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4.3.5.1 Introduction

Although the clustering of metabolic abnormalities associated with insulin resistance has been proposed as an independent cardiovascular risk factor the complex interrelationships that exist between variables limits the use of conventional statistical techniques in determining the biological relevance of associations. As demonstrated in section 4.3.4, factors such as inflammation are correlated with vascular function but are confounded by co-variance with other risk factors, such as measures o f obesity. Factor analysis, however, is a technique that allows inclusion of interrelated variables in the statistical model and has been applied to investigate clustering of cardiovascular risk factors (Donahue et al, 1997; Lempiainen et al,

1999; Meigs et al, 1997). Using this technique Lempiainen (1999) and colleagues identified several factors that were thought to represent insulin resistance, glucose intolerance and hypertension and have demonstrated a significant association between a number of these factors and subsequent cardiovascular disease. However, none o f these studies have included inflammatory and haemostatic indices and the impact of this cluster of metabolic abnormalities on vascular physiology at an earlier stage in the disease process has not been studied.

In this study factor analysis was used to identify major components of the MS, classical cardiovascular risk factors, inflammatory and haemostatic mediators and determine the relationships between these factors and vascular function.

4.3.5.2 Statistical methods

Factor analysis attempts to identify underlying variables, or ‘factors’, that explain the pattern of correlations within a set of observed variables (Kleinbaum et al, 1988; Stevens, 1986). It is often used in data reduction, by identifying a small number of factors that explain most of the variance observed in a much larger number of manifest variables (Figure 4.3.7). Factor analysis can also be used to generate hypotheses regarding causal mechanisms or to screen variables for subsequent analysis (for example, to identify colinearity prior to a linear regression analysis). Factor analysis is a three-step process as outlined below.

Interrelated variables Uncorrelated factors Interpretation

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TGs H D IW H

IL6

Figure 4.3.7 Factorial analysis; schematic diagram

4.3.5.2.1 Principal component analysis

Principal component analysis is a factor extraction method used to form uncorrelated linear combinations of the observed variables. The first principal component is the combination of variables that accounts for the most variance in the dataset. Successive components explain progressively smaller portions of the variance and are all uncorrelated with each other. Principal component analysis is used to obtain the initial factor solution. Generally only components with an eigenvalue (sum o f the squared factor loadings which represents the amount of variance attributable to each component) greater than 1.0 are retained in the analysis for rotation.

4.3.5.2.2 Rotation o f the principal components

Rotation is a general method for making a factor solution easier to interpret. There are several different methods for rotation. Varimax rotation is an orthogonal rotation method that minimizes the number of variables that have high loadings on each factor, thus simplifying the interpretation of the factors and maintains the independence between the factors. Once rotated principal components are referred to as factors and the amount of variance accounted for by each factor is recalculated.

4.3.5.2 3 Interpretation o f factors.

Interpretation involves assessing which factors load high on a particular factor and naming the factor accordingly. Factor loadings, equivalent to a Pearson’s correlation coefficient between the variable and the factor greater than 0.40 are considered significant, though loadings greater than or equal to 0.30 should also be taken into account (Stevens, 1986). Variables may load significantly on more than one factor and these might represent biologically relevant links between physiological processes.

In this study, analyses were performed in men and women separately and then in the whole group. Age, parameters of glucose tolerance and insulin resistance, obesity, blood pressure, lipid levels and inflammatory and haemostatic variables were the initial variables. For each analysis the factor scores were saved as a separate variable and then multiple regression analyses were used to explore which factors determined the main dependent variables of FMD, carotid artery distensibility and IMT. Standardised regression coefficients were used to allow comparison between variables.

4.3 5.3 Results

One hundred and forty two subjects (68 males, 74 women) had a complete dataset and were included in the analysis.

4.3.5.3.1 Principal-component analyses

Principal component analyses reduced the 20 interrelated variables to 4 newly defined uncorrelated factors. Consistent with previous reports (Edwards et al, 1994; Lempiainen et al, 1999; Meigs et al, 1997), factor 1 was a distinct factor which is likely to represent the central metabolic syndrome, was identified. This factor contained positive loadings for fasting insulin level, BMI, WHR and triglycerides and a negative loading for HDL cholesterol level. In addition a significant positive loading for PAIl level was found in both men and women consistent with it being a legitimate part of the MS. In women, levels of 1L6, CRP and SAA also loaded significantly on factor 1, whilst in men the loading for CRP was only of borderline significance. However, in both men and women a separate inflammatory factor

(factor 3 in men and factor 4 in women) with positive loadings for IL6, CRP, SAA and fibrinogen was apparent. Consistent with the study of Meigs et al, (1997) separate factors representing blood pressure (factor 4 in men and factor 2 in women) were found and these were linked to the central metabolic syndrome by co­ associations with PAIl. In men a separate factor for glucose tolerance (factor 2) was found as previously reported (Meigs et al, 1997) whilst in women measures of glucose tolerance loaded on factor 1. In women a separate factor with high loadings for lipid levels was apparent (Factor 3). These four factors accounted for 57% and 63% of the total variance in men and women respectively.

A similar pattern of loadings was found when men and women were considered together. Factor 1 was interpreted as representing the MS, factor 2 as an inflammatory factor, factor 3 as a lipid factor and factor 4 as a blood pressure factor. (Table 4.3.13).