title: Waist circumference cutoff points in school-aged Chinese Han and Uygur children.
abstract: OBJECTIVE
  To examine the distribution of age- and gender-specific waist circumference (WC) and to identify optimal cutoffs for predicting cardiovascular (CV) risk factors in Chinese Han and Uygur children.
  
  
  METHODS
  In total 4,224 Han and Uygur children aged 7-18 were randomly selected from schools in Urumqi city, China. WC, height, and weight were accurately measured in all subjects. Blood pressure, fasting lipids, glucose, and insulin were determined in 258 overweight/obese children and 370 age-, gender-, and ethnicity-matched normal-weight controls selected from the same study sample. LMS (lambda-mu-sigma) method was used to construct WC centile curves. Optimal WC cutoffs were determined by comparing the performance of five sets of WC cutoffs in predicting CV risk factors.
  
  
  RESULTS
  Han boys and girls had higher WC at all percentiles and ages except for girls over the age of 16. Uygur girls also showed a greater increase in WC corresponding with age (2.1 cm/year vs. 1.6 cm/year for the median) than Han girls. Compared with the other four sets of cutoffs, the 85th WC percentile showed the best combination of sensitivity (81.0%) and specificity (71.4%). The two sets of WC cutoffs constructed by allowing centile curves passing through the adult cutoffs for central adiposity using the fitted LMS curves provided poor sensitivities in predicting the presence of cluster of CV risk factors.
  
  
  CONCLUSIONS
  The growth pattern of WC varies with gender and ethnicity. The 85th percentile of WC is an appropriate cutoff for Chinese Han and Uygur children in the prediction of CV risks.
authors.affiliation: Division of Epidemiology and Biostatistics, School of Public Health, Xinjiang Medical University, Urumqi, Xinjiang, China. yanweili01@yahoo.com.cn
authors.forename: Weili
authors.initials: W
authors.lastname: Yan
authors.forename: Hua
authors.initials: H
authors.lastname: Yao
authors.forename: Jianghong
authors.initials: J
authors.lastname: Dai
authors.forename: Jun
authors.initials: J
authors.lastname: Cui
authors.forename: Yan
authors.initials: Y
authors.lastname: Chen
authors.forename: Xiaoyan
authors.initials: X
authors.lastname: Yang
authors.forename: Gregory A
authors.initials: GA
authors.lastname: Harshfield
authors.forename: Xiaoling
authors.initials: X
authors.lastname: Wang
mesh_terms: D000293:Adolescent; D017677:Age Distribution; D000367:Age Factors; D044466:Asian People; D001786:Blood Glucose / Q000032:analysis; D001794:Blood Pressure; D001827:Body Height; D049628:Body Size / Q000208:ethnology*; D001835:Body Weight; D002318:Cardiovascular Diseases / Q000208:ethnology / Q000209:etiology* / Q000378:metabolism / Q000503:physiopathology; D016022:Case-Control Studies; D002648:Child; D002681:China / Q000453:epidemiology; D003430:Cross-Sectional Studies; D005260:Female; D006801:Humans; D007328:Insulin / Q000097:blood; D008055:Lipids / Q000097:blood; D008297:Male; D009765:Obesity / Q000150:complications / Q000208:ethnology* / Q000378:metabolism / Q000503:physiopathology; D050177:Overweight / Q000150:complications / Q000208:ethnology* / Q000378:metabolism / Q000503:physiopathology; D012016:Reference Values; D012307:Risk Factors; D017678:Sex Distribution; D012737:Sex Factors
chemical_list: D001786:Blood Glucose; D007328:Insulin; D008055:Lipids