Multi-omics integration of transcriptomics and metabolomics with machine learning uncovers novel risk factors for Alzheimer's disease.

BackgroundAlzheimer's disease (AD) is a neurodegenerative disorder characterized by cognitive decline, memory impairment, and functional deterioration. Its complex pathogenesis involves amyloid plaques, tau tangles, neuroinflammation, synaptic dysfunction, and interacting genetic, environmental, and lifestyle factors. Transcriptomic and metabolomic studies have revealed molecular disruptions relevant to AD, supporting integrative approaches for biomarker discovery.ObjectiveTo integrate genetically imputed whole-blood transcriptomics and measured plasma metabolomics to predict cognitive performance, assessed using the PACC3 score, and identify influential genes and metabolites associated with cognition.MethodsA machine learning model integrated transcriptomic and metabolomic data from 1046 participants in the Wisconsin Registry for Alzheimer's Prevention (WRAP). Performance was evaluated in a WRAP holdout test set and independently validated in 85 participants from the Wisconsin Alzheimer's Disease Research Center (ADRC). Feature importance was used to identify molecular contributors to prediction.ResultsThe model achieved a normalized root mean squared error of 0.707 and an R