A Comparison of Different Approaches to Unravel the Latent Structure within Metabolic Syndrome

Background Exploratory factor analysis is a commonly used statistical technique in metabolic syndrome research to uncover latent structure amongst metabolic variables. The application of factor analysis requires methodological decisions that reflect the hypothesis of the metabolic syndrome construct. These decisions often raise the complexity of the interpretation from the output. We propose two alternative techniques developed from cluster analysis which can achieve a clinically relevant structure, whilst maintaining intuitive advantages of clustering methodology. Methods Two advanced techniques of clustering in the VARCLUS and matroid methods are discussed and implemented on a metabolic syndrome data set to analyze the structure of ten metabolic risk factors. The subjects were selected from the normative aging study based in Boston, Massachusetts. The sample included a total of 847 men aged between 21 and 81 years who provided complete data on selected risk factors during the period 1987 to 1991. Results Four core components were identified by the clustering methods. These are labelled obesity, lipids, insulin resistance and blood pressure. The exploratory factor analysis with oblique rotation suggested an overlap of the loadings identified on the insulin resistance and obesity factors. The VARCLUS and matroid analyses separated these components and were able to demonstrate associations between individual risk factors. Conclusions An oblique rotation can be selected to reflect the clinical concept of a single underlying syndrome, however the results are often difficult to interpret. Factor loadings must be considered along with correlations between the factors. The correlated components produced by the VARCLUS and matroid analyses are not overlapped, which allows for a simpler application of the methodologies and interpretation of the results. These techniques encourage consistency in the interpretation whilst remaining faithful to the construct under study.

[1]  Daniel W. Jones,et al.  METABOLIC SYNDROME AND RISK OF CHRONIC KIDNEY DISEASE IN US ADULTS: P 170 , 2004 .

[2]  E. Oda Definition of metabolic syndrome. , 2007, Stroke.

[3]  Avron Spiro,et al.  Are metabolic risk factors one unified syndrome? Modeling the structure of the metabolic syndrome X. , 2003, American journal of epidemiology.

[4]  P. Savage,et al.  Metabolic Syndrome and Cardiovascular Disease in Older People: The Cardiovascular Health Study , 2006, Journal of the American Geriatrics Society.

[5]  Duane T. Wegener,et al.  Evaluating the use of exploratory factor analysis in psychological research. , 1999 .

[6]  Michael R. Anderberg,et al.  Cluster Analysis for Applications , 1973 .

[7]  R. D'Agostino,et al.  Metabolic Syndrome as a Precursor of Cardiovascular Disease and Type 2 Diabetes Mellitus , 2005, Circulation.

[8]  The depiction of linear association by matroids , 1990 .

[9]  Hidekatsu Yanai,et al.  The underlying mechanisms for development of hypertension in the metabolic syndrome , 2008, Nutrition Journal.

[10]  Tom Greene,et al.  Descriptively sufficient subcollections of flats in matroids , 1991, Discret. Math..

[11]  R. MacCallum,et al.  THE APPLICATION OF EXPLORATORY FACTOR ANALYSIS IN APPLIED PSYCHOLOGY: A CRITICAL REVIEW AND ANALYSIS , 1986 .

[12]  F. Floyd,et al.  Factor analysis in the development and refinement of clinical assessment instruments. , 1995 .

[13]  J. Shaw,et al.  Metabolic syndrome—a new world‐wide definition. A Consensus Statement from the International Diabetes Federation , 2006, Diabetic medicine : a journal of the British Diabetic Association.

[14]  N. Schneiderman,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. , 2006, Annals of epidemiology.

[15]  D. Kendall,et al.  Is the metabolic syndrome a real clinical entity and should it receive drug treatment? , 2006, Current diabetes reports.

[16]  D L Streiner,et al.  Figuring Out Factors: The Use and Misuse of Factor Analysis , 1994, Canadian journal of psychiatry. Revue canadienne de psychiatrie.

