Association of cardiorenal biomarkers with mortality in metabolic syndrome patients: A prospective cohort study from NHANES

Q1 Medicine

Chronic Diseases and Translational Medicine Pub Date : 2024-09-03 DOI:10.1002/cdt3.149

Qianyi Gao, Shuanglong Jia, Xingbo Mo, Huan Zhang

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Abstract

Objectives

Approximately 20%–25% of the global adult population is affected by metabolic syndrome (MetS), highlighting its status as a major public health concern. This study aims to investigate the predictive value of cardiorenal biomarkers on mortality among patients with MetS, thus optimizing treatment strategies.

Methods

Utilizing data from the National Health and Nutrition Examination Survey (NHANES) cycles between 1999 and 2004, we conducted a prospective cohort study involving 2369 participants diagnosed with MetS. We evaluated the association of cardiac and renal biomarkers with all-cause and cardiovascular disease (CVD) mortality, employing weighted Cox proportional hazards models. Furthermore, machine learning models were used to predict mortality outcomes based on these biomarkers.

Results

Among 2369 participants in the study cohort, over a median follow-up period of 17.1 years, 774 (32.67%) participants died, including 260 (10.98%) from CVD. The highest quartiles of cardiac biomarkers (N-terminal pro-B-type natriuretic peptide [NT-proBNP]) and renal biomarkers (beta-2 microglobulin, [β2M]) were significantly associated with increased risks of all-cause mortality (hazard ratios [HRs] ranging from 1.94 to 2.06) and CVD mortality (HRs up to 2.86), after adjusting for confounders. Additionally, a U-shaped association was observed between high-sensitivity cardiac troponin T (Hs-cTnT), creatinine (Cr), and all-cause mortality in patients with MetS. Machine learning analyses identified Hs-cTnT, NT-proBNP, and β2M as important predictors of mortality, with the CatBoost model showing superior performance (area under the curve [AUC] = 0.904).

Conclusion

Cardiac and renal biomarkers are significant predictors of mortality in MetS patients, with Hs-cTnT, NT-proBNP, and β2M emerging as crucial indicators. Further research is needed to explore intervention strategies targeting these biomarkers to improve clinical outcomes.

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代谢综合征患者心肾生物标志物与死亡率的关系：一项来自 NHANES 的前瞻性队列研究

目标全球约有 20%-25% 的成年人受到代谢综合征（MetS）的影响，这凸显了代谢综合征是一个重大的公共卫生问题。本研究旨在探讨心肾生物标志物对代谢综合征患者死亡率的预测价值，从而优化治疗策略。方法我们利用 1999 年至 2004 年期间的美国国家健康与营养调查（NHANES）数据，对 2369 名确诊 MetS 患者进行了前瞻性队列研究。我们采用加权考克斯比例危险模型评估了心脏和肾脏生物标志物与全因死亡率和心血管疾病（CVD）死亡率的关系。此外，还使用机器学习模型根据这些生物标志物预测死亡率结果。结果在研究队列的 2369 名参与者中，在 17.1 年的中位随访期内，有 774 人（32.67%）死亡，其中 260 人（10.98%）死于心血管疾病。在对混杂因素进行调整后，心脏生物标志物（N-末端前 B 型利钠肽 [NT-proBNP]）和肾脏生物标志物（β-2 微球蛋白 [β2M]）的最高四分位数与全因死亡风险（危险比 [HRs] 从 1.94 到 2.06 不等）和心血管疾病死亡风险（危险比高达 2.86）的增加显著相关。此外，在 MetS 患者中，高敏心肌肌钙蛋白 T（Hs-cTnT）、肌酐（Cr）与全因死亡率之间呈 U 型关联。机器学习分析发现，Hs-cTnT、NT-proBNP 和 β2M 是预测死亡率的重要指标，其中 CatBoost 模型表现更优（曲线下面积 [AUC] = 0.904）。结论心脏和肾脏生物标志物是 MetS 患者死亡率的重要预测指标，其中 Hs-cTnT、NT-proBNP 和 β2M 成为关键指标。需要进一步研究探索针对这些生物标志物的干预策略，以改善临床预后。

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来源期刊

Chronic Diseases and Translational Medicine Medicine-General Medicine

CiteScore

6.70

自引率

0.00%

发文量

195

审稿时长

35 weeks

期刊介绍： This journal aims to promote progress from basic research to clinical practice and to provide a forum for communication among basic, translational, and clinical research practitioners and physicians from all relevant disciplines. Chronic diseases such as cardiovascular diseases, cancer, diabetes, stroke, chronic respiratory diseases (such as asthma and COPD), chronic kidney diseases, and related translational research. Topics of interest for Chronic Diseases and Translational Medicine include Research and commentary on models of chronic diseases with significant implications for disease diagnosis and treatment Investigative studies of human biology with an emphasis on disease Perspectives and reviews on research topics that discuss the implications of findings from the viewpoints of basic science and clinical practic.