Predicting Brain Age Based on Neuroimaging and DNA Methylation via A Multiomics Attention-Based Variational Autoencoder

Brain age (BA) is recognized as a significant biomarker for health, closely associated with brain aging, and has been proposed to correlate with the progression of neurodegenerative diseases. Previous studies on brain age prediction predominantly relied on single omics data, limiting the integration of cooperative information from multiple omics data. Additionally, existing brain age prediction methods are susceptible to intermediate features since not all these features are related to brain age. To address these limitations, the study introduces a multiomics attention-based VAE method to predict brain age by integrating neuroimaging data and DNA methylation (DNAm) data, aiming to identify biologically meaningful features truly associated with brain aging. Experimental results show that our method predicts brain age with the MAE of 2.08 years, outperforming the state-of-the-art methods under the same experimental conditions. Furthermore, we estimated the brain age gap in patients with Mild Cognitive Impairment (MCI) and Alzheimer's Disease (AD). The results of both MCI and AD patients exhibited a larger brain age gap compared to the Cognitively Normal (CN) group, indicating the model's discriminative capacity across different diagnostic groups. These findings can assist in the early diagnosis of AD and the formulation of early treatment strategies.