Longitudinal changes in plasma Cystatin C and all-cause mortality risk among the middle-aged and elderly Chinese population

Ying Zhang; Ling Zhang; Jie Xing; Yujie Weng; Wangquan Xu; Liping Zhi; Min Yuan

doi:10.1016/j.clinbiochem.2024.110858

Longitudinal changes in plasma Cystatin C and all-cause mortality risk among the middle-aged and elderly Chinese population

Clin Biochem. 2024 Nov 22:110858. doi: 10.1016/j.clinbiochem.2024.110858. Online ahead of print.

Authors

Ying Zhang¹, Ling Zhang¹, Jie Xing¹, Yujie Weng¹, Wangquan Xu¹, Liping Zhi¹, Min Yuan²

Affiliations

¹ Department of Health Data Science, Anhui Medical University, Hefei, Anhui 230032, China.
² Department of Health Data Science, Anhui Medical University, Hefei, Anhui 230032, China; MOE Key Laboratory of Population Health Across Life Cycle, Hefei 230032, Anhui, China. Electronic address: myuan@ustc.edu.cn.

PMID: 39581251
DOI: 10.1016/j.clinbiochem.2024.110858

Abstract

Objective: Elevated plasma Cystatin C levels are associated with an increased mortality risk among middle-aged and elderly Chinese individuals. This study explores whether tracking the longitudinal changes in Cystatin C can improve the prediction of mortality risk and allow better risk stratification, jointly with baseline measurements.

Design & methods: This analysis includes 3,195 participants from the China Health and Retirement Longitudinal Study who completed plasma Cystatin C measurements in two waves (2011 and 2015) and were followed through 2020. To evaluate the association between Cystatin C levels/changes and mortality risk, multivariate Cox proportional hazard models were employed, adjusting for potential confounders. Survival probabilities were compared using Kaplan-Meier curves and log-rank tests, while restricted cubic splines were utilized to illustrate any nonlinear relationships between Cystatin C levels and hazard ratios.

Results: Participants in the highest quartile of baseline Cystatin C show an increased risk of mortality compared to those in the lowest quartile (hazard ratio (HR): 1.51, 95 % CI: 1.02-2.24, p = 0.04). Including longitudinal changes in Cystatin C further strengthens this association (HR: 1.81, 95 % CI: 1.20-2.74, p < 0.001). Kaplan-Meier plots show that baseline levels effectively stratify both the entire cohort and gender-specific subgroups (p < 0.001). Moreover, integrating baseline levels with the longitudinal changes in Cystatin C levels provides additional stratification benefits. The predictive performance significantly improves by including longitudinal changes in Cystatin C in baseline-only models, with the concordance index increasing from 0.745 to 0.839 and the area under the receiver operator characteristic curve rising from 0.751 to 0.845. Additionally, significant nonlinear relationships between changes in Cystatin C and HR are observed in the entire population, the males and the females (p = 0.003, 0.018, 0.025).

Conclusions: Dynamic monitoring of changes in Cystatin C could enhance the prediction of mortality risk among middle-aged and elderly individuals.

Keywords: All-cause mortality; China Health and Retirement Longitudinal Study; Longitudinal biomarker; Plasma Cystatin C; Restricted cubic spline.