A new blood based epigenetic age predictor for adolescents and young adults

Abstract

Children have special rights for protection compared to adults in our society. However, more than 1/4 of children globally have no documentation of their date of birth. Hence, there is a pressing need to develop biological methods for chronological age prediction, robust to differences in genetics, psychosocial events and physical living conditions. At present, DNA methylation is the most promising biological biomarker applied for age assessment. The human genome contains around 28 million DNA methylation sites, many of which change with age. Several epigenetic clocks accurately predict chronological age using methylation levels at age associated GpG-sites. However, variation in DNA methylation increases with age, and there is no epigenetic clock specifically designed for adolescents and young adults. Here we present a novel age Predictor for Adolescents and Young Adults (PAYA), using 267 CpG methylation sites to assess the chronological age of adolescents and young adults. We compared different preprocessing approaches and investigated the effect on prediction performance of the epigenetic clock. We evaluated performance using an independent validation data set consisting of 18-year-old individuals, where we obtained a median absolute deviation of just below 0.7 years. This tool may be helpful in age assessment of adolescents and young adults. However, there is a need to investigate the robustness of the age predictor across geographical and disease populations as well as environmental effects.

Epigenetic age acceleration is associated with speed of pubertal growth but not age of pubertal onset

“…This may be due to the fact that EPOCH estimates of EAA were derived from blood, and the Hannum calculator (the basis of extrinsic EAA) was trained using blood, whereas the Horvath calculator (the basis of intrinsic EAA) was trained using a range of tissue types. Which epigenetic calculator has the strongest associations with developmental milestones in children is not established, and EAA clocks developed in youth may perform better than those including older individuals 38 . However, despite these differences in associations, and the fact that these clocks share only 6 CpG sites of the 71 CpG sites in the Hannum clock 39 and the 353 CpG sites in the Horvath clock 40 , 41 , intrinsic and extrinsic EAA estimates are highly correlated (r = 0.76) with each other 42 .…”

MRI segmentation of tooth tissue in age prediction of sub-adults — a new method for combining data from the 1st, 2nd, and 3rd molars

“…Combining our method with other physical traits such as skeletal development and DNA methylation is an approach recommended in the legal context [ 5 , 11 , 12 ]. A combination might reduce the uncertainty stemming from biological variation and enhance the method’s resilience against gaps in teeth data caused by missing teeth.…”