American Journal of Epidemiology Vol. 153, No. 7 : 717-718
Copyright © 2001 by The Johns Hopkins University School of Hygiene and Public Health
LETTERS TO THE EDITOR |
RE: "CLUSTERING OF PROCOAGULATION, INFLAMMATION, AND FIBRINOLYSIS VARIABLES WITH METABOLIC FACTORS IN INSULIN RESISTANCE SYNDROME"
Department of Nutrition Harvard School of Public Health Boston, MA 02115
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INTRODUCTION |
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Sakkinen et al. (1
) derived multiple factors of insulin resistance syndrome through factor analysis of 21 metabolic and hemostatic variables. They used orthogonal transformation (varimax rotation in SAS computer software) to rotate the factors in order to achieve clearer interpretability. Thus, the derived factors including body mass, insulin/glucose, lipids, blood pressure, and so on are "uncorrelated." While their approach is statistically sound, the logic is not consistent with the theory of insulin resistance syndrome, which postulates a common underlying biologic process for the close interrelation among obesity, hyperinsulinemia, glucose intolerance, dyslipidemia, and other metabolic disorders (2). There are substantial data that these components are all intercorrelated, both statistically and biologically. Thus, the "uncorrelated" factors identified from this study and several previous analyses may merely reflect a statistical artifact rather than a biologic reality.
To test the clustering of the components as well as a unified mechanism that underlies various metabolic abnormalities, an alternative rotation method, an oblique rotation (promax rotation in SAS computer software), can be used to produce correlated factors (3). Then second-order factor(s) can be derived by factor analyzing the correlation matrix of the common factors obtained from the first step. This can also be achieved by confirmatory factor analysis (4), which is a theory-testing method as opposed to a data-driven method like explanatory factor analysis. This model-fitting procedure allows one to test the ability of the hypothesized factor structure to account for the observed covariance by examining the overall fit of the model. The analyses can be carried out using SAS PROC CALIS (5) or specialized computer programs, such as LISREL 8 (6). In addition, the confirmatory factor analysis procedure allows for a test of the equality of factor structure between different groups (e.g., male and female) by comparing the model fit of competing models with and without certain constraints on factor loadings (6).
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Sakkinen PA, Wahl P, Cushman M, et al. Clustering of procoagulation, inflammation, and fibrinolysis variables with metabolic factors in insulin resistance syndrome. Am J Epidemiol 2000;152:897–907.
[Abstract/Free Full Text] - Reaven GM. Role of insulin resistance in human disease. Diabetes 1988;37:1595–607.[Abstract]
- SAS Institute, Inc. SAS procedures guide, version 6.11. Cary, NC: SAS Institute, Inc, 1996.
- Long JS. Confirmatory factor analysis: a preface to LISREL. Beverly Hills, CA: Sage Publications, 1983.
- Hatcher L. A step-by-step approach to using the SAS system for factor analysis and structural equation modeling. Cary, NC: SAS Institute, Inc, 1994.
- Joreskog KG, Sorbom D. LISREL 8: structural equation modeling with the SIMPLIS command language. Chicago, IL: Scientific Software International, 1993.
TWO AUTHORS REPLY
Department of Pathology College of Medicine University of Vermont Colchester, VT 05446
Departments of Pathology and of Biochemistry University of Vermont Burlington, VT 05405-0068
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INTRODUCTION |
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We thank Dr. Hu for his letter (1
) regarding our recently published article on the clustering of metabolic and hemostatic risk markers in the insulin resistance syndrome (2), and we agree that the methods we and some others (3–6) have used were essentially exploratory and not confirmatory. Hu maintains that our use of orthogonal transformation in the factor analyses contradicts the current theory of interrelation among the several facets attributed to the insulin resistance syndrome. As was indicated in our article (2), however, four of the 21 measured risk variables (fasting insulin, triglycerides, factor IXc, and fibrin fragment D-dimer) each clustered with two of the seven factors we identified, suggesting points of pathophysiologic commonality among the statistically uncorrelated factors. As Meigs noted in his commentary, "[t]he pattern of overlap provides insight into the underlying structure of the syndrome" (7, p. 908). For example, the overlapping of fibrin fragment D-dimer on both the procoagulation and inflammation factors suggests a nexus between inflammation and thrombin activity.
Our goal in using explanatory factor analysis was to further elucidate relations among putative hemostatic and metabolic components of the insulin resistance syndrome. We did not engage in statistical hypothesis testing of the sort proposed by Hu in his discussion of confirmatory factor analysis (1); rather, we sought to guide subsequent investigations by attempting to discern risk marker relations that can be obscured by the many intercorrelations among the markers. Fundamentally, we believe that the current, limited understanding of the pathophysiology of the insulin resistance syndrome warrants further exploration of this type.
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Hu FB. Re: "Clustering of procoagulation, inflammation, and fibrinolysis variables with metabolic factors in insulin resistance syndrome." (Letter). Am J Epidemiol 2001;153:717.
[Free Full Text] - Sakkinen PA, Wahl P, Cushman M, et al. Clustering of procoagulation, inflammation, and fibrinolysis variables with metabolic factors in insulin resistance syndrome. Am J Epidemiol 2000;152:897–907.
- Donahue RP, Bean JA, Donahue RD, et al. Does insulin resistance unite the separate components of the insulin resistance syndrome? Evidence from the Miami Community Health Study. Diabetes 1997;17:2413–17.
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Edwards KL, Burchfiel CM, Sharp DS, et al. Factors of the insulin resistance syndrome in nondiabetic and diabetic elderly Japanese-American men. Am J Epidemiol 1998;147:441–7.
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Gray RS, Fabsitz RR, Cowan LD, et al. Risk factor clustering in the insulin resistance syndrome. The Strong Heart Study. Am J Epidemiol 1998;148:869–78.
[Abstract/Free Full Text] -
Chen W, Srinivasan SR, Elkasabany A, et al. Cardiovascular risk factors clustering features of insulin resistance syndrome (syndrome X) in a biracial (Black-White) population of children, adolescents, and young adults: the Bogalusa Heart Study. Am J Epidemiol 1999;150:667–74.
[Abstract/Free Full Text] -
Meigs JB. Invited commentary: insulin resistance syndrome? Syndrome X? Multiple metabolic syndrome? A syndrome at all? Factor analysis reveals patterns in the fabric of correlated metabolic risk factors. Am J Epidemiol 2000;152:908–11.
[Abstract/Free Full Text]
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