Neurological disorders such as Alzheimer’s disease, Parkinson’s disease, cerebral small vessel disease (CSVD) and shift-work related disorders represent a significant and growing global health challenge. These conditions arise from complex interactions between genetic susceptibility, molecular biomarkers, and structural/functional brain alterations. Emerging research demonstrates that specific genetic variants and biofluid profiles influence key neuroimaging phenotypes including cortical thickness, white matter integrity, and functional connectivity patterns. However, the precise biological mechanisms underlying these relationships remain poorly understood, limiting our ability to develop targeted interventions.
This study will address three key objectives: First, we will identify genetic variants and biofluid biomarkers (blood metabolites, cerebrospinal fluid proteins, gut microbiome profiles from stool samples) associated with distinct neuroimaging signatures using the rich multimodal data from UK Biobank. Second, we will integrate multi-omics data (genomics, proteomics, metabolomics) with neuroimaging to elucidate mechanistic pathways, with particular focus on synaptic plasticity, neuroinflammation, and neural network dysfunction. Third, we will develop machine learning models incorporating molecular and imaging biomarkers to enable early risk stratification and personalized prediction of disease progression.
Our multi-omics approach bridges the gap between molecular-level genetic risk, peripheral biomarker profiles and macroscopic brain phenotypes, offering unprecedented opportunities to discover novel biomarkers and therapeutic targets. This research will advance precision medicine approaches for neurological disorders by providing a comprehensive understanding of disease mechanisms and enabling more accurate diagnosis and treatment strategies.
