Research Paper Volume 11, Issue 22 pp 10031—10051
DNA methylation profile is a quantitative measure of biological aging in children
- 1 Institute of Pediatrics, Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou, Guangdong, China
- 2 Department of Medical Genetics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, Guangdong, China
- 3 Guangdong Technology and Engineering Research Center for Molecular Diagnostics of Human Genetic Diseases, Guangzhou, Guangdong, China
- 4 Guangdong Province Key Laboratory of Psychiatric Disorders, Guangzhou, Guangdong, China
- 5 Department of Cardiovascular Surgery, Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou, Guangdong, China
- 6 The Guangdong Early Childhood Development Applied Engineering and Technology Research Center, Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangdong, China
received: April 12, 2019 ; accepted: October 26, 2019 ; published: November 22, 2019 ;https://doi.org/10.18632/aging.102399
How to Cite
Copyright © 2019 Wu et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
DNA methylation changes within the genome can be used to predict human age. However, the existing biological age prediction models based on DNA methylation are predominantly adult-oriented. We established a methylation-based age prediction model for children (9-212 months old) using data from 716 blood samples in 11 DNA methylation datasets. Our elastic net model includes 111 CpG sites, mostly in genes associated with development and aging. The model performed well and exhibited high precision, yielding a 98% correlation between the DNA methylation age and the chronological age, with an error of only 6.7 months. When we used the model to assess age acceleration in children based on their methylation data, we observed the following: first, the aging rate appears to be fastest in mid-childhood, and this acceleration is more pronounced in autistic children; second, lead exposure early in life increases the aging rate in boys, but not in girls; third, short-term recombinant human growth hormone treatment has little effect on the aging rate of children. Our child-specific methylation-based age prediction model can effectively detect epigenetic changes and health imbalances early in life. This may thus be a useful model for future studies of epigenetic interventions for age-related diseases.