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Longitudinal changes in plasma Cystatin C and all-cause mortality risk among the middle-aged and elderly Chinese
Ying Zhang1, Ling Zhang1, Jie Xing1
1Department of Health Data Science, Anhui Medical University, Hefei, Anhui 230032, China.
Insights
Tracking changes in Cystatin C levels over time improves mortality risk prediction in older Chinese adults. Dynamic monitoring of Cystatin C offers better risk stratification than baseline measurements alone.
Area of Science:
- Gerontology
- Biomarkers
- Epidemiology
Background:
- Elevated plasma Cystatin C is linked to higher mortality in middle-aged and elderly Chinese individuals.
- Baseline Cystatin C levels predict mortality risk.
Purpose of the Study:
- To investigate if tracking longitudinal changes in Cystatin C improves mortality risk prediction.
- To assess if dynamic Cystatin C monitoring enhances risk stratification.
Main Methods:
- Analysis of 3,195 participants from the China Health and Retirement Longitudinal Study with two Cystatin C measurements.
- Multivariate Cox proportional hazard models and Kaplan-Meier curves were used.
- Restricted cubic splines analyzed nonlinear relationships between Cystatin C and mortality.
Main Results:
- Higher baseline Cystatin C associated with increased mortality risk (HR: 1.51).
- Including longitudinal changes significantly strengthened the association (HR: 1.81).
- Dynamic monitoring improved predictive performance (concordance index from 0.745 to 0.839, AUC from 0.751 to 0.845).
Conclusions:
- Dynamic monitoring of Cystatin C changes enhances mortality risk prediction.
- Integrating longitudinal Cystatin C data provides significant risk stratification benefits.
- This approach is valuable for middle-aged and elderly populations.
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.
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