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

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  • Andrew Woolston
  • Yu-Kang Tu
  • Paul D Baxter
  • Mark S Gilthorpe

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.

Suggested Citation

  • Andrew Woolston & Yu-Kang Tu & Paul D Baxter & Mark S Gilthorpe, 2012. "A Comparison of Different Approaches to Unravel the Latent Structure within Metabolic Syndrome," PLOS ONE, Public Library of Science, vol. 7(4), pages 1-9, April.
  • Handle: RePEc:plo:pone00:0034410
    DOI: 10.1371/journal.pone.0034410
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    Cited by:

    1. Ivan Soldatovic & Rade Vukovic & Djordje Culafic & Milan Gajic & Vesna Dimitrijevic-Sreckovic, 2016. "siMS Score: Simple Method for Quantifying Metabolic Syndrome," PLOS ONE, Public Library of Science, vol. 11(1), pages 1-10, January.
    2. Melkaye G Melka & Michal Abrahamowicz & Gabriel T Leonard & Michel Perron & Louis Richer & Suzanne Veillette & Daniel Gaudet & Tomáš Paus & Zdenka Pausova, 2013. "Clustering of the Metabolic Syndrome Components in Adolescence: Role of Visceral Fat," PLOS ONE, Public Library of Science, vol. 8(12), pages 1-7, December.

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