[17]  J. Shaw,et al.  A Single Factor Underlies the Metabolic Syndrome: A Confirmatory Factor Analysis , 2006, Diabetes Care.

[18]  S. Hahn The metabolic syndrome. , 2009 .

[19]  Jing Chen,et al.  The Metabolic Syndrome and Chronic Kidney Disease in U.S. Adults , 2004, Annals of Internal Medicine.

[20]  R. Kahn Metabolic Syndrome: Is It a Syndrome? Does It Matter? , 2007, Circulation.

[21]  S. Kashyap,et al.  Metabolic syndrome and kidney disease: a systematic review and meta-analysis. , 2011, Clinical journal of the American Society of Nephrology : CJASN.

[22]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[23]  Laura M. Stapleton,et al.  Re: "(Mis)use of factor analysis in the study of insulin resistance syndrome". , 2005, American journal of epidemiology.

[24]  E. Ford,et al.  A comparison of the prevalence of the metabolic syndrome using two proposed definitions. , 2003, Diabetes care.

[25]  R. Niaura,et al.  Hostility and the Metabolic Syndrome in Older Males: The Normative Aging Study , 2000, Psychosomatic medicine.

[26]  G. Chertow,et al.  The metabolic syndrome and chronic kidney disease , 2006, Current opinion in nephrology and hypertension.

[27]  Paul Zimmet,et al.  The metabolic syndrome—a new worldwide definition , 2005, The Lancet.

[28]  H. Minuk,et al.  Metabolic syndrome. , 2005, Journal of insurance medicine.

[29]  Heng Tao Shen,et al.  Principal Component Analysis , 2009, Encyclopedia of Biometrics.

[30]  M. Monami,et al.  How many components for the metabolic syndrome? Results of exploratory factor analysis in the FIBAR study. , 2007, Nutrition, metabolism, and cardiovascular diseases : NMCD.

[31]  D. Lawlor,et al.  (Mis)use of factor analysis in the study of insulin resistance syndrome. , 2005, American journal of epidemiology.

[32]  C. Lafortuna,et al.  Factor analysis of metabolic syndrome components in obese women. , 2008, Nutrition, metabolism, and cardiovascular diseases : NMCD.

[33]  Bernhard Ganter,et al.  Two conjectures of Demetrovics, Furedi, and Katona, concerning partitions , 1991 .

[34]  Edward E. Cureton,et al.  The weighted varimax rotation and the promax rotation , 1975 .

[35]  Victor M Montori,et al.  Metabolic syndrome and risk of incident cardiovascular events and death: a systematic review and meta-analysis of longitudinal studies. , 2007, Journal of the American College of Cardiology.

[36]  Ali S. Hadi,et al.  Finding Groups in Data: An Introduction to Chster Analysis , 1991 .

[37]  R. Darlington,et al.  Factor Analysis , 2008 .

[38]  H. Kaiser The Application of Electronic Computers to Factor Analysis , 1960 .

[39]  Laura M. Stapleton,et al.  Evaluation and comparison of models of metabolic syndrome using confirmatory factor analysis , 2006, European Journal of Epidemiology.

[40]  Jason W. Osborne,et al.  Best practices in exploratory factor analysis: four recommendations for getting the most from your analysis. , 2005 .

[41]  L. Guttman Some necessary conditions for common-factor analysis , 1954 .

[42]  E. Oda The Metabolic Syndrome (Emperor) Wears No Clothes , 2006, Diabetes Care.

[43]  Claude Lenfant,et al.  Definition of Metabolic Syndrome: Report of the National Heart, Lung, and Blood Institute/American Heart Association Conference on Scientific Issues Related to Definition , 2004, Arteriosclerosis, thrombosis, and vascular biology.

[44]  Dennis Child,et al.  The essentials of factor analysis , 1970 .

[45]  L. L. Thurstone,et al.  Experimental study of simple structure , 1940 .

[46]  H. Kaiser The varimax criterion for analytic rotation in factor analysis , 1958 .