title: Synergy between adiposity, insulin resistance, metabolic risk factors, and inflammation in adolescents.
abstract: OBJECTIVE
  The purpose of this study was to investigate relationships between inflammatory markers and components of a metabolic syndrome cluster in adolescents.
  
  
  METHODS
  This was a cross-sectional analysis of an Australian childhood cohort (n = 1,377) aged 14 years. Cluster analysis defined a "high-risk" group similar to adults with metabolic syndrome. Relevant measures were anthropometry, fasting insulin, glucose, lipids, inflammatory markers, liver function, and blood pressure.
  
  
  RESULTS
  Of the children, 29% fell into a high-risk metabolic cluster group compared with 2% by a pediatric metabolic syndrome definition. Relative to the "low-risk" cluster, they had higher BMI (95% CI 19.5-19.8 vs. 24.5-25.4), waist circumference (centimeters) (95% CI 71.0-71.8 vs. 83.4-85.8), insulin (units per liter) (95% CI 1.7-1.8 vs. 3.5-3.9), homeostasis model assessment (95% CI 1.7-1.8 vs. 3.5-3.9), systolic blood pressure (millimeters of mercury) (95% CI 110.8-112.1 vs. 116.7-118.9), and triglycerides (millimoles per liter) (95% CI 0.78-0.80 vs. 1.25-1.35) and lower HDL cholesterol (millimoles per liter) (95% CI 1.44-1.48 vs. 1.20-1.26). Inflammatory and liver function markers were higher in the high-risk group: C-reactive protein (CRP) (P < 0.001), uric acid (P < 0.001), alanine aminotransferase (ALT) (P < 0.001), and gamma-glutamyl transferase (GGT) (P < 0.001). The highest CRP, GGT, and ALT levels were restricted to overweight children in the high-risk group.
  
  
  CONCLUSIONS
  Cluster analysis revealed a strikingly high proportion of 14 year olds at risk of cardiovascular disease-related metabolic disorders. Adiposity and the metabolic syndrome cluster are synergistic in the pathogenesis of inflammation. Systemic and liver inflammation in the high-risk cluster is likely to predict diabetes, cardiovascular disease, and nonalcoholic fatty liver disease.
authors.affiliation: University of Western Australia, School of Medicine and Pharmacology, Royal Perth Hospital, Perth, Western Australia, Australia rhuang@meddent.uwa.edu.au
authors.forename: Rae-Chi
authors.initials: RC
authors.lastname: Huang
authors.forename: Trevor A
authors.initials: TA
authors.lastname: Mori
authors.forename: Valerie
authors.initials: V
authors.lastname: Burke
authors.forename: John
authors.initials: J
authors.lastname: Newnham
authors.forename: Fiona J
authors.initials: FJ
authors.lastname: Stanley
authors.forename: Louis I
authors.initials: LI
authors.lastname: Landau
authors.forename: Garth E
authors.initials: GE
authors.lastname: Kendall
authors.forename: Wendy H
authors.initials: WH
authors.lastname: Oddy
authors.forename: Lawrence J
authors.initials: LJ
authors.lastname: Beilin
mesh_terms: D000293:Adolescent; D000410:Alanine Transaminase / Q000097:blood; D001315:Australia; D002097:C-Reactive Protein / Q000378:metabolism; D002318:Cardiovascular Diseases / Q000453:epidemiology*; D008076:Cholesterol, HDL / Q000097:blood; D016000:Cluster Analysis; D015331:Cohort Studies; D003430:Cross-Sectional Studies; D005260:Female; D006801:Humans; D007249:Inflammation / Q000097:blood / Q000150:complications* / Q000453:epidemiology; D007333:Insulin Resistance / Q000502:physiology*; D008297:Male; D008659:Metabolic Diseases / Q000453:epidemiology*; D050177:Overweight / Q000097:blood / Q000150:complications* / Q000209:etiology; D018579:Patient Selection; D018570:Risk Assessment; D012307:Risk Factors; D014280:Triglycerides / Q000097:blood; D014527:Uric Acid / Q000097:blood
chemical_list: D008076:Cholesterol, HDL; D014280:Triglycerides; D014527:Uric Acid; D002097:C-Reactive Protein; D000410:Alanine Transaminase