title: Extent of metabolic risk in adolescent girls with features of polycystic ovary syndrome.
abstract: OBJECTIVE
  To determine prevalence of metabolic syndrome in adolescents with polycystic ovary syndrome (PCOS) and derive features suggestive of propensity for development of metabolic syndrome.
  
  
  METHODS
  Prospective cohort study.
  
  
  METHODS
  Population-based cohort of adolescents in Western Australia.
  
  
  METHODS
  Metabolic data from 1,377 children aged 14 years, features of PCOS obtained from 244 girls aged 14 to 17 years.
  
  
  METHODS
  Assessment for features of PCOS and subsequent fasting blood samples.
  
  
  METHODS
  Relationship between features of PCOS and features of metabolic syndrome.
  
  
  RESULTS
  With use of five definitions of metabolic syndrome the maximal prevalence of metabolic syndrome recorded was 11.8% in girls with PCOS (National Institutes of Health [NIH]) and 6.6% (Rotterdam) (non-PCOS 0.6% and 0.7%, respectively). With use of cluster analysis of metabolic risk (a technique to cluster the adolescents according to multidimensional relationships of established cardiovascular risk factors), 35.3% with PCOS-NIH were at risk for metabolic syndrome and 26.2% with PCOS-Rotterdam (non-PCOS 15.4% and 15.4%, respectively). Menstrual irregularity and high free T (PCOS-NIH) were associated with high metabolic syndrome risk (odds ratio 3.00, confidence interval 1.3-6.4), not after controlling for body mass index. Of PCOS features, an elevated free T level was most predictive of insulin resistance. Menstrual irregularity and polycystic ovary morphology were not associated with insulin resistance (56.3% vs. 52.9% and 60.0% vs. 34.4%, respectively).
  
  
  CONCLUSIONS
  Despite the low prevalence of metabolic syndrome in girls with PCOS, one third have features putting them at high risk for development of metabolic syndrome.
authors: School of Women's and Infants' Health, University of Western Australia, and Women and Infants Research Foundation, King Edward Memorial Hospital, Perth, Western Australia, Australia. roger.hart@uwa.edu.au
authors: Roger
authors: R
authors: Hart
authors: Dorota A
authors: DA
authors: Doherty
authors: Trevor
authors: T
authors: Mori
authors: Rae-Chi
authors: RC
authors: Huang
authors: Robert J
authors: RJ
authors: Norman
authors: Stephen
authors: S
authors: Franks
authors: Deborah
authors: D
authors: Sloboda
authors: Lawrie
authors: L
authors: Beilin
authors: Martha
authors: M
authors: Hickey
mesh_terms: D000293:Adolescent; D000367:Age Factors; D015415:Biomarkers / Q000097:blood; D016009:Chi-Square Distribution; D016000:Cluster Analysis; D005260:Female; D006801:Humans; D007333:Insulin Resistance; D016015:Logistic Models; D008597:Menstrual Cycle; D024821:Metabolic Syndrome / Q000097:blood / Q000453:epidemiology* / Q000503:physiopathology; D016017:Odds Ratio; D050177:Overweight / Q000453:epidemiology; D011085:Polycystic Ovary Syndrome / Q000097:blood / Q000453:epidemiology* / Q000503:physiopathology; D015995:Prevalence; D018570:Risk Assessment; D012307:Risk Factors; D013739:Testosterone / Q000097:blood; D015854:Up-Regulation; D014904:Western Australia / Q000453:epidemiology
chemical_list: D015415:Biomarkers; D013739:Testosterone