title: Differential relationships between anthropometry measures and cardiovascular risk factors in boys and girls.
abstract: OBJECTIVE
  The aim of the study was to compare commonly used anthropometry for cardiovascular risk factors in 14-year-olds.
  
  
  METHODS
  A total of 1 149 children of an Australian pregnancy (Raine) cohort with recruitment 1989-1991 were assessed for anthropometry and fasting lipids, insulin, and blood pressure.
  
  
  RESULTS
  There were significant distinctions in the associations between anthropometry and groups of cardiovascular risk factors. These distinctions differed by gender. Insulin resistance, triglycerides, C-reactive protein levels, low density lipoprotein (LDH)/high density lipoprotein (HDL) and total/HDL cholesterol ratios had the strongest association with waist, waist/height ratio and body mass index. By contrast, in boys, height was the strongest independent predictor (in a negative direction) of total and LDL-cholesterol. Blood pressure and uric acid was most strongly correlated with body weight and height (heavier and taller boys). Taller male adolescents had highest blood pressures and lowest cholesterol levels.
  
  
  CONCLUSIONS
  No single adolescent anthropometric measure best predicted all cardiovascular risk factors. Each measure showed distinct relationships with specific groups of risk factors. Contrasting associations may reflect different pathogenesis by which gender, puberty, and adiposity affect metabolic risk. No single anthropometric measurement in childhood would appear to be superior or sufficient when investigating the developmental origins of cardiovascular health and related metabolic disease.
authors.affiliation: The University of Western Australia, School of Medicine and Pharmacology,Western Australia. rhuang@meddent.uwa.edu.au
authors.forename: Rae-Chi
authors.initials: RC
authors.lastname: Huang
authors.forename: Nicholas
authors.initials: N
authors.lastname: de Klerk
authors.forename: Trevor A
authors.initials: TA
authors.lastname: Mori
authors.forename: John P
authors.initials: JP
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: Wendy H
authors.initials: WH
authors.lastname: Oddy
authors.forename: Beth
authors.initials: B
authors.lastname: Hands
authors.forename: Lawrence J
authors.initials: LJ
authors.lastname: Beilin
mesh_terms: D000293:Adolescent; D000367:Age Factors; D000704:Analysis of Variance; D000886:Anthropometry; D015415:Biomarkers / Q000097:blood; D001794:Blood Pressure; D001827:Body Height; D015992:Body Mass Index; D001835:Body Weight; D002318:Cardiovascular Diseases / Q000097:blood / Q000453:epidemiology* / Q000503:physiopathology; D005260:Female; D006801:Humans; D007328:Insulin / Q000097:blood; D007333:Insulin Resistance; D016014:Linear Models; D008055:Lipids / Q000097:blood; D008297:Male; D017711:Nonlinear Dynamics; D009765:Obesity / Q000175:diagnosis / Q000453:epidemiology* / Q000503:physiopathology; D011237:Predictive Value of Tests; D018570:Risk Assessment; D012307:Risk Factors; D012737:Sex Factors; D014527:Uric Acid / Q000097:blood; D055105:Waist Circumference; D014904:Western Australia / Q000453:epidemiology
chemical_list: D015415:Biomarkers; D007328:Insulin; D008055:Lipids; D014527:Uric Acid