MOTIVATION: Neurodegenerative disorders influence millions of people worldwide, and uncovering the pathogenesis is of urgent need. Many efforts have been made to detect or predict neurodegenerative disorders, while exploring the pathogenesis has been ignored from a systemic perspective. RESULTS: To handle this issue, we propose a novel and powerful method, referred to as Pathogenesis-aware Mutual-Assistance Classification and Regression Optimization (Pa-MACRO). First, Pa-MACRO incorporates a mutual-assistance bidirectional mapping technique with a joint-embedding fine-grained interpretability module. This can extract the intrinsic factors and their interactions of multimodal pathogenesis. Second, our method can simultaneously classify an at-risk individual and predict the severity triggered by neurodegenerative disorders. Furthermore, to address the small sample size issue and the high-dimensional issue, we meticulously incorporate a semi-supervised cooperative learning method to integrate unlabeled data and extend it to a chromosome-wide setting in the spirit of divide-and-conquer. The Alzheimer's Disease Neuroimaging Initiative (ADNI) database was used to evaluate Pa-MACRO. Without bells and whistles, Pa-MACRO establishes new state-of-the-art results in various settings while maintaining superior interpretability, verifying its power and versatility in revealing the pathogenesis of neurodegenerative disorders. AVAILABILITY AND IMPLEMENTATION: The software is publicly available at https://github.com/ZJ-Techie/Pa-MACRO.

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.