DNA methylation profile is a quantitative measure of biological aging in children

Area of Science:

• Epigenetics
• Developmental Biology
• Computational Biology

Background:

• DNA methylation patterns can predict chronological age.
• Existing biological age prediction models primarily focus on adults, leaving a gap in pediatric research.
• Epigenetic aging in children is not well understood.

Purpose of the Study:

• To develop and validate a DNA methylation-based age prediction model specifically for children aged 9-212 months.
• To investigate factors influencing age acceleration in early life using the developed model.
• To assess the potential of epigenetic markers for early detection of health imbalances.

Main Methods:

• Utilized elastic net regression on 111 CpG sites from 716 blood samples across 11 datasets.
• Focused on CpG sites within genes relevant to development and aging.
• Validated the model's performance by correlating predicted methylation age with chronological age.

Main Results:

• Achieved a 98% correlation and a low error of 6.7 months between predicted and chronological age.
• Identified mid-childhood as a period of fastest aging, with higher acceleration in autistic children.
• Observed that early-life lead exposure accelerates aging in boys, but not girls.
• Found minimal impact of recombinant human growth hormone treatment on aging rate.

Conclusions:

• The child-specific DNA methylation model accurately predicts biological age and detects epigenetic changes.
• The model can identify early-life factors associated with accelerated aging, such as autism and lead exposure.
• This tool holds promise for early health assessments and future research into epigenetic interventions for age-related diseases.