title: Defining obesity cut points in a multiethnic population.
abstract: BACKGROUND
  Body mass index (BMI) is widely used to assess risk for cardiovascular disease and type 2 diabetes. Cut points for the classification of obesity (BMI >30 kg/m2) have been developed and validated among people of European descent. It is unknown whether these cut points are appropriate for non-European populations. We assessed the metabolic risk associated with BMI among South Asians, Chinese, Aboriginals, and Europeans.
  
  
  RESULTS
  We randomly sampled 1078 subjects from 4 ethnic groups (289 South Asians, 281 Chinese, 207 Aboriginals, and 301 Europeans) from 4 regions in Canada. Principal components factor analysis was used to derive underlying latent or "hidden" factors associated with 14 clinical and biochemical cardiometabolic markers. Ethnic-specific BMI cut points were derived for 3 cardiometabolic factors. Three primary latent factors emerged that accounted for 56% of the variation in markers of glucose metabolism, lipid metabolism, and blood pressure. For a given BMI, elevated levels of glucose- and lipid-related factors were more likely to be present in South Asians, Chinese, and Aboriginals compared with Europeans, and elevated levels of the blood pressure-related factor were more likely to be present among Chinese compared with Europeans. The cut point to define obesity, as defined by distribution of glucose and lipid factors, is lower by approximately 6 kg/m2 among non-European groups compared with Europeans.
  
  
  CONCLUSIONS
  Revisions may be warranted for BMI cut points to define obesity among South Asians, Chinese, and Aboriginals. Using these revised cut points would greatly increase the estimated burden of obesity-related metabolic disorders among non-European populations.
authors.affiliation: Population Health Research Institute, McMaster University, Hamilton, ON, Canada.
authors.forename: Fahad
authors.initials: F
authors.lastname: Razak
authors.forename: Sonia S
authors.initials: SS
authors.lastname: Anand
authors.forename: Harry
authors.initials: H
authors.lastname: Shannon
authors.forename: Vladimir
authors.initials: V
authors.lastname: Vuksan
authors.forename: Bonnie
authors.initials: B
authors.lastname: Davis
authors.forename: Ruby
authors.initials: R
authors.lastname: Jacobs
authors.forename: Koon K
authors.initials: KK
authors.lastname: Teo
authors.forename: Matthew
authors.initials: M
authors.lastname: McQueen
authors.forename: Salim
authors.initials: S
authors.lastname: Yusuf
mesh_terms: D000328:Adult; D000368:Aged; D044467:American Indian or Alaska Native / Q000706:statistics & numerical data; D044466:Asian People / Q000706:statistics & numerical data; D001459:Bangladesh / Q000208:ethnology; D015992:Body Mass Index; D002170:Canada / Q000453:epidemiology; D002681:China / Q000208:ethnology; D003924:Diabetes Mellitus, Type 2 / Q000453:epidemiology; D050171:Dyslipidemias / Q000208:ethnology; D005006:Ethnicity / Q000706:statistics & numerical data; D005060:Europe / Q000208:ethnology; D005260:Female; D005947:Glucose / Q000378:metabolism; D005951:Glucose Tolerance Test; D006801:Humans; D007194:India / Q000208:ethnology; D007328:Insulin / Q000097:blood; D007333:Insulin Resistance; D008055:Lipids / Q000097:blood; D008297:Male; D008875:Middle Aged; D009765:Obesity / Q000175:diagnosis / Q000208:ethnology*; D050177:Overweight / Q000208:ethnology; D010154:Pakistan / Q000208:ethnology; D015995:Prevalence; D012016:Reference Values; D012307:Risk Factors; D013188:Sri Lanka / Q000208:ethnology
chemical_list: D007328:Insulin; D008055:Lipids; D005947:Glucose