association study (GWAS) summary data, we implemented a comprehensive analytical framework to investigate the associations between genetically predicted moderate-to-vigorous leisure-time physical activity (MVPA), leisure screen time (LST), and four epigenetic age acceleration (EAA) measures: HannumAgeAccel, intrinsic HorvathAgeAccel, PhenoAgeAccel, and GrimAgeAccel
Shared genetic backgrounds across these traits were quantified through genetic correlation analysis. Overall and independent associations were assessed through univariable and multivariable Mendelian randomization (MR). A recently developed tissue-partitioned MR approach was further adopted to explore potential tissue-specific pathways that contribute to the observed associations.
Among the four EAA measures investigated, consistent results were identified for PhenoAgeAccel and GrimAgeAccel. These two measures were negatively genetically correlated with MVPA (r = -0.18 to -0.29) and positively genetically correlated with LST (r = 0.22-0.37). Univariable MR yielded a robust effect of genetically predicted LST on GrimAgeAccel (β = 0.69), while genetically predicted MVPA (β = -1.02) and LST (β = 0.37) showed marginal effects on PhenoAgeAccel. Multivariable MR suggested an independent association between genetically predicted LST and GrimAgeAccel after accounting for MVPA and other important confounders. Tissue-partitioned MR suggested skeletal muscle tissue associated variants to be predominantly responsible for driving the effect of LST on GrimAgeAccel.
Findings support sedentary lifestyles as a modifiable risk factor in accelerating epigenetic aging, emphasizing the need for preventive strategies to reduce sedentary screen time for healthy aging.
Link: https://doi.org/10.1111/sms.70014
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