AUTHOR=Xiong Yang , Zhong Qian , Zhang Yangchang , Liu Zhihong , Wang Xianding TITLE=The association between circadian syndrome and chronic kidney disease in an aging population: a 4-year follow-up study JOURNAL=Frontiers in Endocrinology VOLUME=15 YEAR=2024 URL=https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2024.1338110 DOI=10.3389/fendo.2024.1338110 ISSN=1664-2392 ABSTRACT=Introduction

Circadian syndrome (CircS) is proposed as a novel risk cluster based on reduced sleep duration, abdominal obesity, depression, hypertension, dyslipidemia and hyperglycemia. However, the association between CircS and chronic kidney disease (CKD) remains unclear. To investigate the cross-sectional and longitudinal association between CircS and CKD, this study was performed.

Methods

A national prospective cohort (China Health and Retirement Longitudinal Study, CHARLS) was used in this study. To define CKD, the estimated glomerular filtration rate (eGFR) was calculated based on the 2012 CKD-EPI creatinine-cystatin C equation. Participants with eGFR <60 mL.min-1/1.73/m2 were diagnosed with CKD. Multivariate binary logistic regression was used to assess the cross-sectional association between CircS and CKD. Subgroup and interactive analyses were performed to determine the interactive effects of covariates. In the sensitivity analysis, the obese population was excluded and another method for calculating the eGFR was used to verify the robustness of previous findings. In addition, participants without CKD at baseline were followed up for four years to investigate the longitudinal relationship between CircS and CKD.

Results

A total of 6355 participants were included in this study. In the full model, CircS was positively associated with CKD (OR = 1.28, 95% CI = 1.04-1.59, P < 0.05). As per one increase of CircS components, there was a 1.11-fold (95% CI = 1.04-1.18, P < 0.05) risk of prevalent CKD in the full model. A significant interactive effect of hyperuricemia in the CircS-CKD association (P for interaction < 0.01) was observed. Sensitivity analyses excluding the obese population and using the 2009 CKD-EPI creatinine equation to diagnose CKD supported the positive correlation between CircS and CKD. In the 2011-2015 follow-up cohort, the CircS group had a 2.18-fold risk of incident CKD (95% CIĀ = 1.33-3.58, P < 0.01) in the full model. The OR was 1.29 (95% CI = 1.10-1.51, P < 0.001) with per one increase of CircS components.

Conclusion

CircS is a risk factor for CKD and may serve as a predictor of CKD for early identification and intervention.