1 Center for Behavioral and Preventive Medicine, Brown University and the Miriam Hospital, Providence, RI.
2 Boston University School of Public Health, Boston, MA.
3 Massachusetts Veterans Epidemiology Research and Information Center, Boston Veterans Administration Healthcare System, Boston, MA.
4 University of Memphis Center for Community Health, Memphis, TN.
Received for publication June 24, 2002; accepted for publication November 13, 2002.
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ABSTRACT |
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blood pressure; body weight; factor analysis, statistical; insulin resistance; lipoproteins
Abbreviations: Abbreviation: SD, standard deviation.
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INTRODUCTION |
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Several previous studies used factor analysis, mainly principal component analysis, to examine the clustering among metabolic risk variables (715) and mostly yielded a three- or four-factor structure underlying the defining risk measures (9, 11, 15). These studies have shown that specific risk variables tend to aggregate together, such that serum insulin and glucose concentrations (8, 9, 11, 13, 15), blood lipids (811, 1315), blood pressure (9, 10, 1315), and obesity measures (body mass index and waist/hip ratio) (8, 10, 15) form separate factors.
Despite some consensus (9, 12, 13, 15), discrepancies have also been observed in the number of underlying factors, which ranged from two (10) to seven (15), and in the combinations of variables loading on each factor (8). These discrepancies have posed challenges to interpretation of the factor structure underlying the metabolic syndrome. The apparent inconsistencies in past findings may well stem from the use of principal component factor analysis and its exploratory and subjective nature (16). For example, different findings may arise on the basis of sample selection, variables included in analysis, number of factors extracted, and rotation method used (17, 18). Another issue in past studies involves the use of orthogonal rotation, such as varimax, to obtain "independent" or "uncorrelated" factors (17). However, applying orthogonal rotation may unwarrantedly restrict correlations between factors and derive statistically independent but artificial factors that do not correspond to actual physiologic processes (19). Finally, the results of multiple uncorrelated factors comprising the metabolic syndrome produced by principal component factor analysis studies also seem contradictory to the concept of a syndrome that is thought to unite related symptoms under one disorder entity.
Because of these observed differences and concerns raised by researchers, it has been suggested to use confirmatory factor analysis to examine the structure of metabolic syndrome (17). The confirmatory factor analysis approach provides some advantages and is complementary to the use of principal component factor analysis (20, 21). First, with confirmatory factor analysis, a hypothesized model detailing the relations among variables and factors is specified and subjected to examination, thus permitting a hypothesis-testing rather than a data-driven approach. Past studies, in fact, provide excellent exploratory knowledge on which models can be built and tested with confirmatory factor analysis. Second, questions such as "if there exists a unified syndrome underlying various metabolic disorders" and "if components of this syndrome are correlated" can be empirically examined by testing corresponding models. Third, the stability of the hypothesized factor structure can be tested across subgroups with different characteristics (e.g., age or diagnosis) to ensure its generalizability and practical use in diverse populations (20).
In this study, confirmatory factor analysis was used to evaluate hypothesized models that delineate the clustering among variables characterizing the metabolic syndrome. On the basis of theoretical conceptualization and empirical evidence provided by previous principal component factor analysis studies, the main analysis proposed and tested a hierarchical four-factor model consisting of measures of insulin resistance, obesity, lipids, and blood pressure. We also examined whether empirical data support the hypothesis that there exists a single unifying factor representing the common pathway underlying all four metabolic factors. Lastly, to investigate the robustness of the proposed factor structure, simultaneous subgroup analyses were carried out to detect possible structural differences across younger versus older groups and across individuals with and without cardiovascular disease.
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MATERIALS AND METHODS |
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Procedures
Participants were instructed to refrain from eating or drinking after midnight and to avoid smoking after 8 p.m. the night before examination. The examination included blood pressure measurement, blood work, an anthropometric evaluation, and assessment of health behaviors by standardized questionnaires. The first blood sample was drawn at 8 a.m. to measure fasting insulin and glucose. Another blood sample was taken 2 hours after the participant orally consumed a 100-g glucose load in order to obtain postchallenge insulin and glucose concentrations.
Measures
Ten measures representing the metabolic syndrome were obtained, including fasting insulin, postchallenge insulin, fasting glucose, postchallenge glucose, body mass index, waist/hip ratio, high density lipoprotein cholesterol, triglycerides, systolic blood pressure, and diastolic blood pressure.
Blood lipids
Total cholesterol, high density lipoprotein cholesterol, low density lipoprotein cholesterol, and triglycerides were obtained from analysis of serum samples. Serum cholesterol was assayed enzymatically (SCALVO Diagnostics, Wayne, New Jersey). The high density lipoprotein cholesterol fraction was measured in the supernatant after precipitation of the low density lipoprotein cholesterol and very low density lipoprotein fractions with dextran sulfate and magnesium, using the Abbott Biochromatic Analyzer 100 (Abbott Laboratories, South Pasadena, California). The triglyceride concentration was measured with a Dupont ACA discrete clinical analyzer (Biomedical Products Department, Dupont Company, Wilmington, Delaware).
