title: The central roles of obesity-associated dyslipidaemia, endothelial activation and cytokines in the Metabolic Syndrome--an analysis by structural equation modelling.
abstract: OBJECTIVE
  The multi-faceted components of the metabolic syndrome now include markers of inflammation 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 obesity may lead to endothelial activation which is in part mediated by dyslipidaemia and proinflammatory cytokines. These factors interact to give rise to hyperinsulinaemia, hypertension 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
  Metabolic syndrome variables, cytokines (IL6 and TNFalpha), markers of inflammation 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, obesity, dyslipidaemia, hyperinsulinaemia, endothelial activation and the anti-fibrinolytic state. We fitted these variables to two separate models, one using IL-6 and the other TNFalpha as the cytokine, and examined the inter-relationships (path analysis) amongst these variables, based on the above hypothesis.
  
  
  RESULTS
  Men were centrally more obese and had increased markers of endothelial activation, inflammation and the anti-fibrinolytic state as well as hyperinsulinaemia and dyslipidaemia, compared with women. Obesity indexes (both body mass index and waist-hip ratio) were strongly associated with multiple cardiovascular risk factors. Both IL6 and TNFalpha were correlated with age, male gender, obesity indexes and markers of endothelial activation. Only IL-6 was associated with smoking while TNFalpha was correlated with hyperinsulinaemia. In the TNFalpha model, 61% of the obesity variance was explained by male gender, 36% of TNFalpha variance by age and dyslipidaemia, 43% of dyslipidaemia variance by age and obesity, 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 TNFalpha, dyslipidaemia and hyperinsulinaemia, 34% of blood pressure variance by hyperinsulinaemia and endothelial activation. In the IL-6 model, we observed similar relationships except that 23% of IL6 variance was explained by smoking and age.
  
  
  CONCLUSIONS
  Using confirmatory factor analysis and structural equation modelling, we found that obesity, dyslipidemia and cytokines were the principal explanatory variables for the various components of the metabolic syndrome, with IL6 and TNFalpha 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.
authors.affiliation: Department of Medicine and Therapeutics, The Chinese University of Hong Kong, The Prince of Wales Hospital, Shatin, NT, Hong Kong, China. jchan@cuhk.edu.hk
authors.forename: J C N
authors.initials: JC
authors.lastname: Chan
authors.forename: J C K
authors.initials: JC
authors.lastname: Cheung
authors.forename: C D A
authors.initials: CD
authors.lastname: Stehouwer
authors.forename: J J
authors.initials: JJ
authors.lastname: Emeis
authors.forename: P C Y
authors.initials: PC
authors.lastname: Tong
authors.forename: G T C
authors.initials: GT
authors.lastname: Ko
authors.forename: J S
authors.initials: JS
authors.lastname: Yudkin
mesh_terms: D000328:Adult; D000368:Aged; D000375:Aging; D001794:Blood Pressure; D001823:Body Composition; D001824:Body Constitution; D015992:Body Mass Index; D008076:Cholesterol, HDL / Q000097:blood; D008078:Cholesterol, LDL / Q000097:blood; D016207:Cytokines / Q000502:physiology*; D004730:Endothelium, Vascular / Q000503:physiopathology*; D005260:Female; D005342:Fibrinolysis; D006801:Humans; D006946:Hyperinsulinism / Q000150:complications; D006949:Hyperlipidemias / Q000150:complications*; D007249:Inflammation / Q000150:complications; D015850:Interleukin-6 / Q000097:blood; D008297:Male; D024821:Metabolic Syndrome; D008875:Middle Aged; D009765:Obesity / Q000150:complications* / Q000503:physiopathology; D012727:Sex Characteristics; D012907:Smoking / Q000009:adverse effects; D014280:Triglycerides / Q000097:blood; D014409:Tumor Necrosis Factor-alpha / Q000032:analysis
chemical_list: D008076:Cholesterol, HDL; D008078:Cholesterol, LDL; D016207:Cytokines; D015850:Interleukin-6; D014280:Triglycerides; D014409:Tumor Necrosis Factor-alpha