PDF(Pubmed)
Abstract：
Previous studies have shown both dysglycaemia and hyperuricemia are associated with an increased risk of atrial fibrillation (AF), while the relationship between serum uric acid (SUA) levels and AF in different fasting glucose patterns (FBG) is unclear. Therefore, this study aimed to determine the association between SUA and AF in different FBG patterns.
A total of 1840 patients in this case-control study were enrolled, including 920 AF patients and 920 controls. Patients were divided into three groups according to the different FBG patterns: normoglycemic, impaired fasting glucose (IFG), and diabetes mellitus (DM). Multivariate logistic regression models were performed to evaluate the relationship between SUA and AF in different FBG patterns. Pearson correlation analysis was used to explore the correlation between SUA and metabolic factors. Receiver operating characteristic (ROC) curve models indicated the diagnostic efficiency of SUA for diagnosing AF.
SUA was independently associated with AF after adjusting for all confounding factors in different FBG patterns(normoglycemic: OR=1.313, 95% CI:1.120-1.539; IFG: OR=1.386, 95% CI:1.011-1.898; DM: OR=1.505, 95% CI:1.150-1.970). Pearson\'s correlation analysis suggested that SUA in AF patients was correlated with several different metabolic factors in different FBG patterns (p<0.05). ROC curve analysis showed that SUA in the normoglycemic group combined with CHD and APOB [AUC: 0.906 (95% CI: 0.888-0.923)], in the IFG group combined with CHD and Scr [AUC: 0.863 (95% CI: 0.820-0.907)], in the DM group combined with CHD and SBP [AUC: 0.858 (95% CI: 0.818-0.898)] had the highest AUC for predicting AF.
Findings implied a significant association between SUA and AF in different FBG patterns and provide specific models combined with other factors (CHD, APOB, SCr, SBP), which might contribute to the diagnosis of AF.
A total of 1840 patients in this case-control study were enrolled, including 920 AF patients and 920 controls. Patients were divided into three groups according to the different FBG patterns: normoglycemic, impaired fasting glucose (IFG), and diabetes mellitus (DM). Multivariate logistic regression models were performed to evaluate the relationship between SUA and AF in different FBG patterns. Pearson correlation analysis was used to explore the correlation between SUA and metabolic factors. Receiver operating characteristic (ROC) curve models indicated the diagnostic efficiency of SUA for diagnosing AF.
SUA was independently associated with AF after adjusting for all confounding factors in different FBG patterns(normoglycemic: OR=1.313, 95% CI:1.120-1.539; IFG: OR=1.386, 95% CI:1.011-1.898; DM: OR=1.505, 95% CI:1.150-1.970). Pearson\'s correlation analysis suggested that SUA in AF patients was correlated with several different metabolic factors in different FBG patterns (p<0.05). ROC curve analysis showed that SUA in the normoglycemic group combined with CHD and APOB [AUC: 0.906 (95% CI: 0.888-0.923)], in the IFG group combined with CHD and Scr [AUC: 0.863 (95% CI: 0.820-0.907)], in the DM group combined with CHD and SBP [AUC: 0.858 (95% CI: 0.818-0.898)] had the highest AUC for predicting AF.
Findings implied a significant association between SUA and AF in different FBG patterns and provide specific models combined with other factors (CHD, APOB, SCr, SBP), which might contribute to the diagnosis of AF.
摘要：
未经证实：先前的研究表明，血糖异常和高尿酸血症与房颤（AF）的风险增加有关。而不同空腹血糖模式(FBG)下血清尿酸(SUA)水平与房颤的关系尚不清楚。因此,本研究旨在确定不同FBG模式下SUA与AF之间的关联。
未经评估：本病例对照研究共纳入1840例患者，包括920例房颤患者和920例对照。根据不同的FBG模式将患者分为三组：血糖正常，空腹血糖受损（IFG），糖尿病（DM）。采用多因素logistic回归模型评价不同FBG模式下SUA与AF的关系。采用Pearson相关分析探讨SUA与代谢因子的相关性。接收器工作特性（ROC）曲线模型表明SUA诊断AF的诊断效率。
UNASSIGNED：SUA在调整了不同FBG模式下的所有混杂因素后与AF独立相关（血糖正常：OR=1.313，95％CI：1.120-1.539；IFG：OR=1.386，95％CI：1.011-1.898；DM：OR=1.505，95％CI：1.150-1.970）。Pearson相关分析显示,房颤患者SUA与不同FBG模式下的几种不同代谢因子存在相关性(p<0.05)。ROC曲线分析显示，血糖正常组SUA合并CHD和APOB[AUC：0.906（95％CI：0.888-0.923）]，在合并CHD和Scr的IFG组中[AUC：0.863（95％CI：0.820-0.907）]，在DM合并CHD和SBP组[AUC:0.858(95%CI:0.818-0.898)]中,预测AF的AUC最高.
UNASSIGNED：研究结果暗示了不同FBG模式下SUA和AF之间的显着关联，并提供了与其他因素（CHD，APOB,SCr,SBP),这可能有助于房颤的诊断。
未经评估：本病例对照研究共纳入1840例患者，包括920例房颤患者和920例对照。根据不同的FBG模式将患者分为三组：血糖正常，空腹血糖受损（IFG），糖尿病（DM）。采用多因素logistic回归模型评价不同FBG模式下SUA与AF的关系。采用Pearson相关分析探讨SUA与代谢因子的相关性。接收器工作特性（ROC）曲线模型表明SUA诊断AF的诊断效率。
UNASSIGNED：SUA在调整了不同FBG模式下的所有混杂因素后与AF独立相关（血糖正常：OR=1.313，95％CI：1.120-1.539；IFG：OR=1.386，95％CI：1.011-1.898；DM：OR=1.505，95％CI：1.150-1.970）。Pearson相关分析显示,房颤患者SUA与不同FBG模式下的几种不同代谢因子存在相关性(p<0.05)。ROC曲线分析显示，血糖正常组SUA合并CHD和APOB[AUC：0.906（95％CI：0.888-0.923）]，在合并CHD和Scr的IFG组中[AUC：0.863（95％CI：0.820-0.907）]，在DM合并CHD和SBP组[AUC:0.858(95%CI:0.818-0.898)]中,预测AF的AUC最高.
UNASSIGNED：研究结果暗示了不同FBG模式下SUA和AF之间的显着关联，并提供了与其他因素（CHD，APOB,SCr,SBP),这可能有助于房颤的诊断。
