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Research Article

Metabolic dysfunction, rather than obesity, is a risk factor for chronic kidney disease in Chinese population

, , , &
Article: 2335158 | Received 03 Aug 2023, Accepted 21 Mar 2024, Published online: 10 Apr 2024
 

Abstract

Background

Metabolic dysfunction and obesity are closely related to chronic kidney disease (CKD). However, studies on the relationship between various metabolic syndrome-body mass index (MetS-BMI) phenotypes and the risk of CKD in the Chinese population have not yet been explored.

Materials and methods

Data from the China Health and Retirement Longitudinal Study (CHARLS) 2015 were analyzed in this study. This study enrolled 12,054 participants. Participants were divided into six distinct groups according to their MetS-BMI status. Across the different MetS-BMI groups, the odd ratios (ORs) for CKD were determined using multivariable logistic regression models.

Results

The prevalence of CKD was higher in metabolically unhealthy groups than in the corresponding healthy groups. Moreover, the fully adjusted model showed that all metabolically unhealthy individuals had an increased risk of developing CKD compared to the metabolically healthy normal weight group (OR = 1.62, p = 0.002 for the metabolically unhealthy normal weight group; OR = 1.55, p < 0.001 for the metabolically unhealthy overweight group; and OR = 1.77, p < 0.001 for the metabolically unhealthy obesity group.

Conclusions

This study is the first to evaluate the relationship between the MetS-BMI phenotype and renal prognosis in the Chinese population. Individuals with normal weights are at different risk of developing CKD depending on their different metabolic phenotypes.

Authors’ contributions

S.Y. performed the data analyses and wrote the manuscript; S.Z. and J.L. analyzed all data; Y.L. supervised the project; S.Y. wrote and edited the manuscript; and G.Y. revised the manuscript.

Disclosure statement

None of the authors declares competing financial interests.

Additional information

Funding

This study was supported by the National Natural Science Foundation of China (Grant No. 82100824) and the Chinese Postdoctoral Science Foundation (Grant No. 2021M700633).