Fasting and postchallenge serum insulin and glucose
Two blood samples were analyzed to obtain fasting and postchallenge insulin and glucose values. The serum insulin concentration was determined by a solid-phase 125I-labeled radioimmunoassay (Diagnostic Products Corporation, Los Angeles, California). The serum glucose concentration was measured in duplicate on an autoanalyzer by the hexokinase method (23).
Blood pressure
Systolic blood pressure and fifth-phase diastolic blood pressure were measured to the nearest 2 mmHg with a standard mercury sphygmomanometer with a 14-cm cuff. Both left and right arm pressures were recorded with the participant in a sitting position, followed by right arm pressures taken in a supine position and then followed 30 seconds later by a second reading of right arm pressures taken in a standing position. The palpatory method was used to check auscultatory systolic readings. The means of all systolic and diastolic readings were used in analyses.
Anthropometric measures
Weight was measured to the nearest 0.5 pound (0.2268 kg) on a standard hospital scale with the participant dressed in undershorts and socks. It was later converted to kilograms. Height was measured to the nearest 0.1 inch (0.254 cm) with the participant standing in bare feet against a wall, and this value was then converted to meters. Body mass index was computed as kg/m2. Abdomen circumference was measured in centimeters at the level of the umbilicus with the participant standing. Hip circumference was measured in centimeters at the greatest protrusion of the buttocks. The waist/hip ratio was calculated by dividing the abdomen circumference by the hip circumference.
Formulation of the factor structure of the metabolic syndrome
On the basis of theoretical conceptualization, measures belonging to the same physiologic sources, such as glucose metabolism, obesity, lipids, or blood pressure, should share more commonality than do measures from different processes. As reviewed previously, prior research did largely support a three- to four-factor solution with measures divided by their respective categories (715). Therefore, we proposed a hierarchical four-factor model with four first-order factors comprising the major components of the metabolic syndrome (insulin resistance, obesity, lipid, and blood pressure) and a second-order factor reflecting the syndrome itself (figure 1). Accordingly, the insulin resistance factor was defined by fasting insulin, postchallenge insulin, glucose, and postchallenge glucose (8, 11, 13, 15), the obesity factor manifested by body mass index and waist/hip ratio (10), the lipid factor composed of high density lipoprotein cholesterol and triglycerides (8), and the blood pressure factor represented by systolic blood pressure and diastolic blood pressure (9, 10, 13, 15). A higher order factor, the metabolic syndrome, was created to reflect the common process underlying all these risk factors. Finally, residual variance terms specific to each of the measures were specified in the model, which represented the variance not accounted for by the common factors, including measurement errors.
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In summary, there were five latent factors and 10 measured variables in this model (figure 1). The model contained 30 parameters to be estimated, including 14 path coefficients (loadings) between factors and measures and between the first-order and second-order factors, 14 residual variances, and two covariances between residuals. There was an approximate 85:1 case:variable ratio, and there was an approximate 30:1 case:parameter ratio. Both were excellent for conducting confirmatory factor analysis.
We also tested two alternative metabolic syndrome factor structures to evaluate their goodness of fit. One assumed a common factor that directly underlies all measured metabolic risk variables without the presence of separate insulin resistance, obesity, lipid, and blood pressure factors (figure 2). The other postulated that the four major metabolic factors were correlated, with the interfactor correlations representing the common association among the components of metabolic syndrome (figure 3).
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The model was evaluated in three ways. First, the 2 test was used to evaluate the congruency between the hypothesized model and empirical data from the sample. Second, because
2 tests are highly sensitive to sample size and often result in statistically significant but empirically trivial differences, three model fit indices were also used to evaluate the model. These were comparative fit index, average absolute standardized residuals, and root mean square error of approximation. Finally, the residual matrix was inspected for any large deviation from zero that was indicative of discrepancy between parameter estimates and empirical data.
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RESULTS |
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Factor structure of the metabolic syndrome
The results showed that the metabolic syndrome could be summarized by an overarching factors subsuming four component factors, each sharing varying degrees of associations with the overall common factor. Each component factor was further defined by its respective metabolic risk variables. As illustrated in figure 1, the metabolic syndrome factor was strongly represented by the insulin resistance factor (standardized coefficient as factor loading = 0.83, p < 0.01) and the obesity factor (loading = 0.80, p < 0.01), moderately by the lipid factor (loading = 0.59, p < 0.01), and only modestly by the blood pressure factor (loading = 0.33, p < 0.01). In other words, the metabolic syndrome factor explained 69 percent of the variance in the insulin resistance factor, 64 percent for the obesity factor, 35 percent for the lipid factor, and 11 percent for the blood pressure factor.
