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. DESIGN: Prospective cohort study. SETTING: Population-based cohort of adolescents in Western Australia. PARTICIPANT(S): Metabolic data from 1,377 children aged 14 years, features of PCOS obtained from 244 girls aged 14 to 17 years. INTERVENTION(S): Assessment for features of PCOS and subsequent fasting blood samples. MAIN OUTCOME MEASURE(S): Relationship between features of PCOS and features of metabolic syndrome. RESULT(S): 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). CONCLUSION(S): 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. Crown
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. DESIGN: Prospective cohort study. SETTING: Population-based cohort of adolescents in Western Australia. PARTICIPANT(S): Metabolic data from 1,377 children aged 14 years, features of PCOS obtained from 244 girls aged 14 to 17 years. INTERVENTION(S): Assessment for features of PCOS and subsequent fasting blood samples. MAIN OUTCOME MEASURE(S): Relationship between features of PCOS and features of metabolic syndrome. RESULT(S): 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). CONCLUSION(S): 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. Crown
Authors: Leon Straker; Jenny Mountain; Angela Jacques; Scott White; Anne Smith; Louis Landau; Fiona Stanley; John Newnham; Craig Pennell; Peter Eastwood Journal: Int J Epidemiol Date: 2017-10-01 Impact factor: 7.196
Authors: Kristen M Williams; Sharon E Oberfield; Chengchen Zhang; Donald J McMahon; Aviva B Sopher Journal: Horm Res Paediatr Date: 2015-10-30 Impact factor: 2.852
Authors: E Gourgari; M Lodish; R Shamburek; M Keil; R Wesley; M Walter; M Sampson; S Bernstein; D Khurana; C Lyssikatos; S Ten; A Dobs; A T Remaley; C A Stratakis Journal: J Clin Endocrinol Metab Date: 2015-09-15 Impact factor: 5.958