中国全科医学 ›› 2024, Vol. 27 ›› Issue (18): 2179-2185.DOI: 10.12114/j.issn.1007-9572.2023.0892

• 论著 • 上一篇    下一篇

胰岛素抵抗代谢评分与慢性心力衰竭患者不良预后的相关性研究

阴秋果, 秦欣童, 张议丹, 姜鹏, 郭平, 贾兴泰, 简立国*()   

  1. 450000 河南省郑州市,郑州大学第二附属医院心血管内科
  • 收稿日期:2024-01-17 修回日期:2024-02-28 出版日期:2024-06-20 发布日期:2024-03-22
  • 通讯作者: 简立国

  • 作者贡献:
    阴秋果提出主要研究目标,负责研究的构思与设计,研究的实施,并撰写论文;阴秋果、秦欣童、张议丹、姜鹏负责数据收集与整理;阴秋果、郭平、贾兴泰负责对文章的修订;简立国负责对研究过程的质量控制和审查,全面协调文章进展。
  • 基金资助:
    河南省医学科技攻关计划项目(LHGJ20200398)

Correlation between Insulin Resistance Metabolic Score and Poor Prognosis in Patients with Chronic Heart Failure

YIN Qiuguo, QIN Xintong, ZHANG Yidan, JIANG Peng, GUO Ping, JIA Xingtai, JIAN Liguo*()   

  1. Department of Cardiology, the Second Affiliated Hospital of Zhengzhou University, Zhengzhou 450000, China
  • Received:2024-01-17 Revised:2024-02-28 Published:2024-06-20 Online:2024-03-22
  • Contact: JIAN Liguo

摘要: 背景 胰岛素抵抗(IR)与心血管疾病的发生、发展关系密切,目前多项研究已经证实了IR在心力衰竭(HF)患者中非常普遍,并与不良心血管结局有关,而反映IR的指标——胰岛素抵抗代谢评分(Mets-IR)与慢性心力衰竭(CHF)患者不良预后之间的关联目前尚不明确。目的 分析Mets-IR与CHF患者不良预后之间的相关性。方法 本研究为回顾性研究,选取2020年1月—2021年1月在郑州大学第二附属医院心血管内科确诊为CHF的患者313例为研究对象。根据是否发生全因死亡将患者分为两组:全因死亡组(61例)和对照组(252例)。将Mets-IR作为分类变量进行分析,以中位数将Mets-IR分为两类:低水平Mets-IR(Mets-IR<37.28)和高水平Mets-IR(Mets-IR≥37.28)。收集患者基线资料,其中包括Mets-IR及其年龄、血清生物标志物和超声心动图指标,随访截至2022-12-31。通过本院电子病历系统或电话随访收集患者预后情况,主要终点事件为全因死亡,次要终点事件为因HF再入院。不同水平Mets-IR患者全因死亡及因HF再入院的生存曲线采用Kaplan-Meier图和Log-rank检验进行分析。应用Cox比例风险回归模型分析Mets-IR与全因死亡及因HF再入院风险的相关性。构建受试者工作特征(ROC)曲线,分析Mets-IR对CHF患者全因死亡及因HF再入院风险的预测价值。结果 中位随访时间25.0(9.0,28.5)个月,313例CHF患者中出现全因死亡61例(19.5%)、因HF再入院121例(38.7%)。全因死亡组患者年龄、BMI、空腹血糖、Mets-IR、N末端B型钠尿肽前体、血尿酸、中性粒细胞计数、红细胞分布宽度、心房颤动、高血压、利尿剂、醛固酮受体拮抗剂、美国纽约心脏病学会分级高于对照组,三酰甘油、高密度脂蛋白胆固醇、低密度脂蛋白胆固醇、白蛋白、血红蛋白、血钠、左心室射血分数、血管紧张素转化酶抑制剂/血管紧张素受体拮抗剂/血管紧张素受体-脑啡肽酶抑制剂低于对照组(P<0.05)。Log-rank检验结果显示,高水平Mets-IR患者的全因死亡率及因HF再入院率均高于低水平Mets-IR患者(P<0.001)。调整多个混杂因素后的Cox比例风险回归分析结果显示,与低水平Mets-IR患者相比,高水平Mets-IR患者全因死亡风险(HR=2.90,95%CI=1.51~5.54,P=0.001)、因HF再入院风险(HR=1.55,95%CI=1.04~2.30,P=0.030)均升高。Mets-IR预测全因死亡风险、因HF再入院风险的ROC曲线下面积分别为0.68(95%CI=0.62~0.75)、0.62(95%CI=0.55~0.68)。结论 Mets-IR水平升高可能会增加CHF患者的全因死亡及因HF再入院风险,可用于CHF患者的危险分层。