As for each component factor, the insulin resistance factor was defined mainly by fasting insulin and postchallenge insulin and, to a lesser degree, by glucose and postchallenge glucose (loadings = 0.83, 0.78, 0.34, and 0.37, respectively; all p < 0.01). The obesity factor was well represented by the body mass index and the waist/hip ratio (loadings = 0.77 and 0.60; both p < 0.01). The lipid factor was reflected by triglycerides and high density lipoprotein cholesterol (loadings = 0.78 and 0.60; both p < 0.01). Finally, the blood pressure factor was equally manifested by systolic blood pressure and diastolic blood pressure (loadings = 0.75 and 0.76; both p < 0.01).
Alternative models
The test of single-factor structure (figure 2) revealed that this model performed poorly and received little empirical support, with 2 = 631.44 (df = 32, p < 0.0001), the comparative fit index = 0.74, and the root mean square error approximation = 0.15. On the other hand, the correlated four-factor model appeared comparable with the first model tested, with
2 = 101.75 (df = 27, p < 0.01), the comparative fit index = 0.97, and the root mean square error approximation = 0.06. This result was not surprising because the common association among factors in this model was represented by six paired correlations instead of one second-order factor. In this model, varying degrees of interfactor correlations were observed. The insulin resistance factor was strongly correlated with the obesity factor (r = 0.66) and moderately with the lipid factor (r = 0.49). The obesity factor also had a moderately high association with the lipid factor (r = 0.48). The blood pressure factor, although significantly correlated with others, appeared to have weaker relations with the insulin resistance (r = 0.28), obesity (r = 0.26), and lipid (r = 0.17) factors.
The use of correlated factors is similar to creating a second-order factor to represent the commonality among factors. Constructing a second-order factor, however, allows the testing of the single-factor hypothesis directly. In fact, the interfactor correlations in figure 3 are almost identical to the products of the two loadings of corresponding first-order factors on the second-order factor. For example, the interfactor correlation between the insulin resistance and obesity factors in figure 3 is 0.66, which is the same as the product of loadings of the insulin resistance factor = 0.83 and the obesity factor = 0.80 on the metabolic syndrome factor in figure 1.
Subgroup analyses
Simultaneous multigroup analyses (20) were conducted to test the stability and generalizability of the proposed hierarchical four-factor structure and to explore possible differences across subgroups. First, the structure was examined across groups of individuals with or without documented cardiovascular disease, with cardiovascular disease defined by diagnosed hypertension, angina pectoris, ischemia, coronary heart disease, and myocardial infarction. Second, the model was examined across younger (less than 60 years of age) and older participants.
In a comparison of individuals with and without cardiovascular disease, the factor structure was identical except for the strength of the path between the body mass index and the obesity factor (2diff = 6.71, df = 1, p < 0.01; final model
2 = 141.00, df = 63, n = 847, p < 0.01, comparative fit index = 0.97, root mean square error approximation = 0.04). It was found that body mass index had a slightly stronger association with the obesity factor among individuals with cardiovascular disease (loading = 0.79, p < 0.01) than among those without cardiovascular disease (loading = 0.75, p < 0.01). Analyses for age groups also revealed only a small difference (
2diff = 6.58, df = 1, p < 0.01; final model
2 = 134.46, df = 62, n = 847, p < 0.01, comparative fit index = 0.97, root mean square error approximation = 0.04), such that the path between triglycerides and the lipid factor was stronger for younger (loading = 0.84, p < 0.01) than for older (loading = 0.71, p < 0.01) individuals. No subgroup differences were found in the paths between the metabolic syndrome factor and its component factors, indicating that proposed structure was highly stable across subgroups.
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DISCUSSION |
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Varying degrees of association were observed between the metabolic syndrome factor and its four component factors. Insulin resistance and obesity appeared to be the two most essential features of the syndrome, followed by hyperlipidemia, and, to a lesser extent, hypertension. The strong association between insulin resistance and obesity is consistent with the observation in several previous studies in which insulin resistance and obesity loaded on the same factor (810, 12, 13, 15). The consistency of this relation across studies is compelling and supports the notion of a synergistic physiologic process or common pathway underlying impaired glucose metabolism and obesity. There is evidence suggesting that visceral obesity may be linked to decreased glucose tolerance with hyperinsulinemia, leading to a reduction in insulin-mediated glucose uptake (2, 26, 27). It has also been suggested that adipose cells, via their tendency to increase the production of free fatty acids, may slow down glucose uptake, stimulate hepatic glucose production, and suppress pancreatic insulin secretion (28). These interrelated mechanisms may explain the strong association between insulin resistance and obesity and may constitute the basic pathogenetic pathways of the metabolic syndrome for coronary heart disease and diabetes.
