Cardiovascular-Kidney-Metabolic (CKM) syndrome is defined by the interaction between metabolic risk factors, chronic kidney disease, and the cardiovascular system, with the result that incidence and mortality rates are influenced.
The objective of this study is to develop an integrated CKM disease prediction model incorporating social, behavioural and genetic factors. The UK Biobank cohort will facilitate analysis of the epidemiological characteristics of CKM disease, the staging of CKM syndrome, and its distribution/prognosis.
The identification of multidimensional risk factors is imperative, encompassing socioeconomic status, lifestyle, psychosocial factors, biomarkers (metabolic, inflammatory, renal, cardiovascular), and genetic factors (genome-wide association studies, polygenic risk scores).
A comprehensive predictive model will be constructed using machine learning algorithms, and its discriminatory ability, calibration ability, and reclassification ability will be validated. Interaction analyses will explore the impact of different dimensional factors on CKM disease risk.
The objective of this study is to establish a CKM disease prediction model, with the aim of providing clinical evidence for the early identification, risk stratification and prevention of the disease.
