Comparation of two cystatin C-based eGFR equations in assessing risk of all-cause mortality and incident cardiovascular disease.

IF 3.9 2区医学 Q2 NUTRITION & DIETETICS

Nutrition & Metabolism Pub Date : 2024-11-19 DOI:10.1186/s12986-024-00870-4

Guangda Xin, Qianyu Li, Chen Sheng, Yining Zha, Kailiang Cheng

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引用次数: 0

Abstract

Background: Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) 2021 and European Kidney Function Consortium (EKFC) 2023 both recently updated the equations to estimate the glomerular filtration rate (eGFR) using cystatin C; however, little is known about the benefits of using the equations for the risk stratification of health outcomes. We conducted this longitudinal study to compare the cystatin C CKD-EPI and EKFC equations to track the risks of cardiovascular disease and all-cause mortality among Chinese adults.

Methods: We used data from China Health and Retirement Longitudinal Study (CHARLS) between 2011 and 2018. Adjusted logistic regression models and restricted cubic spline functions were used to evaluate the relationships of cystatin C-based eGFR values with incidence of cardiovascular disease and mortality.

Results: A total of 6 496 participants were finally included in this study. The mean age of the participants was 59.6 (± 9.5) years, including 2996 (46.1%) males. There were 473 deaths and 1996 cases of cardiovascular disease observed during a maximum follow-up of 7.0 years. Using cystatin C-based CKD-EPI equation, people of eGFR < 60 mL/min/1.73 m² had an increased risk of mortality (risk ratio [RR], 1.527; 95% CI, 1.068-2.178) and incident cardiovascular disease (RR, 1.363; 95% CI, 1.006-1.844), compared to those of eGFR ≥ 90 mL/min/1.73 m². On the contrary, we did not observe significant associations of eGFR levels by EKFC equation with mortality nor cardiovascular disease.

Conclusions: The findings indicated that cystatin C-based eGFR using CKD-EPI equation is more closely associated with all-cause mortality and cardiovascular disease compared to EKFC equation among Chinese adults. The cystatin C-based eGFR by CKD-EPI equation should be monitored in health practice, which needs further validation in other populations.

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两种基于胱抑素 C 的 eGFR 计算公式在评估全因死亡率和心血管疾病发病风险方面的比较。

背景：慢性肾脏病流行病学协作组（CKD-EPI）2021和欧洲肾功能联合会（EKFC）2023最近都更新了使用胱抑素C估算肾小球滤过率（eGFR）的方程；然而，人们对使用该方程进行健康风险分层的益处知之甚少。我们开展了这项纵向研究，比较胱抑素 C CKD-EPI 和 EKFC 方程，以追踪中国成年人的心血管疾病风险和全因死亡率：我们使用了中国健康与退休纵向研究（CHARLS）2011年至2018年的数据。采用调整后的逻辑回归模型和受限立方样条函数来评估基于胱抑素 C 的 eGFR 值与心血管疾病发病率和死亡率的关系：本研究最终纳入了 6 496 名参与者。参与者的平均年龄为 59.6（± 9.5）岁，其中男性 2996 人（46.1%）。在最长 7.0 年的随访期间，共发现 473 例死亡病例和 1996 例心血管疾病病例。使用基于胱抑素 C 的 CKD-EPI 等式，与 eGFR ≥ 90 mL/min/1.73 m2 的人群相比，eGFR 2 的人群的死亡风险（风险比 [RR]，1.527；95% CI，1.068-2.178）和心血管疾病发病风险（RR，1.363；95% CI，1.006-1.844）增加。相反，我们没有观察到通过 EKFC 方程计算的 eGFR 水平与死亡率或心血管疾病有显著关联：研究结果表明，在中国成年人中，与 EKFC 方程相比，基于胱抑素 C 的 CKD-EPI 方程的 eGFR 与全因死亡率和心血管疾病的关系更为密切。在医疗实践中应监测以 CKD-EPI 方程为基础的胱抑素 C eGFR，这需要在其他人群中进一步验证。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Nutrition & Metabolism 医学-营养学

CiteScore

8.40

自引率

0.00%

发文量

审稿时长

4-8 weeks

期刊介绍： Nutrition & Metabolism publishes studies with a clear focus on nutrition and metabolism with applications ranging from nutrition needs, exercise physiology, clinical and population studies, as well as the underlying mechanisms in these aspects. The areas of interest for Nutrition & Metabolism encompass studies in molecular nutrition in the context of obesity, diabetes, lipedemias, metabolic syndrome and exercise physiology. Manuscripts related to molecular, cellular and human metabolism, nutrient sensing and nutrient–gene interactions are also in interest, as are submissions that have employed new and innovative strategies like metabolomics/lipidomics or other omic-based biomarkers to predict nutritional status and metabolic diseases. Key areas we wish to encourage submissions from include: -how diet and specific nutrients interact with genes, proteins or metabolites to influence metabolic phenotypes and disease outcomes; -the role of epigenetic factors and the microbiome in the pathogenesis of metabolic diseases and their influence on metabolic responses to diet and food components; -how diet and other environmental factors affect epigenetics and microbiota; the extent to which genetic and nongenetic factors modify personal metabolic responses to diet and food compositions and the mechanisms involved; -how specific biologic networks and nutrient sensing mechanisms attribute to metabolic variability.