Background:
Dementia and Alzheimer’s disease (AD) is a major global health challenge, with enormous personal and societal costs. Predicting who will experience cognitive decline and when, remains a key unmet need that would enable prevention and earlier treatments. Our recent work (1, 2) developed a unique predictive assay that integrates stem cell–derived hippocampal neurogenesis readouts with multimodal patient data—including lifestyle, education, nutrition, and metabolomics—to forecast AD progression and dementia onset. While highly accurate, the current model depends on complex laboratory experiments, limiting its scalability and use in large population cohorts.
Novelty and Importance:
This PhD project will bridge the gap between laboratory-based models and real-world clinical application by translating neurogenesis-associated signatures into proxy biomarkers measurable in large-scale databases such as the UK Biobank. These proxies—such as already collected circulating inflammatory factors, metabolomic patterns, and polygenic risk scores—will serve as indirect indicators of hippocampal neurogenesis, enabling predictive modelling without laboratory assays. The project will harness advanced multimodal machine learning and data harmonisation techniques to integrate biological, genetic, and lifestyle data. This research directly supports DRIVE-Health’s Theme 2: Multimodal Patient Data Streams, by leveraging heterogeneous data to drive precision health innovations and scalable risk prediction tools.
Aims and Objectives:
Over four years, the project will:
1. Harmonise existing datasets combining neurogenesis, cognitive, and lifestyle measures.
2. Identify and validate proxy biomarkers of hippocampal neurogenesis available in the UK Biobank.
3. Develop and test predictive models of AD progression using multimodal data streams.
4. Assess generality of these models across diverse populations, eventually delivering an open-access, scalable tool for early detection and prevention of AD.
References:
1. Maruszak et al., Brain, 2023 DOI:10.1093/brain/awac472
2. Du Preez et al., Alzheimer’s & Dementia, 2021, DOI:10.1002/alz.12428