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 a

SUBMITTER: Martinez-Enguita D 

PROVIDER: S-EPMC11799293 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature