Modeling Alzheimer's disease with human multi-omics profiles: Promise to personalized medicine

Alzheimer's disease (AD) presents considerable heterogeneity in disease risk and outcomes, posing a major challenge for effective therapeutic development. Recent advances in multi-omics profiling are revolutionizing our understanding of the complex AD pathogenesis. Leveraging long-read sequencing and large-scale multi-ancestry genetic studies, researchers are unraveling the complex polygenic architecture of AD. Additionally, advances in multi-omics techniques unlock the power of molecular profiling of human biological samples, particularly spatial omics of human postmortem brain tissues and molecular profiling of biofluids, to understand the complex AD pathogenesis. Importantly, artificial intelligence (AI)-driven approaches integrate multimodal data to identify molecular features associated with disease risk and endophenotypes, fostering sophisticated models for disease risk predictions and molecular-based patient stratification. Coupled with human-induced pluripotent stem cell-derived brain cell models for validation and mechanistic studies, this comprehensive AI-integrated, multi-omics study approach promises to delineate both shared AD pathological processes and individual variations, paving the way for personalized medicine.