# Pathophysiological Domains Underlying the Metabolic Syndrome: An Alternative Factor Analytic Strategy

**Authors:** Carel F. W. Peeters, James Dziura, Floryt van Wesel  
**arXiv:** [1609.02313](https://arxiv.org/abs/1609.02313)  
**Date:** 2016-09-08  
**Summary page:** https://www.summarizepaper.com/en/paper/1609.02313v1/

## Key points

- Purpose of the study: Review factor analytic efforts in the study of metabolic syndrome (MBS) and propose an alternative factor analytic strategy
- Wide variation in results obtained from different studies due to misapplication of factor analysis (FA)
- Proposed strategy consists of four steps to address weaknesses in FA application
- Reanalysis of high-profile MBS dataset reveals more parsimonious constellation of pathophysiological domains underlying phenotypic expressions of MBS compared to original analysis and other analyses
- Findings emphasize correlated factors related to impaired glucose metabolism and impaired lipid metabolism
- Pathophysiological domains underlying MBS driven by multiple interrelated metabolic impairments, suggesting multifactorial etiology for MBS
- Misusage of FA in MBS research highlighted, alternative factor analytic strategy proposed for more accurate insights into pathophysiological domains underlying MBS
- Findings provide evidence for further exploration into understanding how different metabolic impairments interact to cause or contribute to MBS development and progression.

## Summary

The purpose of this study is to review the factor analytic efforts in the study of metabolic syndrome (MBS) and propose an alternative factor analytic strategy. Factor analysis (FA) has become a common tool in MBS research, but there is a wide variation in the results obtained from different studies, which can be attributed to misapplication of FA. The authors aim to address this issue by proposing a new factor analytic strategy consisting of four steps that confront weaknesses in the application of the FA model. The proposed strategy focuses on separating dimensionality and pattern selection explicitly and evaluating competing inequality-constrained loading patterns directly. To demonstrate the effectiveness of their approach, the authors reanalyze a high-profile MBS dataset with anthropometric measurements on overweight children and adolescents. The results of the reanalysis reveal a more parsimonious constellation of pathophysiological domains underlying phenotypic expressions of MBS compared to the original analysis and other analyses. Specifically, the findings emphasize correlated factors related to impaired glucose metabolism and impaired lipid metabolism. Based on these results, it can be inferred that pathophysiological domains underlying phenotypic expressions of MBS are driven by multiple interrelated metabolic impairments. This suggests the possible existence of a multifactorial etiology for MBS. In conclusion, this study highlights the misusage of FA in MBS research and proposes an alternative factor analytic strategy that provides more accurate insights into the pathophysiological domains underlying MBS. The findings support the idea that MBS has a multifactorial etiology involving various interconnected metabolic impairments. Thus, this study provides valuable evidence for further exploration into understanding how different metabolic impairments interact to cause or contribute to MBS development and progression.