Genetic and environmental contributions to epigenetic aging across adolescence and young adulthood.

BACKGROUND

Epigenetic aging estimators commonly track chronological and biological aging, quantifying its accumulation (i.e., epigenetic age acceleration) or speed (i.e., epigenetic aging pace). Their scores reflect a combination of inherent biological programming and the impact of environmental factors, which are suggested to vary at different life stages. The transition from adolescence to adulthood is an important period in this regard, marked by an increasing and, then, stabilizing epigenetic aging variance. Whether this pattern arises from environmental influences or genetic factors is still uncertain. This study delves into understanding the genetic and environmental contributions to variance in epigenetic aging across these developmental stages. Using twin modeling, we analyzed four estimators of epigenetic aging, namely Horvath Acceleration, PedBE Acceleration, GrimAge Acceleration, and DunedinPACE, based on saliva samples collected at two timepoints approximately 2.5 years apart from 976 twins of four birth cohorts (aged about 9.5, 15.5, 21.5, and 27.5 years at first and 12, 18, 24, and 30 years at second measurement occasion).

RESULTS

Half to two-thirds (50-68%) of the differences in epigenetic aging were due to unique environmental factors, indicating the role of life experiences and epigenetic drift, besides measurement error. The remaining variance was explained by genetic (Horvath Acceleration: 24%; GrimAge Acceleration: 32%; DunedinPACE: 47%) and shared environmental factors (Horvath Acceleration: 26%; PedBE Acceleration: 47%). The genetic and shared environmental factors represented the primary sources of stable differences in corresponding epigenetic aging estimators over 2.5 years. Age moderation analyses revealed that the variance due to individually unique environmental sources was smaller in younger than in older cohorts in epigenetic aging estimators trained on chronological age (Horvath Acceleration: 47-49%; PedBE Acceleration: 33-68%). The variance due to genetic contributions, in turn, potentially increased across age groups for epigenetic aging estimators trained in adult samples (Horvath Acceleration: 18-39%; GrimAge Acceleration: 24-43%; DunedinPACE: 42-57%).

CONCLUSIONS

Transition to adulthood is a period of the increasing variance in epigenetic aging. Both environmental and genetic factors contribute to this trend. The degree of environmental and genetic contributions can be partially explained by the design of epigenetic aging estimators.