Age-Associated Genetic and Environmental Contributions to Epigenetic Aging Across Adolescence and Emerging Adulthood

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Age-Associated Genetic and Environmental Contributions to Epigenetic Aging Across Adolescence and Emerging Adulthood

Authors

Kuznetsov, D. V.; Liu, Y.; Schowe, A. M.; Czamara, D.; Instinske, J.; Pahnke, C. K. L.; Noethen, M. M.; Spinath, F. M.; Binder, E. B.; Diewald, M.; Forstner, A. J.; Kandler, C.; Moenkediek, B.

Abstract

Background: Epigenetic aging estimators commonly track chronological and biological aging, quantifying its accumulation (i.e., epigenetic age acceleration) or its speed (i.e., epigenetic aging pace). These estimates reflect a combination of inherent biological programming and the impact of environmental factors, which are suggested to vary at different stages of life. 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 remains uncertain. This study delves into understanding the age-associated genetic and environmental contributions to differences both between and within individuals across these developmental stages. For this purpose, we analyzed four differently developed epigenetic aging estimators, namely, Horvath Acceleration, GrimAge Acceleration, PedBE Acceleration, and DunedinPACE, which were collected from 976 twins aged 8 to 31 years (M=16.0, SD=6.05) measured twice, two years apart. Results: Approximately half (33-76%) of the between-individual differences in epigenetic aging and most (70-99%) of the variance in within-individual changes across the two years were attributable to unique environmental factors. The contribution of these factors to the variance of epigenetic estimators trained on chronological age notably increased with time (Horvath Acceleration: from 80% to 90%; PedBE Acceleration: from 70% to 99%). Genetic contributions were more pronounced in the between-individual differences than in the variance of within-individual changes. For epigenetic aging estimators trained on chronological age, both additive genetic factors (8%-39%) and shared environmental influences (13%-49%) contributed to the variance. For epigenetic estimators trained on biological indicators, either additive (GrimAge Acceleration) or additive and non-additive genetic (DunedinPACE) factors were relevant. The variance of three estimators, initially developed in adult samples, demonstrated increasing contributions of genetic factors to the variance across adolescence and emerging adulthood (Horvath Acceleration: from 18% to 39%; GrimAge Acceleration: from 24% to 43%; DunedinPACE: from 42% to 57%). Conclusions: Our findings suggest that increasing between-individual differences in epigenetic aging during adolescence and emerging adulthood are the result of both unique life experiences and genetic underpinnings, whereas variance in within-individual changes is primarily attributable to unique life experiences.

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