Diagnosis Of Polycystic Ovary Syndrome Cases Who Cardiometabolic Risk Factors Comparison Of Diagnostic Criteria

Aim: Diagnosis of polycystic ovary syndrome (PCOS) is performed by using Rotterdam 2003 criteria which is based on the presence of at least two of the following criteria; a. Hyperandrogenism and/or hyperandrogenismus (HA), b. Amenorrhea or oligoanovulation (OA), c. Visualisation of polycystic ovaries by ultrasonography (PCO). This new diagnostic criteria have enlarged the spectrum of PCOS phenotypes, however, cardio-metabolic risk profiles of phenotypic subsets of the syndrome are not clear. The aim of our study is to determine the cardio-metabolic risk factors in different PCOS phenotypes. Material and Method: For this purpose, 89 patients with PCOS and 25 age and body-mass-index (BMl) matched healthy controls were included. Participants were grouped based on Rotterdam 2003 criteria as; Group 1; OA+HA (n=23), Group 2; OA+PCO (n=22), Group 3; HA+PCO (n=22), Group 4; OA+HA+PCO (n=22), Group 5; Control group (n=25). Laboratory blood tests including fasting glucose, post-load 2nd hour glucose, insulin, lipid parameters and other parameters of metabolic syndrome were performed. lnsulin sensitivities were calculated using Homeostasis Model Assessment of lnsulin Resistance (HOMA-lR) formula, euglycemic hyperinsulinemic clamp (EHC) test was performed on randomly selected five cases in each subgroup, 25 cases in total and indicated as "M" value mg/kg/min. Results: Mean BMl of the study subgroups was similar; OA+HA; 26.1±5.3 kg/m2, OA+PCO; 27.9±5.2, HA+PCO; 24.3±4.2, OA+HA+PCO; 27.9±7.5, control; 24.7±5.2 (p>0,05). Study group had higher total testosterone, luteinizing hormone (LH), triglyceride and fibrinogen, and also had higher glucose levels at second hour of oral glucose tolerance test than the control group (p<0.05). There was no statistically significant difference among groups regarding HOMA-IR and M values (p>0,05). Phenotypes with oligomenorrhea or anovulation (Group1+ +2+4) were more obese than the group with regular ovulation (Group3) (p=0.039). Conclusion: Cardio-metabolic risk profile was found to be similar among our PCOS subgroups. As obesity is an important contributor of cardiovascular risk associated with PCOS, this finding may be explained by their mean BMl value which is not within the obesity range. Obesity also has an important impact on the severity of menstrual irregularities. Accordingly, in our study we found that phenotypes with menstrual irregularities are more obese than the ovulatory group. Taken together, our findings support that obesity appears to be the primary determinant of metabolic abnormalities in different PCOS phenotypes.