Dynamic changes in metabolic syndrome components and chronic kidney disease risk: a population-based prospective cohort study.

Abstract

Objective: To investigate the relationships between dynamic changes in metabolic syndrome (MetS) components and chronic kidney disease (CKD) risk.

Methods: Data from the UK Biobank, including baseline assessments from 2006 to 2010, repeat assessments in 2012-2013, and linked national health records, were analyzed. MetS components consisted of abdominal obesity, elevated blood pressure (BP), fasting blood glucose (FBG), serum uric acid (SUA), and lipid abnormalities. The Kaplan-Meier method and log-rank test were used to analyze CKD incidence and group differences. Cox regression models assessed the association between dynamic changes in MetS components and CKD risk.

Results: The study enrolled 455,060 participants (45.7% male, 18.4% aged 65 years or older) with a median follow-up of 12.68 years. Those with MetS had a significantly higher 10-year CKD cumulative incidence probability of CKD than those without MetS (4.14% VS 1.14%). Multivariate analysis showed all baseline metabolic abnormalities were linked to CKD risk with HRs from 1.40(1.35-1.45) to 1.85 (1.78-1.92), and MetS strongly associated with CKD (HR: 2.31). CKD risk rose with more MetS components and progression stages. Notably, with FBG being the exception, the four MetS components that shifted from normal at baseline to abnormal at follow - up were associated with elevated CKD risk, with HRs (95% CI) ranging from 1.21 (1.00-1.48) to 1.73 (1.34-2.24). Participants with high baseline SUA, even if it normalized at follow - up, still faced a 1.30 - fold higher CKD risk (95% CI: 1.25-1.35), distinct from other components. For those developing one and ≥ 2 new MetS components at follow - up, the CKD risk HRs (95% CI) were 1.49 (1.00-2.35) and 2.26 (1.21-4.24) respectively.

Conclusion: MetS and its component changes are significantly associated with CKD risk, in a dose - response pattern. Incorporating SUA into MetS assessments enhances risk identification, especially noting females' higher susceptibility to elevated SUA. Dynamic monitoring of MetS components is crucial for assessing and predicting CKD risk.

Clinical trial number: Not applicable.