Abstract
The aim of this study was to evaluate and compare three competing models of the underlying factor structure of metabolic syndrome using confirmatory factor analysis (CFA). Data from the Insulin Resistance Atherosclerosis Study (IRAS) was used, which has previously been evaluated using principal components analysis (PCA). The three models that were evaluated consisted of oblique and orthogonal two-factor models with hypothesized underlying “metabolic” and “blood pressure” factors, and a four-factor model theorizing “insulin resistance,” “obesity,” “lipids,” and “blood pressure” as the underlying constructs. Several CFAs were performed using EQS Multivariate Software Version 5.7b with maximum likelihood estimation. The results showed that the four-factor model yielded significantly better data-model fit than two-factor models, with a comparative fit index of 0.963, and standardized root mean square residual of 0.036. Factors exhibited good construct reliability and variance extracted estimates except for the lipids factor. We concluded that the four-factor model of metabolic syndrome was the most plausible model among the three competing models.
Similar content being viewed by others
Abbreviations
- BMI:
-
body mass index
- CFI:
-
comparative fit index
- CFA:
-
confirmatory factor analysis
- HDL:
-
high density lipoprotein
- IRAS:
-
Insulin Resistance Atherosclerosis Study
- LDL:
-
low density lipoprotein
- S I :
-
measurement of insulin sensitivity
- PCA:
-
principal components analysis
- SRMR:
-
standardized root mean square residual
References
Ford ES, Giles WH, Dietz WH Prevalence of the metabolic syndrome among US adults: findings from the third National Health and Nutrition Examination Survey JAMA 2002; 287:356–359
Grundy SM, Cleeman JI, Daniels SR, et al. Diagnosis and management of metabolic syndrome: an American Heart Association/National Heart, Lung, and Blood Institute Scientific Statement Circulation 2005;112:2735–2752
Lakka HM, Laaksonen DF, Lakka TA, et al. The metabolic syndrome and total cardiovascular disease mortality in middle-aged men JAMA 2002; 288:2709–2716
Mitka M. Does the metabolic syndrome really exist? JAMA 2005; 294:2010–2013
Reaven GM, Lerner R, Stern M, et al. Role of insulin in endogenous hypertriglyceridemia J Clin Invest 1967; 46:1756–1767
Kahn R, Guse J, Ferrannini E, et al. The metabolic syndrome: time for a critical appraisal Diabetes Care 2005; 28:2289–2304
Hanson RL, Imperatore G, Bennett PH, Knowler WC Components of the “metabolic syndrome” and incidence of type 2 diabetes Diabetes 2002; 51:3120–3127
Young TK, Chateau D, Zhang M Factor analysis of ethnic variation in the metabolic (insulin resistance) syndrome in three Canadian populations Am J Human Biol 2002; 14:649–658
Meigs JB. Invited commentary: insulin resistance syndrome? Syndrome X? Multiple metabolic syndrome? A syndrome at all? Factor analysis reveals patterns in the fabric of correlated metabolic risk factors Am J Epidemiol 2000; 152:908–911
Lawlor DA, Ebrahim S, May M, Davey Smith G (Mis)use of factor analysis in the study of insulin resistance syndrome Am J Epidemiol 2004; 159:1013–1018
Shen BJ, Todaro JR, Niaura R, et al. Are metabolic risk factors one unified syndrome? Modeling the structure of the metabolic syndrome X Am J Epidemiol 2003; 157:701–711
Shen BJ, Goldberg RB, LLabre MM, et al. Is the factor structure of the metabolic syndrome comparable between men and women and across three ethnic groups: The Miami Community Health Study. Ann Epidemiol. (in press). Available online October 27, 2005
Novak S, Stapleton M, Litaker JR, Lawson KA A confirmatory factor analysis evaluation of the coronary heart disease risk factors of metabolic syndrome with emphasis on the insulin resistance factor Diabetes Obes Metab 2003; 5:388–396
Bentler PM. (1995) EQS Structural Equations Program Manual. Multivariate Software, Encino, CA
Hanley AJ, Karter AJ, Festa A, et al. Factor analysis of metabolic syndrome using directly measured insulin sensitivity. The Insulin Resistance Atherosclerosis StudyDiabetes2002; 51:2642–2647
Pacini G, Bergman RN MINMOD: a computer program to calculate insulin sensitivity, pancreatic responsiveness from the frequently sampled intravenous glucose tolerance test Comput Methods Programs Biomed 1986;23:113–122
Kline RB (1998) Principles and Practice of Structural Equation Modeling. Guilford Press, New York
Olefsky JM, Krusynska YT. Insulin resistance. In: Porte D, Jr., Sherwin RS, Baron A (eds), Ellenberg & Rifkin’s Diabetes Mellitus. (6th ed.). McGraw-Hill, New York, 2003: 367–400
Moller DE, Kaufman KD Metabolic syndrome: a clinical and molecular perspective Annu Rev Med 2005;56:45–62
Bollen KA (1989). Structural Equations with Latent Variables. John Wiley & Sons, New York, NY
Hu L, Bentler PM Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives Struct Equation Model: A Multidiscip J;6:1–55
Fornell Larcker DF Evaluating structural equation models with unobservable variables and measurement error J Marketing Res 1981;18:39–50
Hair JF, Anderson RE, Tatham RL, et al. (1995) Multivariate Data Analysis, (4th ed). Macmillan Publishing Company, New York
Salmenniemi U, Ruotsalainen E, Pihlajamaki J, et al. Multiple abnormalities in glucose and energy metabolism and coordinated changes in levels of adiponectin, cytokines, and adhesion molecules in subjects with metabolic syndrome Circulation 2004; 110:3824–3848
Pladevall M, Singal B, William LK, et al. A single factor underlies the metabolic syndrome: a confirmatory factor analysis Diabetes Care 2006; 29:113–122
World Health Organization. Definition, diagnosis and classification of diabetes mellitus and its complications. Report of a WHO consultation. Part 1: diagnosis and classification of diabetes mellitus. World Health Org, Geneva, 1999
Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults. (2001) Executive summary of the third report of the National Cholesterol Education Program (NCEP) expert panel on detection, evaluation, and treatment of high blood cholesterol in adults (Adult Treatment Panel III) JAMA 285:2488–2497
Moller DE, Kaufman KD Metabolic syndrome: a clinical and molecular perspective Annu Rev Med 2005;56:45–62
Betteridge D LDL heterogeneity: implications for atherogenicity in insulin resistance and NIDDM Diabetologia 1997;40:149S–151S
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Shah, S., Novak, S. & Stapleton, L.M. Evaluation and comparison of models of metabolic syndrome using confirmatory factor analysis. Eur J Epidemiol 21, 343–349 (2006). https://doi.org/10.1007/s10654-006-9004-2
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10654-006-9004-2