The association between the cardiac metabolic index and rapid kidney function decline and CKD in individuals with different glucose metabolism statuses: results from the China health and retirement longitudinal study

被引：0
作者：
Wei-Zhen Tang [1 ]
Qin-Yu Cai [2 ]
Tai-Hang Liu [1 ]
Tao-Ting Li [2 ]
Gao-hui Zhu [2 ]
Jia-cheng Li [3 ]
Kang-Jin Huang [1 ]
Hong-Yu Xu [1 ]
He-Zhe Hua [2 ]
Rong Li [2 ]
机构：
[1] Department of Endocrinology, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, Children’s Hospital of Chongqing Medical Univers
[2] Department of Bioinformatics, School of Basic Medical Sciences, Chongqing Medical University, Chongqing
[3] Department of human resources, The First Affiliated Hospital of Chongqing Medical University, Chongqing
[4] Department of Endocrinology, Children’s Hospital of Chongqing Medical University, 136 Zhongshan Er Rd, Yuzhong District, Chongqing
来源：
Lipids in Health and Disease | / 24卷 / 1期
关键词：
Cardiometabolic index; CHARLS; CKD; Glucose metabolism; Rapid kidney function decline;
D O I：
10.1186/s12944-025-02572-z
中图分类号：
学科分类号：
摘要：
Background: The Cardiometabolic Index (CMI) is a new measure that combines fat distribution and lipid profiles. However, its relationship with rapid decline in renal function and the chronic kidney disease (CKD), especially in individuals with varying glucose metabolism, is still unclear. Method: This study included 3,485 participants aged 45 and above from the China Longitudinal Study on Health and Retirement (CHARLS), with baseline assessments in 2011–2012 and follow-ups in 2015 and 2018. Participants were grouped into four categories (Q1-Q4) based on baseline CMI levels. The primary outcome was rapid decline in renal function, with CKD events also observed. Multivariable logistic models and restricted cubic spline (RCS) analysis were used to explore the relationship between baseline CMI levels and the risk of kidney disease in individuals with different glucose metabolism statuses. Nine machine learning models were developed using baseline CMI to validate its predictive ability for kidney disease risk. Finally, mediation causal analysis was conducted to examine whether the development of diabetes in the non-diabetic population serves as an important mediator in the relationship between CMI and kidney disease. Results: During the follow-up period, a total of 173 participants (4.96%) experienced rapid decline in renal function, and 87 participants (2.50%) developed CKD. With increasing baseline CMI levels, the risk of rapid decline in renal function and CKD significantly increased. Among the various machine learning models for predicting kidney disease, logistic regression performed excellently, with AUCs exceeding 0.6, indicating the strong predictive ability of baseline CMI. For the primary outcome, multivariable logistic regression analysis showed that, in all participants, as well as in the normal glucose regulation (NGR) group and the prediabetes (Pre-DM) group, the incidence of rapid decline in renal function significantly increased across different CMI groups (P < 0.05), with trend RR values of 1.285(1.076,1.536), 1.308 (1.015, 1.685) and 1.566 (1.207, 2.031), respectively. However, this association was not observed in patients with diabetes (P for trend > 0.05). RCS analysis further indicated that higher baseline CMI levels were associated with a greater risk of rapid decline in renal function in all participants and in the non-diabetic population. A similar trend was observed for CKD. Finally, mediation causal analysis showed that the development of new-onset diabetes in the non-diabetic population may not be an important mediator in the relationship between CMI and kidney disease. Conclusion: Higher baseline CMI levels were significantly linked to rapid decline in renal function and CKD in middle-aged and elderly individuals, with the relationship varying by glucose metabolism status. CMI may serve as a useful indicator for predicting kidney disease risk, especially in non-diabetic population. © The Author(s) 2025.
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