Clinical nephrology · 2025
Longitudinal trajectory of metabolic components and outcomes in chronic kidney disease: The National Health Insurance Service-National Health Screening Cohort.
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Huh Hyuk, Lee Jae Sung, Park Eun Hee, Noh Minji, Koo Hoseok, Yoo Kyung Don
Аннотация
AIMS: Longitudinal trajectory analysis can provide important insights into the optimal levels of metabolic factors in chronic kidney disease (CKD). This study evaluated the association between longitudinal trajectories of metabolic disturbances and prognosis in CKD.
MATERIALS AND METHODS: We used data from the National Health Insurance Service-National Health Screening Cohort, which comprises data from 514,866 subjects randomly selected from the 2002 and 2003 health screening participants, who were aged between 40 and 79 years. Subjects were classified into trajectory groups using K-means clustering - an algorithm that assigns individual data points to groups according to similarity of the data - based on metabolic parameters, including blood pressure (BP), total cholesterol, triglycerides, low-density lipoprotein cholesterol (LDL-C), and body mass index (BMI). Subjects were classified into groups with similar trajectories based on the central value with the minimum distance. The optimal number of clusters was selected using the Calinski-Harabasz index. Outcomes were a decline in renal function and all-cause mortality.
RESULTS: A total of 24,094 CKD patients were included in the trajectory analysis. After clustering, BP, triglycerides, and LDL-C were divided into low and high categories, while BMI was classified into 6 categories according to the distribution of participants. Logistic regression analysis showed that a high systolic BP trajectory and underweight trajectory were associated with all-cause mortality, while high systolic BP, low diastolic BP, and high triglyceride trajectories were associated with a decline in renal function.
CONCLUSION: This study demonstrated the association between longitudinal trajectories of metabolic disturbances and the prognosis of CKD. Using trajectories of metabolic parameters could be helpful for predicting renal outcomes and mortality in CKD.
MATERIALS AND METHODS: We used data from the National Health Insurance Service-National Health Screening Cohort, which comprises data from 514,866 subjects randomly selected from the 2002 and 2003 health screening participants, who were aged between 40 and 79 years. Subjects were classified into trajectory groups using K-means clustering - an algorithm that assigns individual data points to groups according to similarity of the data - based on metabolic parameters, including blood pressure (BP), total cholesterol, triglycerides, low-density lipoprotein cholesterol (LDL-C), and body mass index (BMI). Subjects were classified into groups with similar trajectories based on the central value with the minimum distance. The optimal number of clusters was selected using the Calinski-Harabasz index. Outcomes were a decline in renal function and all-cause mortality.
RESULTS: A total of 24,094 CKD patients were included in the trajectory analysis. After clustering, BP, triglycerides, and LDL-C were divided into low and high categories, while BMI was classified into 6 categories according to the distribution of participants. Logistic regression analysis showed that a high systolic BP trajectory and underweight trajectory were associated with all-cause mortality, while high systolic BP, low diastolic BP, and high triglyceride trajectories were associated with a decline in renal function.
CONCLUSION: This study demonstrated the association between longitudinal trajectories of metabolic disturbances and the prognosis of CKD. Using trajectories of metabolic parameters could be helpful for predicting renal outcomes and mortality in CKD.