Comprehensive Clinical Assessment and Management of Kidney Function Decline in Older Adults and Development of an Early Warning System
This completed observational cohort study evaluates a comprehensive set of clinical and molecular markers of kidney function in two established cohorts of Chinese adults aged 60 years or older. The study assesses markers reflecting glomerular filtration, kidney tubular transport, and kidney endocrine function and evaluates their associations with kidney failure, cardiovascular and cerebrovascular events, and all-cause mortality. It also compares approaches to estimating glomerular filtration rate and develops and internally validates an early-warning model for kidney function decline. Existing baseline and follow-up data and stored serum and urine specimens are used. No intervention or additional study-specific clinical procedure is administered.
The study includes 5,588 participants, comprising 1,788 participants from the Rugao Longevity and Ageing Study and 3,800 participants from the Shanghai Older-Adult Cohort. The study uses existing demographic, lifestyle, clinical, and laboratory data, longitudinal follow-up information, and measurements obtained from stored serum and urine specimens. No intervention is assigned, and no additional study-specific examination or biospecimen collection is performed. Markers reflecting glomerular filtration, kidney tubular transport, and kidney endocrine function are evaluated in relation to incident kidney failure, cardiovascular and cerebrovascular events, and all-cause mortality. Approaches to estimating glomerular filtration rate and combinations of kidney function-related biomarkers are compared for prognostic assessment. Multivariable linear, logistic, and Cox proportional hazards regression models are used, as appropriate, to evaluate associations while accounting for potential confounding factors. For prediction modeling, the analytic sample is divided in a 7:3 ratio into model-development and internal-validation sets. Statistical learning and machine-learning methods, including random forests, decision trees, support vector machines, and neural-network approaches, are evaluated. Cross-validation is used during model development and model selection. The study also supports the development of a secure, access-controlled data-sharing and analytics platform.
Inclusion Criteria: 1. Enrollment in the Rugao Longevity and Ageing Study or the Shanghai Older-Adult Cohort. 2. Age 60 years or older, of either sex, with a baseline estimated glomerular filtration rate greater than 60 mL/min/1.73 m². 3. Availability of the baseline data required for the study, including blood pressure, body weight, body mass index, smoking and alcohol use, surgical history, family history, complete blood count, liver function, electrolytes, kidney function, serum cystatin C, blood glucose, urinalysis, and urinary albumin-to-creatinine ratio. 4. Availability of follow-up information sufficient to ascertain the prespecified clinical outcomes. 5. Clinically stable condition and adequate ability to understand and communicate at enrollment in the parent cohort. 6. Written informed consent for participation and the permitted research use of clinical data and stored biospecimens.
Exclusion Criteria: 1. Missing essential baseline or follow-up data required for the relevant analysis. 2. Unavailability of stored blood or urine specimens required for the planned biomarker analysis. 3. Missing information on lifestyle, health-related behaviors, or social support required for the relevant analysis.
[{"measure":"Incident End-Stage Kidney Disease","timeFrame":"From cohort baseline through the end of available follow-up, up to 10 years"},{"measure":"Incident Cardiovascular or Cerebrovascular Event","timeFrame":"From cohort baseline through the end of available follow-up, up to 10 years"},{"measure":"All-Cause Mortality","timeFrame":"From cohort baseline through the end of available follow-up, up to 10 years"}]