title: A comparison of different approaches to unravel the latent structure within metabolic syndrome.
abstract: 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.
authors.affiliation: Division of Biostatistics, Centre for Epidemiology and Biostatistics, University of Leeds, Leeds, United Kingdom. cen5aw@leeds.ac.uk
authors.forename: Andrew
authors.initials: A
authors.lastname: Woolston
authors.forename: Yu-Kang
authors.initials: YK
authors.lastname: Tu
authors.forename: Paul D
authors.initials: PD
authors.lastname: Baxter
authors.forename: Mark S
authors.initials: MS
authors.lastname: Gilthorpe
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D016000:Cluster Analysis; D003430:Cross-Sectional Studies; D003627:Data Interpretation, Statistical; D005163:Factor Analysis, Statistical; D006801:Humans; D008297:Male; D024821:Metabolic Syndrome / Q000378:metabolism* / Q000473:pathology; D008875:Middle Aged; D025341:Principal Component Analysis; D012307:Risk Factors; D055815:Young Adult