The association between Polycystic Ovary Syndrome (PCOS) and Metabolic Syndrome: A Statistical Modelling Approach — ASN Events
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  3. The association between Polycystic Ovary Syndrome (PCOS) and Metabolic Syndrome: A Statistical Modelling Approach

The association between Polycystic Ovary Syndrome (PCOS) and Metabolic Syndrome: A Statistical Modelling Approach (#332)

Sanjeeva Ranasinha 1 , Anju Joham 1 2 , Lisa Moran 1 3 , Robert Norman 3 , Sophia Zoungas 1 2 , Jacqueline Boyle 1 4 , Helena Teede 1 2
  1. Monash Applied Research Stream, School of Public Health and Preventative Medicine, Monash University, Clayton, VIC, Australia
  2. Southern Health, Clayton, VIC, Australia
  3. Robinson Institute, University of Adelaide, North Adelaide , SA, Australia
  4. Jean Hailes for Women's Health, Clayton, VIC , Australia

Context
Polycystic ovary syndrome (PCOS) affects 6-21% of women. The majority of women with PCOS exhibit clustering of metabolic features.

Objective
We aimed to apply rigorous statistical methods to test these relationships to further understand the interplay between PCOS, metabolic features including insulin resistance, obesity and androgen status.

Design
Cross-sectional analysis of data from a retrospective dataset from case records and data from a national population based study.

Settings
Reproductive endocrine clinic and general community

Participants
Participants were selected from case records of women attending reproductive endocrine clinics in South Australia (n=172) for treatment of infertility or features of PCOS. An age and BMI matched cohort of control women (n=335) were used as a comparison group andwere chosen from the Australian Diabetes, Obesity and Lifestyle Study (AusDiab), a national population based study .

Main outcome measures
This study examines the statistical factor structure to determine contributing factors for metabolic syndrome in PCOS using confirmatory factor analysis (CFA).

Results
Metabolic syndrome in the PCOS cohort is strongly represented by the obesity (loading=0.95, p<0.001) and independently also by insulin resistance factors (loading=0.92, p<0.001). It is represented moderately by blood pressure (loading=0.62, p<0.001) and lipids (loading=0.67, p=0.002). On further analysis, the insulin resistance factor strongly correlated with the obesity (r=0.73, p<0.001) and lipid (r=0.68, p<0.001) factors and moderately with the blood pressure factor (r=0.43).

Conclusions
The current analysis supports the hypothesis that PCOS women are much more likely to display metabolic clustering in comparison to age and BMI matched controls. Obesity and insulin resistance are independently and strongly associated with metabolic syndrome in PCOS. Potentially, simultaneous strategies to improve insulin resistance and weight management strategies may be important to address PCOS and the metabolic syndrome in future.

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