Grant: Project Grant

Biological clocks constructed from imaging and clinical phenotypes advances our knowledge of human aging and disease. As one of the most important aspects of aging, biological brain clocks (brain age gap or BAG) compares a person's chronological age with their "brain age," estimated by AI from MRI scans and measure changes in brain structure and function. Accelerated brain aging is associated with increased disease risk of the brain. Factors like genetics, lifestyle, and social environment influence how quickly the brain ages. However, current brain clocks mostly rely on imaging and do not capture the full biology of aging. There are also unmet needs in further improving BAG for clinical utility. Circulating proteomics and metabolomics reflect overall body health and play key roles in brain function, they provide additional molecular layers to enrich these biological clocks. These enriched brain clocks could improve early disease detection, guide treatment choices, and even help identify new drug targets. In this project, we will leverage multi-omics and neuroimaging resources from multi-ancestry biobanks and machine learning approaches to develop multimodal BAGs. We will study the genetic factors that influence brain aging and identify druggable targets, and test how well the multimodal BAGs predict long-term cognitive decline and treatment response in multiple sclerosis. By creating next-generation brain clocks that bring together imaging, genetics, and molecular biology, this project will provide powerful new tools for predicting brain health, slowing cognitive decline, and accelerating the development of personalized therapies.