title: Association between different growth curve definitions of overweight and obesity and cardiometabolic risk in children.
abstract: BACKGROUND
  Overweight and obesity in young people are assessed by comparing body mass index (BMI) with a reference population. However, two widely used reference standards, the Centers for Disease Control and Prevention (CDC) and the World Health Organization (WHO) growth curves, have different definitions of overweight and obesity, thus affecting estimates of prevalence. We compared the associations between overweight and obesity as defined by each of these curves and the presence of cardiometabolic risk factors.
  
  
  METHODS
  We obtained data from a population-representative study involving 2466 boys and girls aged 9, 13 and 16 years in Quebec, Canada. We calculated BMI percentiles using the CDC and WHO growth curves and compared their abilities to detect unfavourable levels of fasting lipids, glucose and insulin, and systolic and diastolic blood pressure using receiver operating characteristic curves, sensitivity, specificity and kappa coefficients.
  
  
  RESULTS
  The z scores for BMI using the WHO growth curves were higher than those using the CDC growth curves (0.35-0.43 v. 0.12-0.28, p < 0.001 for all comparisons). The WHO and CDC growth curves generated virtually identical receiver operating characteristic curves for individual or combined cardiometabolic risk factors. The definitions of overweight and obesity had low sensitivities but adequate specificities for cardiometabolic risk. Obesity as defined by the WHO or CDC growth curves discriminated cardiometabolic risk similarly, but overweight as defined by the WHO curves had marginally higher sensitivities (by 0.6%-8.6%) and lower specificities (by 2.6%-4.2%) than the CDC curves.
  
  
  CONCLUSIONS
  The WHO growth curves show no significant discriminatory advantage over the CDC growth curves in detecting cardiometabolic abnormalities in children aged 9-16 years.
authors.affiliation: Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, Que. lisa.kakinami@mcgill.ca
authors.forename: Lisa
authors.initials: L
authors.lastname: Kakinami
authors.forename: Mélanie
authors.initials: M
authors.lastname: Henderson
authors.forename: Edgard E
authors.initials: EE
authors.lastname: Delvin
authors.forename: Emile
authors.initials: E
authors.lastname: Levy
authors.forename: Jennifer
authors.initials: J
authors.lastname: O'Loughlin
authors.forename: Marie
authors.initials: M
authors.lastname: Lambert
authors.forename: Gilles
authors.initials: G
authors.lastname: Paradis
mesh_terms: D000293:Adolescent; D015415:Biomarkers / Q000097:blood; D001786:Blood Glucose / Q000378:metabolism; D001794:Blood Pressure; D015992:Body Mass Index; D002487:Centers for Disease Control and Prevention, U.S.; D002648:Child; D003430:Cross-Sectional Studies; D050171:Dyslipidemias / Q000097:blood / Q000175:diagnosis* / Q000209:etiology; D005260:Female; D044882:Glucose Metabolism Disorders / Q000097:blood / Q000175:diagnosis* / Q000209:etiology; D057238:Growth Charts; D006801:Humans; D006973:Hypertension / Q000097:blood / Q000175:diagnosis* / Q000209:etiology; D007328:Insulin / Q000097:blood; D008055:Lipids / Q000097:blood; D008297:Male; D009765:Obesity / Q000097:blood / Q000150:complications / Q000175:diagnosis; D050177:Overweight / Q000097:blood / Q000150:complications / Q000175:diagnosis*; D011792:Quebec; D012372:ROC Curve; D012307:Risk Factors; D012680:Sensitivity and Specificity; D014481:United States; D014944:World Health Organization
chemical_list: D015415:Biomarkers; D001786:Blood Glucose; D007328:Insulin; D008055:Lipids