Clinical value of serum uric acid and homocysteine levels in predicting the occurrence of atrial fibrillation in patients with type 2 diabetes mellitus.

IF 2.9 4区 医学 Q3 ENGINEERING, BIOMEDICAL
Dong Li, Jinlong Deng, Lixian Ma
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引用次数: 0

Abstract

Background: The objective of this study was to evaluate the predictive value of serum uric acid (UA) and homocysteine (Hcy) levels for atrial fibrillation (AF) development in type 2 diabetes mellitus (T2DM) patients.

Methods: Clinical data of 400 patients diagnosed with T2DM between January 2020 and August 2023 were retrospectively analyzed. They were categorized into AF group and non-AF group according to whether AF occurred or not. The predictive efficacy of serum UA and Hcy on the occurrence of AF in patients with T2DM was analyzed by using the receiver operating characteristic (ROC). The combined values were derived using regression coefficients, enabling joint prediction. Logistic regression analysis was employed to identify the influential factors. The nomogram prediction model was developed using R software based on the screened influencing factors, with internal validation performed via the Bootstrap method. ROC curves, calibration curves, and decision curves were plotted to evaluate the efficacy of the model.

Results: Compared with the non-AF group, the total bilirubin (TBIL), DBIL/TBIL, total protein (TP), UA, Hcy, cystatin C (Cys C), and large platelet ratio (PLCR) levels were significantly higher in the AF group, whereas triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and platelet count (PLT) levels were significantly lower (p < 0.05). The area under the curve (AUC) predicted by the combination of serum UA and serum Hcy was 0.928, which was higher than that of UA (Z = 2.635, p = 0.008) and Hcy (Z = 4.629, p < 0.001). UA, Hcy, TP, PLCR, TG, and LDL-C were all influential factors for AF in patients with T2DM (p < 0.05). The nomogram model constructed on the basis of the above independent influences predicted an AUC of 0.946 (95% CI: 0.924-0.968) for the occurrence of AF, with p = 0.134 in the Hosmer-Lemeshow test. In addition, calibration curve and decision curve analyses showed good agreement and clinical benefit for this nomogram model.

Conclusion: Serum UA and Hcy levels exhibited some predictive value for the occurrence of AF in patients with T2DM. The nomogram model incorporating demographic and serological parameters demonstrated good diagnostic performance and may serve as a valuable predictive tool for AF occurrence.

血清尿酸和同型半胱氨酸水平预测2型糖尿病患者房颤发生的临床价值
背景:本研究的目的是评估血清尿酸(UA)和同型半胱氨酸(Hcy)水平对2型糖尿病(T2DM)患者房颤(AF)发展的预测价值。方法:回顾性分析2020年1月至2023年8月诊断为T2DM的400例患者的临床资料。根据是否发生房颤分为AF组和非AF组。采用受试者工作特征(ROC)分析血清UA、Hcy对T2DM患者房颤发生的预测作用。利用回归系数推导组合值,实现联合预测。采用Logistic回归分析确定影响因素。基于筛选的影响因素,利用R软件建立nomogram预测模型,并通过Bootstrap方法进行内部验证。绘制ROC曲线、校正曲线和决策曲线,评价模型的疗效。结果:与非房颤组比较,房颤组总胆红素(TBIL)、DBIL/TBIL、总蛋白(TP)、UA、Hcy、胱抑素C (Cys C)、大血小板比率(PLCR)水平显著升高,甘油三酯(TG)、高密度脂蛋白胆固醇(HDL-C)、低密度脂蛋白胆固醇(LDL-C)、血小板计数(PLT)水平显著降低(p)。血清UA和Hcy水平对2型糖尿病患者房颤的发生具有一定的预测价值。结合人口学和血清学参数的nomogram模型显示出良好的诊断性能,可以作为AF发生的有价值的预测工具。
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来源期刊
BioMedical Engineering OnLine
BioMedical Engineering OnLine 工程技术-工程:生物医学
CiteScore
6.70
自引率
2.60%
发文量
79
审稿时长
1 months
期刊介绍: BioMedical Engineering OnLine is an open access, peer-reviewed journal that is dedicated to publishing research in all areas of biomedical engineering. BioMedical Engineering OnLine is aimed at readers and authors throughout the world, with an interest in using tools of the physical and data sciences and techniques in engineering to understand and solve problems in the biological and medical sciences. Topical areas include, but are not limited to: Bioinformatics- Bioinstrumentation- Biomechanics- Biomedical Devices & Instrumentation- Biomedical Signal Processing- Healthcare Information Systems- Human Dynamics- Neural Engineering- Rehabilitation Engineering- Biomaterials- Biomedical Imaging & Image Processing- BioMEMS and On-Chip Devices- Bio-Micro/Nano Technologies- Biomolecular Engineering- Biosensors- Cardiovascular Systems Engineering- Cellular Engineering- Clinical Engineering- Computational Biology- Drug Delivery Technologies- Modeling Methodologies- Nanomaterials and Nanotechnology in Biomedicine- Respiratory Systems Engineering- Robotics in Medicine- Systems and Synthetic Biology- Systems Biology- Telemedicine/Smartphone Applications in Medicine- Therapeutic Systems, Devices and Technologies- Tissue Engineering
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