RE: "(MIS)USE OF FACTOR ANALYSIS IN THE STUDY OF INSULIN RESISTANCE SYNDROME"

Weihong Tang, James S. Pankow and Donna K. Arnett

Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN 55454

In a recent Journal article, Lawlor et al. (1Go) commented on the use of factor analysis in the study of insulin resistance syndrome or metabolic syndrome. The article highlighted a number of important analytical issues related to factor analysis, and it favored an approach driven by biology, with which we concur. However, we have some concerns with a few of the authors' statements.

First, the authors (1Go) stated that the weak correlation between hypertension and insulin resistance syndrome or identification of a separate blood pressure factor reflects the fact that both systolic and diastolic blood pressures are often included in factor models and are highly collinear. We recently conducted a maximum-likelihood-based factor analysis in the National Heart, Lung, and Blood Institute Family Heart Study (2Go). In the initial factor analysis model, we included 13 variables based on their biologic relevance to the syndrome, and we excluded variables that had a factor loading of less than 0.4 with the primary factor (named the metabolic syndrome factor) in a stepwise manner. In intermediate models that included both systolic and diastolic blood pressures or systolic blood pressure only, blood pressure showed consistently weaker correlations with the metabolic syndrome factor when compared with obesity, dyslipidemia, and insulin resistance variables. Several other studies that included only one blood pressure measure in their factor models also observed a weak correlation between blood pressure and the obesity/insulin factor (3Go–5Go). This observation is in line with results from an epidemiologic study that found a weaker association between hypertension and insulin resistance than between hypertension and dyslipidemia (6Go). In the Family Heart Study, cases whose metabolic syndrome was defined by the National Cholesterol Education Program Adult Treatment Panel III criteria (7Go) were less likely to have hypertension (67 percent) than abdominal obesity (87 percent), low high density lipoprotein cholesterol (76 percent), or high triglycerides (82 percent). Pathophysiologically, components of the metabolic syndrome, including hypertension, are likely influenced by common as well as unique mechanisms. Therefore, it is possible that, in some populations, the mechanisms unique to hypertension exert a stronger influence than those common to metabolic syndrome–related variables, leading to a weaker correlation between hypertension and the metabolic syndrome.

Second, although we agree with the authors (1Go) that the focus of the field should move to confirmatory factor analysis, we do not think that explanatory factor analysis should be abandoned. Explanatory factor analysis has its value in hypothesis generating and is needed in studies in which novel risk factors or a different population are investigated, especially when the existing model or theory does not fit the data from the new population. Such a discrepancy could be caused by differences in genetic backgrounds or environmental factors between populations. In this case, explanatory factor analysis can provide useful data for constructing new models or updating theories. The usefulness of explanatory factor analysis has been shown in a confirmatory factor analysis study in which the construction of a hierarchical four-factor model was based on theory and findings from previous explanatory factor analysis studies (8Go). Recently published studies that consider novel risk factors such as plasminogen activator inhibitor-1 antigen and/or C-reactive protein have added to understanding the underlying structure of the metabolic syndrome (9Go, 10Go).

Finally, although the National Cholesterol Education Program Adult Treatment Panel III criteria for the metabolic syndrome are effective in categorizing persons for treatment purposes, the criteria ignore the quantitative nature of these traits and thus may not be most suitable in certain situations, such as genetic linkage analyses. Factor analysis characterizes the underlying multivariate correlation structure of the metabolic syndrome and provides quantitative measures for the syndrome. Two genetic linkage studies that incorporated explanatory factor analysis identified common genomic regions that may contain genes influencing multiple metabolic syndrome–related traits (2Go, 11Go). These studies highlight the biologic, and analytical, relevance of the factor analysis approach to complex syndromes.

ACKNOWLEDGMENTS

Dr. Tang is supported in part by National Heart, Lung, and Blood Institute training grant T32-HL07972.

Conflict of interest: none declared.

References

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