The pathophysiological processes of chronic diseases, such as diabetes and hypertension, are highly complex and characterized by significant dynamic fluctuations. However, traditional clinical monitoring is often constrained to “snapshot” invasive procedures-such as fingerstick blood sampling or arterial catheterization-which carry high infection risks and suffer from low patient compliance. Leveraging the extensive cohort and multidimensional data of the UK Biobank, this study aims to investigate the associations between alternative physiological indicators and chronic diseases to determine their potential as independent predictors.
To achieve this, the study will:
1!Apply causal inference methods to identify factors with causal relationships to chronic disease risk from routinely collected physiological parameters;
2!Analyze longitudinal dynamic features to address the limitations of traditional “snapshot” monitoring in capturing hemodynamic instability;
3!Develop precision risk prediction models that integrate multi-omics and longitudinal clinical data.
By utilizing the UK Biobank to reveal the dynamic evolutionary mechanisms of chronic diseases, this research seeks to overcome the constraints of traditional invasive monitoring. The development of non-invasive prediction models will significantly alleviate the monitoring burden while providing core technical support for personalized precision prevention.