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作 者:李倩[1] 邓开慧 龙艳君[1] 林鑫[1] 郄淑文[1] 周朝敏[1] 杨霞[1] 查艳[1] Li Qian;Deng Kaihui;Long Yanjun;Lin Xin;Qie Shuwen;Zhou Chaomin;Yang Xia;Zha Yan(Department of Nephrology, Guizhou Provincial People′s Hospital, Guiyang 550002, China;Department of Nephrology, the Second Affiliated Hospital of Guizhou Medical University, Kaili 556000, Guizhou, China)
机构地区:[1]贵州省人民医院肾内科,贵阳550002 [2]贵州医科大学第二附属医院肾内科,贵州凯里556000
出 处:《中华医学杂志》2019年第20期1567-1571,共5页National Medical Journal of China
基 金:贵州省科技计划项目(黔科合基础[2016]1087号)贵州省科技合作计划(黔科合LH字[2016]7151)贵州省高层次创新人才项目(黔科合平台人才[2018]5636号)贵州省人民医院青年基金(GZSYQN[2018]11号).
摘 要:目的探讨维持性血液透析患者蛋白质能量消耗(PEW)的影响因素。方法采用多中心横断面研究,收集2018年6至8月贵州省11家血液透析中心维持性血液透析患者一般资料及实验室检查结果,同时进行物理测量及人体成分分析。根据体质指数将PEW分为轻度、中度和重度,分析各指标对PEW患病程度的影响,进一步采用探索性因子分析将各因素归为几个公因子,logistic回归分析各公因子对PEW的影响程度。结果单因素分析结果显示,体细胞质量、瘦体重、脂肪量、体质指数、握力、小腿围、臀围、腰围、上臂中点周径、三头肌皮褶厚度、血红蛋白、白蛋白、前白蛋白、血钙、血磷、血镁、血肌酐、甲状旁腺素在不同程度PEW组间差异均有统计学意义(均P<0.05)。因子分析结果显示以上指标可归为5个公因子,有序多分类logistic回归模型显示,随着PEW患病程度的增加,各公因子得分降低,各公因子对PEW的影响程度从大到小依次排序为公因子2脂肪含量公因子(β=-2.258,P<0.001)、公因子4蛋白及血钙水平公因子(β=-1.589,P<0.001)、公因子1身体形态公因子(β=-1.144,P=0.001)、公因子3血磷和肌酐代谢公因子(β=-0.740,P=0.016)。结论脂肪含量降低、贫血、低蛋白血症、钙磷代谢紊乱是PEW的重要影响因素。Objective To analyze the influencing factors of protein energy wasting (PEW) in maintenance hemodialysis (MHD) patients. Methods A multicenter cross-sectional study was conducted in eleven hemodialysis centers of Guizhou province between June and August 2018. Clinical data, physical parameters, body composition data and laboratory values of MHD patients were collected. Analysis of variance was used to assess the impact of the indicators on the prevalence of PEW. Factor analysis was carried out after further classifing the factors into several common factors, and logistic regression was used to analyze the impact of common factors on PEW. Results The results of univariate analysis showed that somatic cell mass, lean weight, fat content, body mass index (BMI), grip strength, leg circumference, hip circumference, waist circumference, midpoint circumference of upper arm, triceps skin fold thickness, hemoglobin, albumin, prealbumin, serum calcium, phosphorus, serum magnesium, creatinine, parathyroid hormone were the influential factors of PEW (all P<0.05). Factor analysis indicated that the above indicators can be classified into five common factors. Logistic regression model showed that with the increase of the prevalence of PEW, the scores of common factors decreased, the absolute value of regression coefficient beta in sequence, was common factor 2 (β=-2.258, P<0.001), common factor 4 (β=-1.589, P<0.001), common factor 1 (β=-1.144, P=0.001) and common factor 3 (β=-0.740, P=0.016). Conclusion The reduction of fat content, anemia, hypoproteinemia, disorder of calcium and phosphorus metabolism were important factors influencing PEW.
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