This research aims to explore the relationship between behavior rhythms and the prognosis of chronic kidney disease (CKD) using data from the UK Biobank. While existing studies have shown that circadian rhythms and activity patterns are linked to various chronic diseases, the role of rest-activity rhythms, activity level, and timing in CKD progression remains underexplored.
The primary research questions include:
Are there specific abnormalities in rest-activity rhythms in patients with chronic kidney disease?
Is abnormal rest-activity behavior, activity level, or timing associated with faster progression of CKD or worse kidney function?
Can activity timing and behavioral rhythms serve as effective indicators for early risk assessment in CKD patients?
To address these questions, the study will employ the following approach:
Data Integration and Cleaning: Using UK Biobank data from questionnaires, clinical assessments, laboratory tests, and wearable device data, we will extract variables related to rest-activity rhythms and physical activity.
Statistical Analysis: Multivariate regression, clustering analysis, and survival analysis will be used to assess the independent association between behavior rhythms and CKD prognosis, controlling for confounding factors such as age, sex, lifestyle, and comorbidities.
Subgroup Analysis: Subgroup analysis will be conducted based on patient characteristics and disease severity to evaluate the influence of behavior rhythms on CKD progression across different groups.