Visceral fat and its dynamic change are associated with renal damage: Evidence from two cohorts

ABSTRACT

Background and Aims

To evaluate the association of Chinese visceral adiposity index (CVAI) and its dynamic trends with risk of renal damage, and to compare its prediction performance with that of other obesity indices.

Methods and Results

A community-based population with 23 905 participants from Shantou city was included in the cross-sectional analysis. A total of 9,778 individuals from two separated cohort were included in the longitudinal portion. Five patterns of CVAI change were predefined (low-stable, decreasing, moderate, increasing, and persistent-high). Logistic and Cox regressions were used to evaluate the association between CVAI and renal damage. We explored potential mechanisms using the mediating effect method, and the prediction performance was determined by receiver operating characteristic curve analysis. Results from both cross-sectional and longitudinal data revealed a positive and linear association between CVAI and risk of renal damage. Pooled analysis of the two cohorts showed that per unit increase in Z score of CVAI induced 18% increased risk of renal damage (P = .008). Longitudinal trends of CVAI were also associated with renal damage, and the moderate, increasing, and persistent-high patterns showing a higher risk. Blood pressure and glucose had a mediating effect on renal damage induced by CVAI. Among several obesity indices, CVAI was the optimal for predicting renal damage.

Conclusion

A higher level of immediate CVAI and longitudinal increasing and persistent-high patterns of CVAI were independently associated with increased risk of renal damage. Monitoring immediate level and long-term trend of CVAI may contribute to the prevention of renal damage.

KEYWORDS:

• Chinese visceral adiposity index
• renal damage
• longitudinal trends
• cohort study
• risk factor

Disclosure statement

No potential conflict of interest was reported by the author(s).

Author contributions

Conceptualization, MYL, YQC and XRT; Methodology, MYL and YQC; Software, MYL and SWW; Validation, XLD; Formal analysis, MYL; Investigation, MYL, SWW and XLD; Data Curation, YQC and XRT; Writing-Original Draft Preparation, MYL; Writing-Review & Editing, MYL, SWW, XLD, YQC and XRT; Visualization, MYL; Funding Acquisition, YQC and XRT.

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/10641963.2023.2271187

Data availability statement

Data of the China Health and Retirement Longitudinal Study are available at its website (http://charls.pku.edu.cn/). Original data of the Shantou cohort are available from the corresponding authors upon reasonable request.

Additional information

Funding

This research was supported by the Grant for Key Disciplinary Project of Clinical Medicine under the Guangdong High-level University Development Program, Guangdong University Innovation Team Project [2019KCXTD003], 2020 Li Ka Shing Foundation Cross-Disciplinary Research Grant [2020LKSFG19B], “Dengfeng Project” for the construction of high-level hospitals in Guangdong Province - the First Affiliated Hospital of Shantou University Medical College [2020], Science and Technology project in Guangdong Province [2021010303].