Lafortuna Claudio L, Adorni Fulvio, Agosti Fiorenza, Sartorio Alessandro
Istituto di Bioimmagini e Fisiologia Molecolare, Consiglio Nazionale delle Ricerche, Segrate, Milano, Italy.
Nutr Metab Cardiovasc Dis. 2008 Mar;18(3):233-41. doi: 10.1016/j.numecd.2007.02.002. Epub 2007 Jun 27.
Factor analysis is a multivariate correlation technique that is frequently employed to characterise the clustering of intercorrelated abnormalities, which underlie the metabolic syndrome in cohorts of individuals with different characteristics. To our knowledge, it has never been used to identify the components of this syndrome in obese subjects. The purpose of this study was to use factor analysis to investigate the clustering of features, which characterise the metabolic syndrome, in a cohort of 552 obese women aged 18-83 years (mean body mass index: 43.0 kg/m(2)+/-5.7 SD).
Principal component analysis reduced ten correlated physiological variables, to four uncorrelated factors that explained 72.2% of the variance in the original parameters. These factors were interpreted as: (1) an insulin resistance factor, with positive loading of fasting serum insulin and homeostatic model assessment of insulin resistance; (2) a metabolic glucose/lipid factor, with positive loading of fasting plasma glucose, triglycerides, waist-to-hip ratio, and inverse loading of high density lipoprotein cholesterol; (3) a body mass factor, with positive loading of body mass and waist circumference; and (4) a blood pressure factor, with positive loading of systolic and diastolic blood pressure.
The identification of four independent factors is consistent with previous findings among samples of different populations and may also support, in obese women, the hypothesis that multiple physiological determinants are responsible for the abnormalities underlying the metabolic syndrome. Nonetheless, findings in this cohort of obese women suggest that the absolute degree of adiposity is not correlated with any tested component of the metabolic syndrome, but that the relative fat distribution is highly correlated with the development of hyperglycaemic and dyslipidaemic phenomena. Furthermore, insulin resistance appears to be a major factor in obese individuals, independent of other metabolic and anthropometic abnormalities.
因子分析是一种多元相关技术,常用于描述相互关联的异常情况的聚类,这些异常是不同特征个体队列中代谢综合征的基础。据我们所知,它从未被用于识别肥胖受试者中该综合征的组成部分。本研究的目的是使用因子分析来研究在一个由552名年龄在18 - 83岁(平均体重指数:43.0 kg/m²±5.7标准差)的肥胖女性组成的队列中,表征代谢综合征的特征聚类情况。
主成分分析将十个相关的生理变量简化为四个不相关的因子,这些因子解释了原始参数中72.2%的方差。这些因子被解释为:(1)胰岛素抵抗因子,空腹血清胰岛素和胰岛素抵抗稳态模型评估呈正负荷;(2)代谢性葡萄糖/脂质因子,空腹血糖、甘油三酯、腰臀比呈正负荷,高密度脂蛋白胆固醇呈负负荷;(3)体重因子,体重和腰围呈正负荷;(4)血压因子,收缩压和舒张压呈正负荷。
四个独立因子的识别与不同人群样本中的先前发现一致,并且在肥胖女性中也可能支持这样的假设,即多种生理决定因素是代谢综合征潜在异常的原因。尽管如此,这个肥胖女性队列中的发现表明,肥胖的绝对程度与代谢综合征的任何测试成分均无相关性,但相对脂肪分布与高血糖和血脂异常现象的发生高度相关。此外,胰岛素抵抗似乎是肥胖个体中的一个主要因素,独立于其他代谢和人体测量异常。