Precise and interpretable neural networks reveal epigenetic signatures of aging across youth in health and disease.

Abstract

Introduction: DNA methylation (DNAm) age clocks are powerful tools for measuring biological age, providing insights into aging risks and outcomes beyond chronological age. While traditional models are effective, their interpretability is limited by their dependence on small and potentially stochastic sets of CpG sites. Here, we propose that the reliability of DNAm age clocks should stem from their capacity to detect comprehensive and targeted aging signatures.

Methods: We compiled publicly available DNAm whole-blood samples (n = 17,726) comprising the entire human lifespan (0-112 years). We used a pre-trained network-coherent autoencoder (NCAE) to compress DNAm data into embeddings, with which we trained interpretable neural network epigenetic clocks. We then retrieved their age-specific epigenetic signatures of aging and examined their functional enrichments in age-associated biological processes.

Results: We introduce NCAE-CombClock, a novel highly precise (R² = 0.978, mean absolute error = 1.96 years) deep neural network age clock integrating data-driven DNAm embeddings and established CpG age markers. Additionally, we developed a suite of interpretable NCAE-Age neural network classifiers tailored for adolescence and young adulthood. These clocks can accurately classify individuals at critical developmental ages in youth (AUROC = 0.953, 0.972, and 0.927, for 15, 18, and 21 years) and capture fine-grained, single-year DNAm signatures of aging that are enriched in biological processes associated with anatomic and neuronal development, immunoregulation, and metabolism. We showcased the practical applicability of this approach by identifying candidate mechanisms underlying the altered pace of aging observed in pediatric Crohn's disease.

Discussion: In this study, we present a deep neural network epigenetic clock, named NCAE-CombClock, that improves age prediction accuracy in large datasets, and a suite of explainable neural network clocks for robust age classification across youth. Our models offer broad applications in personalized medicine and aging research, providing a valuable resource for interpreting aging trajectories in health and disease.