Dataset Information

Prevalence of metabolic syndrome is higher among non-obese PCOS women with hyperandrogenism and menstrual irregularity in Korea.

ABSTRACT:

Background

Hyperandrogenism (HA) has been linked with several components of metabolic syndrome (MetS). Few studies in Asian women have evaluated the important risk factors for and prevalence of MetS according to PCOS subtype. In this study, we investigated differences in metabolic parameters and the prevalence of MetS in two major phenotypic subgroups of PCOS in Korea. Furthermore, we investigated the relationship between HA-associated parameters and MetS.

Materials and methods

This cross-sectional observational study was conducted from May 2010 to December 2011 in Korea. A total of 837 females with PCOS, aged 15-40, were recruited from Departments of Obstetrics and Gynecology at 13 hospitals. Of those, 700 subjects with either polycystic ovaries (PCO)+HA+oligomenorrhea/amenorrhea (O) or PCO+O were eligible for this study. MetS was diagnosed according to the modified National Cholesterol Education Program (NCEP) Adult Treatment Panel (ATP) III guidelines and the International Diabetes Federation (IDF) criteria.

Results

MetS was more prevalent in the PCO+HA+O group (19.7%) than in the PCO+O (11.9%) group. There were statistically significant trends for an increased risk of MetS in the PCO+HA+O group compared to the PCO+O group. After adjustment for age, the odds ratio of MetS was 2.192 in non-obese subjects with PCO+HA+O compared to those with PCO+O, whereas the risk of MetS was not different in obese patients. Multivariate logistic regression analysis showed that high free androgen index and low sex hormone-binding globulin were significantly associated with MetS in non-obese women with PCOS, with odds ratios of 4.234 (95% CI, 1.893-9.474) and 4.612 (95% CI, 1.978-10.750), respectively. However, no associations were detected between MetS and SHBG and FAI in obese PCOS subjects.

Conclusions

Our results indicate that HA and its associated parameters (FAI and SHBG) are significantly associated with MetS in non-obese PCOS subjects, whereas this association was not observed in obese subjects.

SUBMITTER: Kim MJ

PROVIDER: S-EPMC4047097 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Publications

Prevalence of metabolic syndrome is higher among non-obese PCOS women with hyperandrogenism and menstrual irregularity in Korea.

Kim Min-Ju MJ Lim Nam-Kyoo NK Choi Young-Min YM Kim Jin-Ju JJ Hwang Kyu-Ri KR Chae Soo-Jin SJ Park Chan-Woo CW Choi Doo-Seok DS Kang Byung-Moon BM Lee Byung-Seok BS Kim Tak T Park Hyun-Young HY

PloS one 20140605 6

<h4>Background</h4>Hyperandrogenism (HA) has been linked with several components of metabolic syndrome (MetS). Few studies in Asian women have evaluated the important risk factors for and prevalence of MetS according to PCOS subtype. In this study, we investigated differences in metabolic parameters and the prevalence of MetS in two major phenotypic subgroups of PCOS in Korea. Furthermore, we investigated the relationship between HA-associated parameters and MetS.<h4>Materials and methods</h4>Th ...[more]

PMID: 24901345

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Project description:The regularity of menstrual cycles is considered an indicator of women's reproductive health. Previous studies with a cross-sectional design have documented the relationship between menstrual cycle irregularities, insulin-resistance and the future risks for metabolic disorders. Limited data documented by prospective studies can lead to premature conclusions regarding the relationship between menstrual cycle irregularities and other conditions influencing women's health. The present study therefore, using a prospective design aimed to assess the risk of metabolic disorders in women with a history of irregular menstrual cycles, was based on the data gathered from the Tehran Lipid and Glucose study (TLGS) an ongoing prospective cohort study initiated in 1999. Participants of the current study were 2128 women, aged between 18-49 years, followed for 15 years. Based on their menstrual cycles, the women were divided into two groups: (i) women with regular menstrual cycles (n = 1749), and (ii) those with irregular menstrual cycles (n = 379). The proportional COX regression model was used to compare hazard ratios (HRs) between the groups for the proposed events, including diabetes mellitus (DM), pre-diabetes (pre-DM), hypertension (HTN), pre-hypertension (pre-HTN) and dyslipidemia. It was found that during a 15-year follow up, there were 123 cases of DM, 456 cases of pre-DM, 290 cases of HTN, 481 cases of pre-HTN, and 402 cases of dyslipidemia. Compared to those with regular cycles, women with irregular menstrual cycles were found to have an increased risk for DM2 (age adjusted Hazard Ratios (HRs), 2.01; 95% confidence intervals (CI:1.59-3.50), the increased risk for DM, associated with irregular cycles remained significant after the adjustment for Body Mass Index (BMI), fasting blood sugar (FBS), family history of diabetes, and parity (HRS, 1.73; 95% CI: 1.14-2.64). There was no significant difference in the increased risk for pre-DM between the groups (age adjusted HRs, 1.33, 95% CI: 1.05-1.69). However, after the adjustment of BMI, FBS and family history of pre-DM, compared to those with regular menstrual cycles, irregular menstrual cycles showed an increased risk for pre-DM (HRs, 1.33; 95% CI: 1.05-1.69). No statistically significant difference was found in the increasing risk for other proposed events between the groups demonstrating that menstrual cycle irregularities could be considered a marker of metabolic disorders and a predisposing factor of the increased risk for diabetes mellitus and pre-diabetes in women with irregular menstrual cycles.

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Dataset Information

Prevalence of metabolic syndrome is higher among non-obese PCOS women with hyperandrogenism and menstrual irregularity in Korea.

Background

Materials and methods

Results

Conclusions

Publications

Prevalence of metabolic syndrome is higher among non-obese PCOS women with hyperandrogenism and menstrual irregularity in Korea.

Similar Datasets

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