Hanley Anthony J G, Karter Andrew J, Festa Andreas, D'Agostino Ralph, Wagenknecht Lynne E, Savage Peter, Tracy Russell P, Saad Mohammed F, Haffner Steven
Division of Clinical Epidemiology, University of Texas Health Sciences Center at San Antonio, San Antonio, Texas 78229-3900, USA.
Diabetes. 2002 Aug;51(8):2642-7. doi: 10.2337/diabetes.51.8.2642.
Factor analysis, a multivariate correlation technique, has been used to provide insight into the underlying structure of metabolic syndrome, which is characterized by physiological complexity and strong statistical intercorrelation among its key variables. The majority of previous factor analyses, however, have used only surrogate measures of insulin sensitivity. In addition, few have included members of multiple ethnic groups, and only one has presented results separately for subjects with impaired glucose tolerance. The objective of this study was to investigate, using factor analysis, the clustering of physiologic variables using data from 1,087 nondiabetic participants in the Insulin Resistance Atherosclerosis Study (IRAS). This study includes information on the directly measured insulin sensitivity index (S(I)) from intravenous glucose tolerance testing among African-American, Hispanic, and non-Hispanic white subjects aged 40-69 years at various stages of glucose tolerance. Principal factor analysis identified two factors that explained 28 and 9% of the variance in the dataset, respectively. These factors were interpreted as 1) a " metabolic" factor, with positive loadings of BMI, waist, fasting and 2-h glucose, and triglyceride and inverse loadings of log(S(I)+1) and HDL; and 2) a "blood pressure" factor, with positive loadings of systolic and diastolic blood pressure. The results were unchanged when surrogate measures of insulin resistance were used in place of log(S(I)+1). In addition, the results were similar within strata of sex, glucose tolerance status, and ethnicity. In conclusion, factor analysis identified two underlying factors among a group of metabolic syndrome variables in this dataset. Analyses using surrogate measures of insulin resistance suggested that these variables provide adequate information to explore the underlying intercorrelational structure of metabolic syndrome. Additional clarification of the physiologic characteristics of metabolic syndrome is required as individuals with this condition are increasingly being considered candidates for behavioral and pharmacologic intervention.
因子分析是一种多元相关技术,已被用于深入了解代谢综合征的潜在结构,代谢综合征的特点是生理复杂性以及其关键变量之间存在很强的统计相关性。然而,之前的大多数因子分析仅使用胰岛素敏感性的替代指标。此外,很少有研究纳入多个种族群体的成员,只有一项研究分别给出了糖耐量受损受试者的结果。本研究的目的是利用因子分析,通过胰岛素抵抗动脉粥样硬化研究(IRAS)中1087名非糖尿病参与者的数据,研究生理变量的聚类情况。该研究包括来自非裔美国人、西班牙裔和非西班牙裔白人40 - 69岁处于不同糖耐量阶段的受试者静脉葡萄糖耐量试验中直接测量的胰岛素敏感性指数(S(I))的信息。主因子分析确定了两个因子,分别解释了数据集中28%和9%的方差。这些因子被解释为:1)一个“代谢”因子,BMI、腰围、空腹及2小时血糖、甘油三酯的载荷为正,log(S(I)+1)和高密度脂蛋白(HDL)的载荷为负;2)一个“血压”因子,收缩压和舒张压的载荷为正。当使用胰岛素抵抗的替代指标替代log(S(I)+1)时,结果不变。此外,在性别、糖耐量状态和种族分层内结果相似。总之,因子分析在该数据集中的一组代谢综合征变量中确定了两个潜在因子。使用胰岛素抵抗替代指标的分析表明,这些变量为探索代谢综合征潜在的相互关联结构提供了充分信息。随着越来越多患有这种疾病的个体被视为行为和药物干预的候选对象,需要对代谢综合征的生理特征进行进一步阐明。