Clinical value of serum uric acid and homocysteine levels in predicting the occurrence of atrial fibrillation in patients with type 2 diabetes mellitus.
{"title":"Clinical value of serum uric acid and homocysteine levels in predicting the occurrence of atrial fibrillation in patients with type 2 diabetes mellitus.","authors":"Dong Li, Jinlong Deng, Lixian Ma","doi":"10.1186/s12938-025-01418-0","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusion: </strong>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.</p>","PeriodicalId":8927,"journal":{"name":"BioMedical Engineering OnLine","volume":"24 1","pages":"86"},"PeriodicalIF":2.9000,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12239247/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"BioMedical Engineering OnLine","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1186/s12938-025-01418-0","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}
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.
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