基于多元统计学方法的骨量肌量减少性肥胖综合征的结构特征研究  

A Clinical Study of Structural Properties of Osteosarcopenic Obesity Syndrome Using Multivariate Statistical Methods

作　　者：聂义珍[1] 闫朝岐[1] 燕巍[1] 付红梅[1] 赵兴鹃[1] 尹慧 吴群红[2] NIE Yizhen;YAN Zhaoqi;YAN Wei;FU Hongmei;ZHAO Xingjuan;YIN Hui;WU Qunhong(Physical Examination Center,the 2nd Affiliated Hospital of Harbin Medical University,Harbin 150086,China;School of Health Management,Harbin Medical University,Harbin 150081,China)

机构地区：[1]哈尔滨医科大学附属第二医院体检中心,黑龙江省哈尔滨市150086 [2]哈尔滨医科大学卫生管理学院,黑龙江省哈尔滨市150081

出　　处：《中国全科医学》2022年第22期2733-2739,2745,共8页Chinese General Practice

基　　金：黑龙江省卫生计生委科研课题(2018164)。

摘　　要：背景骨量肌量减少性肥胖综合征(OSO)是一种严重损害老年人健康的疾病,对疾病进行临床分型可为疾病的临床诊治提供指导。基于OSO诊断变量间的相关性对OSO进行分型,并探寻OSO的结构特征,可为OSO的防治提供新的思路。目的探索OSO的结构特征,为实现OSO的个体化诊治提供理论依据。方法本研究为横断面研究。于2018年1月至2020年10月,采用随机抽样法,选取在哈尔滨医科大学附属第二医院体检中心接受健康体检、年龄≥60岁的老年OSO患者作为研究对象,采集其OSO诊断变量〔四肢骨骼肌指数,握力,体脂百分比(BF%),腰椎1~4(L_(1~4))、髋部、股骨颈骨密度(BMD),体质指数(BMI),腰围,步速〕、社会人口学特征、生活方式、常见慢性病患病情况等方面的资料。在利用因子分析法对OSO诊断变量数据进行分析前,采用KMO检验、Bartlett's球形检验评价OSO诊断变量数据是否适合进行因子分析。通过主成分分析法,提取特征值≥1.000的成分,并运用最大方差正交旋转法得出方差最大正交旋转矩阵。根据因子正交旋转矩阵,对公因子进行命名。基于公因子得分,利用离差平方和系统聚类法生成树状结构并对患者进行分类,通过比较不同类别患者间诊断变量水平和临床特征的差异,分析OSO的结构特征。结果共纳入107例老年OSO患者。KMO值为0.688,Bartlett's球形检验χ^(2)=492.374,P<0.001,表明OSO诊断变量数据适合进行因子分析;按特征根>1.000的标准可提取3个公因子(骨质疏松因子、肌肉+体脂因子、肥胖因子),3个公因子的累积方差贡献率为81.408%,各诊断变量在所属公因子上的载荷值为0.770~0.918。聚类分析结果显示,共将OSO患者分为3类。不同类别人群四肢骨骼肌指数、握力、BF%、BMD_(L1~4)、BMD_(髋部)、BMD_(股骨颈)、BMI、腰围比较,差异均有统计学意义(P<0.05)。其中第1类人群的BMD_(L1~4)、BMD_(髋部)、BMD_(股骨颈Background Osteosarcopenic obesity syndrome(OSO)is a disease that seriously endangers the health of older people.The rational classification of the disease can guide the clinical diagnosis and treatment.Therefore,classifying OSO based on inter-correlations of its diagnostic variables and exploring its structural properties may offer insights into clinical prevention and treatment of OSO.Objective To explore the structural properties of OSO,providing a theoretical basis for individualized diagnosis and treatment of the disease.Methods A cross-sectional study was conducted with a random sample of OSO patients(≥60 years old)who underwent physical examination in Physical Examination Center,the 2nd Affiliated Hospital of Harbin Medical University from January 2018 to December 2020.The data collected include 9 diagnostic variables for OSO〔skeletal muscle index,grip strength,body fat percentage,BMD of the lumbar spine(L1-L4),hip and femoral neck,BMI,waist circumference,walking pace〕,sociodemographic characteristics,lifestyle and prevalence of common chronic diseases.KMO test and Bartlett's test of sphericity were used to evaluate the suitability of diagnostic variables for factor analysis.The components with an eigenvalue equal to or greater than 1.000 were extracted by principal component analysis,and the varimax orthogonal rotation matrix was obtained by the varimax orthogonal rotation method.The common factors were named according to the orthogonal rotation matrix of factors.On the basis of factor analysis,thesum of squares and systematic cluster analysis were used to develop a dendrogram for classifying patients.The structural properties of OSO were analyzed by comparing the values of diagnostic variables and clinical features among patients of different categories.Results A total of 107 cases were included.The KMO value(0.688)and the result of Bartlett's test of sphericity(χ^(2)=492.374,P<0.001)indicated that the data of diagnostic variables were suitable for factor analysis.Three common factors(osteoporosis

关 键 词：骨量肌量减少性肥胖综合征 因子分析 聚类分析 多元统计学方法 

分 类 号：R589[医药卫生—内分泌] R195[医药卫生—内科学]