Integrative multimodal analysis of lifespan development

Project Summary

Late-life conditions such as dementia present an ongoing major challenge for the healthcare system. Current successful therapeutic interventions for dementia show greater benefits if given earlier in the course of disease progression, therefore, it is important to identify dementia risk early. In this project, we aim to use predictive modelling and machine learning to better understand lifespan development and aging. We will use metabolomic, genomic, cognitive, and clinical measures, to identify patterns and predictors of age-related changes. We will use machine learning techniques to identify patterns in the data, which will help us to understand who may be at a greater risk of cognitive decline and developing dementia. Our work will help uncover the relationship between metabolomics, genomics, and cognitive phenotypes as early markers of dementia risk, yielding new insights into pathways underlying heterogeneity in dementia and informing strategies for early detection and prevention.