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A longitudinal cohort based association study between uric acid level and metabolic syndrome in Chinese Han urban male population

机译:基于纵向队列的中国汉族城市男性人群尿酸水平与代谢综合征的相关性研究

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Background It has been recently demonstrated that serum uric acid (UA) is associated with metabolic syndrome (MetS) or its related clinical indications based on cross-sectional or prospective cohort studies. Nonetheless, due to the fact that UA level constantly fluctuates from time to time even for the person, using a single measure of UA level at baseline of those studies may not be sufficient for estimating the UA-Mets association. Methods To further estimate this time-dependent association, we fitted a generalized estimating equation (GEE) regression model with data from a large-scale 6-year longitudinal study, which included 2222 participants aged?>?=25?years with an average of 3.5 repeated measures of UA per person in the Health Management Center of Shandong Provincial Hospital, Shandong, China. Results After adjusting for other potential confounding factors (i.e., total cholesterol, low-density lipoprotein), it was verified that time-dependent UA level was an independent risk factor for MetS (OR?=?1.6920, p? Conclusions Serum UA level may serve as an important risk factor of MetS. Additionally, our study suggested that UA level be an independent risk factor to obesity, hypertension and dyslipidemia, but a protective factor to hyperglycemia. These findings are concordant with results from other studies on Asian populations, and jointly provide a basis to further develop a risk assessment model for predicting MetS using UA levels and other factors in China.
机译:背景技术最近,基于横断面研究或前瞻性队列研究,已经证明血清尿酸(UA)与代谢综合征(MetS)或其相关的临床适应症有关。但是,由于UA水平甚至对于人而言都会不时地波动,因此在这些研究的基线使用单一UA水平度量值可能不足以估计UA-Mets关联。方法为了进一步估计这种与时间相关的关联,我们使用来自大型六年纵向研究的数据拟合了广义估计方程(GEE)回归模型,该模型包括2222名年龄≥25岁的参与者,平均年龄≥25岁。 3.5在中国山东省立医院卫生管理中心进行的每人UA重复测量。结果在校正了其他潜在的混杂因素(例如总胆固醇,低密度脂蛋白)后,证实时间依赖性UA水平是MetS的独立危险因素(OR≥1.6920,p?)结论血清UA水平可能此外,我们的研究表明,UA水平是肥胖,高血压和血脂异常的独立危险因素,但是高血糖的保护因素,这些发现与其他针对亚洲人群的研究结果一致。共同为进一步开发利用中国尿酸水平和其他因素预测MetS的风险评估模型提供基础。

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