Literature DB >> 12145182

Factor analysis of metabolic syndrome using directly measured insulin sensitivity: The Insulin Resistance Atherosclerosis Study.

Anthony J G Hanley1, Andrew J Karter, Andreas Festa, Ralph D'Agostino, Lynne E Wagenknecht, Peter Savage, Russell P Tracy, Mohammed F Saad, Steven Haffner.   

Abstract

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.

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Year:  2002        PMID: 12145182     DOI: 10.2337/diabetes.51.8.2642

Source DB:  PubMed          Journal:  Diabetes        ISSN: 0012-1797            Impact factor:   9.461


  50 in total

1.  Principal component 1 score calculated from metabolic syndrome diagnostic parameters is a possible marker for the development of metabolic syndrome in middle-aged Japanese men without treatment for metabolic diseases.

Authors:  Kazuki Mochizuki; Rie Miyauchi; Yasumi Misaki; Yoko Ichikawa; Toshinao Goda
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2.  Insulin resistance is not necessarily an essential element of metabolic syndrome.

Authors:  Rudruidee Karnchanasorn; Horng-Yi Ou; Lee-Ming Chuang; Ken C Chiu
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3.  Metabolic syndrome is common among middle-to-older aged Mediterranean patients with rheumatoid arthritis and correlates with disease activity: a retrospective, cross-sectional, controlled, study.

Authors:  S A Karvounaris; P I Sidiropoulos; J A Papadakis; E K Spanakis; G K Bertsias; H D Kritikos; E S Ganotakis; D T Boumpas
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Review 4.  The metabolic syndrome.

Authors:  Marc-Andre Cornier; Dana Dabelea; Teri L Hernandez; Rachel C Lindstrom; Amy J Steig; Nicole R Stob; Rachael E Van Pelt; Hong Wang; Robert H Eckel
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5.  Factor analysis of modifiable cardiovascular risk factors and prevalence of metabolic syndrome in adult Taiwanese.

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6.  Evaluation and comparison of models of metabolic syndrome using confirmatory factor analysis.

Authors:  Sonalee Shah; Suzanne Novak; Laura M Stapleton
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7.  Characteristics of metabolic syndrome based on clustering pattern among Korean adolescents: findings from the Korean National Health and Nutrition Examination Survey, 2007-2008.

Authors:  Min Jung Ko; Eun Young Lee; Kirang Kim
Journal:  Eur J Pediatr       Date:  2012-10-23       Impact factor: 3.183

Review 8.  Genetic determinants of cardiometabolic risk: a proposed model for phenotype association and interaction.

Authors:  Piers R Blackett; Dharambir K Sanghera
Journal:  J Clin Lipidol       Date:  2012-04-22       Impact factor: 4.766

Review 9.  The metabolic syndrome: time for a critical appraisal. Joint statement from the American Diabetes Association and the European Association for the Study of Diabetes.

Authors:  R Kahn; J Buse; E Ferrannini; M Stern
Journal:  Diabetologia       Date:  2005-09       Impact factor: 10.122

10.  Validation of metabolic syndrome score by confirmatory factor analysis in children and adults and prediction of cardiometabolic outcomes in adults.

Authors:  Anna Viitasalo; Timo A Lakka; David E Laaksonen; Kai Savonen; Hanna-Maaria Lakka; Maija Hassinen; Pirjo Komulainen; Tuomo Tompuri; Sudhir Kurl; Jari A Laukkanen; Rainer Rauramaa
Journal:  Diabetologia       Date:  2014-01-24       Impact factor: 10.122

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