关键词: 心力衰竭, 慢性心力衰竭, 胰岛素抵抗代谢评分, 全因死亡, 因心力衰竭再入院, 不良预后, 回顾性研究, Cox比例风险模型

Abstract:

Background

Insulin resistance (IR) is closely related to the development and progression of cardiovascular disease, and several studies have now demonstrated that IR is highly prevalent in patients with heart failure (HF) and is associated with adverse cardiovascular outcomes, whereas the association between the Metabolic Score of Insulin Resistance (Mets-IR), an indicator reflecting IR, and the poor prognosis in patients with chronic heart failure (CHF) is currently unknown.

Objective

To analyse the correlation between Mets-IR and poor prognosis in patients with CHF.

Methods

This was a retrospective study, and 313 patients who were diagnosed with CHF in the Department of Cardiovascular Medicine of the Second Affiliated Hospital of Zhengzhou University from January 2020 to January 2021 were selected as study subjects. The patients were divided into two groups according to whether all-cause mortality occurred: the all-cause mortality group (61 cases) and the control group (252 cases). Mets-IR was analysed as a categorical variable, and Mets-IR was classified into two categories by median: low level Mets-IR (Mets-IR<37.28) and high level Mets-IR (Mets-IR≥37.28). Patients' baseline data, which included Mets-IR and their age, serum biomarkers and echocardiographic indices, were collected and followed up until 2022-12-31, and patients' prognosis was collected through our electronic medical record system or telephone follow-up, with the primary endpoint event being all-cause mortality and the secondary endpoint event being readmission due to HF. Survival curves for all-cause mortality and readmission due to HF in patients with different levels of Mets-IR were analysed using Kaplan-Meier plots and Log-rank tests. Cox proportional hazards regression model was applied to analyse the correlation between Mets-IR and the risk of all-cause mortality and readmission due to HF. Receiver operating characteristic (ROC) curves were constructed to analyse the predictive value of Mets-IR for the risk of all-cause mortality and readmission due to HF in CHF patients.

Results

At a median follow-up of 25.0 (9.0, 28.5) months, 61 (19.5%) all-cause mortality and 121 (38.7%) readmissions due to HF occurred in 313 CHF patients. Patients in the all-cause mortality group had higher age, BMI, fasting glucose, Mets-IR, N-terminal B-type natriuretic peptide precursor, blood uric acid, neutrophil count, erythrocyte distribution width, atrial fibrillation, hypertension, diuretics, aldosterone receptor antagonist, and New York Heart Association classification than controls, and triacylglycerol, high-density lipoprotein cholesterol, and low-density lipoprotein cholesterol, albumin, haemoglobin, blood sodium, left ventricular ejection fraction, and angiotensin-converting enzyme inhibitor/angiotensin receptor antagonist /angiotensin receptor-enkephalinase inhibitor were lower than those of the control group (P<0.05) .The results of the log-rank test showed that the all-cause mortality rate and the readmission rate due to HF were both higher in the patients with high-level Mets-IR than those with low-level Mets-IR (P<0.001). Cox proportional hazards regression analysis after adjusting for several confounders showed that compared with low-level Mets-IR patients, high-level Mets-IR patients had higher risks of all-cause mortality (HR=2.90, 95%CI=1.51-5.54, P=0.001) and readmission due to HF (HR=1.55, 95%CI=1.04-2.30, P=0.030). The area under the ROC curve for Mets-IR to predict the risk of all-cause mortality and the risk of readmission due to HF were 0.68 (95%CI=0.62-0.75) and 0.62 (95%CI=0.55-0.68) .

Conclusion

Elevated Mets-IR levels may increase the risk of all-cause mortality and readmission due to HF in patients with CHF, and can be used for risk stratification of CHF patients.

Key words: Heart failure, Chronic heart failure, Insulin resistance metabolic score, All-cause mortality, Readmission due to heart failure, Poor prognosis, Retrospective studies, Cox proportional hazards models

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