Research Questions:
1.What genetic, metabolic, and environmental factors contribute to the onset, progression, and subtype differentiation of otolaryngological diseases?
2.Can integrated multi-modal datafrom UK Biobank enhance the accuracy of early diagnosis and prognosis prediction for otolaryngological diseases?
3.What molecular pathways and inter-system interactions underlie the pathogenesis of otolaryngological diseases?
4.Which pre-diagnostic or baseline factors are robust predictors of long-term prognosis in otolaryngological diseases?
Objectives:
1.Identify demographic, genetic, and lifestyle/environmental factors linked to the incidence, severity, and subtype classification of otolaryngological diseases.
2.Develop and validate machine learning algorithms using UK Biobank’s clinical data , genetic data, imaging data, and serum biomarker data. These models will be used to predict: Disease risk ;Treatment response ;Long-term prognosis .
3.Uncover disease-associated molecular pathways and microbiome interactions via multi-omics integration. Explore the causal relationship between metabolic abnormalities and the occurrence or prognosis of otolaryngological diseases.
4.Validate robust prognostic indicators for otolaryngological diseases using UK Biobank’s long-term follow-up data.
Scientific Rationale:
Otolaryngological diseases are highly prevalent worldwide, severely impacting quality of life , with complex etiologies involving genetic, environmental, and metabolic factors-but their mechanisms and prognostic drivers remain poorly understood, due to current research limitations such as small sample sizes , reliance on single-modal data, short follow-up, and lack of integration between onset and prognosis research; UK Biobank addresses these gaps, especially for prognosis studies, via unique advantages: up to 15+ years of follow-up , time-dependent data, large sample sizes , and multi-modal integration.
