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

TL;DR

This paper reviews the use of factor analysis in metabolic syndrome research, highlights common misapplications, and proposes an improved strategy that reveals a more parsimonious set of underlying pathophysiological domains.

Contribution

It introduces a novel, explicit factor analytic strategy that separates dimensionality from pattern selection and applies it to reanalyze metabolic syndrome data.

Findings

Reanalysis shows fewer, more coherent pathophysiological domains.

Correlated factors of glucose and lipid metabolism impairments are identified.

Results suggest a multifactorial etiology of metabolic syndrome.

Abstract

Purpose: Factor analysis (FA) has become part and parcel in metabolic syndrome (MBS) research. Both exploration- and confirmation-driven factor analyzes are rampant. However, factor analytic results on MBS differ widely. A situation that is at least in part attributable to misapplication of FA. Here, our purpose is (i) to review factor analytic efforts in the study of MBS with emphasis on misusage of the FA model and (ii) to propose an alternative factor analytic strategy. Methods: The proposed factor analytic strategy consists of four steps and confronts weaknesses in application of the FA model. At its heart lies the explicit separation of dimensionality and pattern selection as well as the direct evaluation of competing inequality-constrained loading patterns. A high-profile MBS data set with anthropometric measurements on overweight children and adolescents is reanalyzed using this strategy. Results: The reanalysis implied a more parsimonious constellation of pathophysiological domains underlying phenotypic expressions of MBS than the original analysis (and many other analyzes). The results emphasize correlated factors of impaired glucose metabolism and impaired lipid metabolism. Conclusions: Pathophysiological domains underlying phenotypic expressions of MBS included in the analysis are driven by multiple interrelated metabolic impairments. These findings indirectly point to the possible existence of a multifactorial aetiology.