Research Questions:
What novel blood biomarkers can be identified for aging and aging-associated diseases?
How do these biomarkers correlate with multi-system aging mechanisms?
How can these biomarkers improve diagnosis, prognosis, and therapy for aging-related diseases?
Objectives:
Identify novel biomarkers linked to aging-related diseases.
Use pre-existing AI models to prioritize biomarkers.
Validate findings using UK Biobank datasets (biomarkers, phenotypic profiles, genomic data).
Scientific Rationale:
Aging drives systemic changes that increase chronic diseases like fibrosis, cardio-metabolic disorders, cancers and neurodegeneration. Blood biomarkers are key for early disease detection and personalized care. By leveraging UK Biobank data and AI tools, we aim to identify novel biomarkers that complement existing care frameworks. This innovative approach will expand understanding of aging mechanisms and improve health outcomes.
Methodology:
Aggregate mutli-omics datasets and utilise Insilico PandaOmics platform for biomarker prioritization.
Validate findings using multi-level UK Biobank data:
Biomarker panels and phenotypic data for deep insights into multi-system aging processes.
Hypothesis-based validation on top results:
Analysis of PPI graphs, pathway enrichment, transcription factor interactions, and organ-tissue crosstalk mechanisms.
Notes on AI Compliance: Pretrained AI models will evaluate biomarkers. UK Biobank participant-level data will not train AI models or be integrated into generative AI systems.
Expected Outcomes:
Discover biomarkers relevant to aging mechanisms and associated diseases.
Return results to UK Biobank, including data dictionaries and methodologies, per their guidelines.
Dissemination Note: This research is for internal use. All derived data will be submitted to UK Biobank per their guidelines. Findings will remain confidential unless publication is planned, whereby UK Biobank will be notified.
