Does the epigenetic clock GrimAge predict mortality independent of genetic influences : an 18 year follow-up study in older female twin pairs
Föhr, T., Waller, K., Viljanen, A., Sanchez, R., Ollikainen, M., Rantanen, T., Kaprio, J., & Sillanpää, E. (2021). Does the epigenetic clock GrimAge predict mortality independent of genetic influences : an 18 year follow-up study in older female twin pairs. Clinical Epigenetics, 13, Article 128. https://doi.org/10.1186/s13148-021-01112-7
Published inClinical Epigenetics
DisciplineGerontologia ja kansanterveysLiikuntalääketiedeGerontologian tutkimuskeskusGerontology and Public HealthSports and Exercise MedicineGerontology Research Center
© 2021 the Authors
Background: Epigenetic clocks are based on DNA methylation (DNAm). It has been suggested that these clocks are useable markers of biological aging and premature mortality. Because genetic factors explain variations in both epigenetic aging and mortality, this association could also be explained by shared genetic factors. We investigated the infuence of genetic and lifestyle factors (smoking, alcohol consumption, physical activity, chronic diseases, body mass index) and education on the association of accelerated epigenetic aging with mortality using a longitudinal twin design. Utilizing a publicly available online tool, we calculated the epigenetic age using two epigenetic clocks, Horvath DNAmAge and DNAm GrimAge, in 413 Finnish twin sisters, aged 63–76 years, at the beginning of the 18-year mortality follow-up. Epigenetic age acceleration was calculated as the residuals from a linear regression model of epigenetic age estimated on chronological age (AAHorvath, AAGrimAge, respectively). Cox proportional hazard models were conducted for individuals and twin pairs. Results: The results of the individual-based analyses showed an increased mortality hazard ratio (HR) of 1.31 (CI95: 1.13–1.53) per one standard deviation (SD) increase in AAGrimAge. The results indicated no signifcant associations of AAHorvath with mortality. Pairwise mortality analyses showed an HR of 1.50 (CI95: 1.02–2.20) per 1 SD increase in AAGrimAge. However, after adjusting for smoking, the HR attenuated substantially and was statistically non-signifcant (1.29; CI95: 0.84–1.99). Similarly, in multivariable adjusted models the HR (1.42–1.49) was non-signifcant. In AAHorvath, the non-signifcant HRs were lower among monozygotic pairs in comparison to dizygotic pairs, while in AAGrimAge there were no systematic diferences by zygosity. Further, the pairwise analysis in quartiles showed that the increased within pair diference in AAGrimAge was associated with a higher all-cause mortality risk. Conclusions: In conclusion, the fndings suggest that DNAm GrimAge is a strong predictor of mortality independent of genetic infuences. Smoking, which is known to alter DNAm levels and is built into the DNAm GrimAge algorithm, attenuated the association between epigenetic aging and mortality risk. ...
ISSN Search the Publication Forum1868-7075
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Additional information about fundingThis work was supported by the Academy of Finland (grant 251723 to TR, grants 265240, 263278, 308248, 312073, 336823 to JK, 297908 and 251316 to MO), EC MC ITN Project EPITRAIN (JK and MO), University of Helsinki Research Funds (MO), the Sigrid Juselius Foundation (to JK and MO), the Juho Vainio Foundation (ES), and the Yrjö Jahnsson Foundation (ES).
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