Lipid measures had a moderate contribution to the metabolic syndrome factor and clear associations with other components of the syndrome including insulin resistance and obesity. The moderate associations suggest that mechanisms relating lipids to insulin resistance and obesity are more complex and may involve more intermediate processes. It is well known that free fatty acids play an important role in lipid metabolism and are a prominent source of triglycerides stored in adipose tissue (2, 29). In patients exhibiting metabolic disorders, particularly those with insulin resistance, the suppression of free fatty acid release from adipose tissue is impaired, providing an important ingredient for the liver to synthesize triglycerides and very low density lipoprotein (30). In turn, increased lipid production by the liver is associated with an overall increase in total glucose production and may place additional stress on beta-cell function and insulin production. These interrelated mechanisms among insulin resistance, lipids, and glucose production, therefore, provide support for our model, which indicates that metabolic syndrome risk factors work in synergy and, ultimately, come together to influence the etiology of chronic diseases. The mechanisms linking lipid metabolism, obesity, and insulin dependence, nevertheless, are still controversial and require further research (2).
Although blood pressure was evidenced to be an element of the metabolic syndrome in our analyses, the strength of this relation was weaker than with other components. This is consistent with prior studies reporting tenuous relations between blood pressure and other risk variables (10, 18). Research has suggested that insulin-related metabolic abnormality (insulin resistance and hyperinsulinemia) may give rise to the pathogenesis of hypertension via a number of mechanisms, including increased renal sodium and water retention, plasma noradrenaline, and sympathetic nervous system activity (3133). The association between hypertension and the metabolic syndrome, however, has been challenged (2, 14, 34), and such an association is not always evident in African Americans and Pima Indians (35). Our results showed that the majority of the variance in blood pressure was not attributable to the metabolic syndrome, indicating that blood pressure may be related to the syndrome only secondarily to other mechanisms.
Furthermore, the results are largely consistent with the National Cholesterol Education Programs guideline for the metabolic syndrome (36). Our analyses further suggest that the body mass index may be a criterion comparable with waist circumference and that blood pressure shows a smaller association with the syndrome than others. Triglycerides and high density lipoprotein cholesterol are represented as part of the same risk component. Thus, the inclusion of both as criteria may be redundant. For determination of the exact number of risk factors required for diagnosing the metabolic syndrome, more prospective research is needed to examine the associations between each component with diabetes and cardiovascular disease endpoints. Nonetheless, our findings strongly indicate that insulin resistance, obesity, and an unfavorable lipid profile are essential features and should be represented in the diagnostic guideline.
There are some limitations to the present study that may illuminate directions for future research. First, participants in this study were primarily older, Caucasian males, which may limit the generalizability of the findings. Given that the prevalence and course of cardiovascular disease differ across genders and ethnic groups (37), it is plausible that the factor structure of metabolic syndrome may manifest, at least partially, as a function of sample characteristics. Future research should corroborate if the same structure can be replicated among women, youths, and minority members. Furthermore, in this study, we included only measures that are most often associated with the metabolic syndrome. Recent studies have expanded the concept of the metabolic syndrome to include other physiologic variables such as uric acid, inflammation, procoagulation, and vitamin K-dependent protein (10, 15). Future studies should investigate the influence of these elements on the factor structure of the syndrome.
Another limitation involved the use of a cross-sectional design to examine the relations among metabolic risk variables and factors. Although this is similar to most previous studies examining the factor structure of the metabolic syndrome, cross-sectional analyses represent only a snapshot of a complicated physiologic system at a single point in time. These risk variables and factors, nevertheless, are likely to be linked by a series of progressive and reciprocal mechanisms. Future research should examine the longitudinal relations among metabolic risk factors to advance our understanding of the development of coronary heart disease and diabetes. For example, it would be interesting to investigate whether obesity precedes hyperinsulinemia, which then leads to dyslipidemia, hypertension, and eventually coronary heart disease, or if some other etiology exists. Applying a modeling approach with a longitudinal design can be instrumental to testing these hypotheses.
Finally, there is a growing body of literature examining the relation between the metabolic syndrome and psychosocial characteristics (38, 39). Another future direction is to explore the impact of psychosocial characteristics (e.g., socioeconomic status, psychologic traits, lifestyle behaviors) on the syndrome. To better plan for targeted prevention and treatment in the general population, a better understanding of which psychosocial groups are at higher risk for the development and exacerbation of the metabolic syndrome will contribute to the development of innovative biopsychosocial approaches that maximize the effectiveness of public health interventions.
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ACKNOWLEDGMENTS |
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NOTES |
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REFERENCES |
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