The central roles of XXXX -associated dyslipidaemia, endothelial activation and cytokines in the Metabolic Syndrome--an analysis by structural equation modelling.

@entity1 @entity32 @entity650 @entity101 @entity28 @entity2299 @entity310 @entity209
HYPOTHESIS: The multi-faceted components of the @entity209 now include markers of @entity32 and endothelial activation. Despite this growing body of epidemiological data, standard statistical methods fail to evaluate the nature of these associations adequately. In this pilot study, we hypothesize that @entity28 may lead to endothelial activation which is in part mediated by dyslipidaemia and proinflammatory cytokines. These factors interact to give rise to hyperinsulinaemia, @entity101 and an anti-fibrinolytic state. To test this hypothesis, we used confirmatory factor analysis and structural equation modelling to fit these data to a model designed on theoretical grounds. METHODS: @entity209 variables, cytokines ( @entity650 and @entity310 ), markers of @entity32 and endothelial activation were measured in 107 Caucasian non-diabetic subjects aged 40-75 y. Using confirmatory factor analysis, we identified six factors to represent composite measurements of blood pressure, @entity28 , dyslipidaemia, hyperinsulinaemia, endothelial activation and the anti-fibrinolytic state. We fitted these variables to two separate models, one using @entity650 and the other @entity310 as the cytokine, and examined the inter-relationships (path analysis) amongst these variables, based on the above hypothesis. RESULTS: @entity1 were centrally more @entity28 and had increased markers of endothelial activation, @entity32 and the anti-fibrinolytic state as well as hyperinsulinaemia and dyslipidaemia, compared with @entity1 . Obesity indexes (both body mass index and waist-hip ratio) were strongly associated with multiple cardiovascular risk factors. Both @entity650 and @entity310 were correlated with age, male gender, @entity28 indexes and markers of endothelial activation. Only @entity650 was associated with smoking while @entity310 was correlated with hyperinsulinaemia. In the @entity310 model, 61% of the @entity28 variance was explained by male gender, 36% of @entity310 variance by age and dyslipidaemia, 43% of dyslipidaemia variance by age and @entity28 , 33% of hyperinsulinaemia variance by dyslipidaemia and a non-smoking state, 29% of anti-fibrinolytic state variance by hyperinsulinaemia, 65% of endothelial activation variance by @entity310 , dyslipidaemia and hyperinsulinaemia, 34% of blood pressure variance by hyperinsulinaemia and endothelial activation. In the @entity650 model, we observed similar relationships except that 23% of @entity650 variance was explained by smoking and age. CONCLUSIONS: Using confirmatory factor analysis and structural equation modelling, we found that @entity28 , @entity2299 and cytokines were the principal explanatory variables for the various components of the @entity209 , with @entity650 and @entity310 having different explanatory variables and effects. These complex inter-relationships were in part mediated by hyperinsulinaemia and endothelial activation. While this hypothetical model was based on scientific evidence, supported by rigorous analysis, it requires further confirmation in large-scale prospective studies. Given the complexity of the biological system and its interactions with exogenous factors, structural equation modelling provides a useful scientific tool for hypothesis testing, complementary to the more traditional experimental and cohort studies.
