Metabolic syndrome in polycystic ovary syndrome: a systematic review, meta-analysis and meta-regression.

Monash Centre for Health Research and Implementation, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia. Diabetes and Vascular Medicine Unit, Monash Health, Clayton, Victoria, Australia. Robinson Research Institute, University of Adelaide and Fertility SA, Adelaide, South Australia, Australia. Monash Partners Academic Health Sciences Centre, Melbourne, Victoria, Australia.

Obesity reviews : an official journal of the International Association for the Study of Obesity. 2019;(2):339-352

Abstract

Women with polycystic ovary syndrome (PCOS) have increased risk of metabolic syndrome. The relative contribution of clinical, demographic or biochemical factors to metabolic syndrome in PCOS is not known. A literature search was conducted in MEDLINE, CINAHL, EMBASE and clinical trial registries. Of 4530 studies reviewed, 59 were included in the systematic review and 27 in the meta-analysis and meta-regression. In good and fair quality studies, women with PCOS had an overall increased prevalence of metabolic syndrome (odds ratio, OR 3.35, 95% confidence interval, CI 2.44, 4.59). Increased prevalence of metabolic syndrome occurred in overweight or obese women with PCOS (OR 1.88, 95% 1.16, 3.04) but not in lean women (OR 1.45, 95% CI 0.35, 6.12). In meta-regression analyses, the markers of metabolic syndrome diagnostic criteria (waist circumference, high-density lipoprotein cholesterol, triglyceride, blood pressure), BMI, glucose tolerance (2-hr oral glucose tolerance test) and surrogate markers of insulin resistance (HOMA-IR) but not markers of reproductive dysfunction (sex hormone binding globulin, testosterone, PCOS phenotypes) contributed significantly to the heterogeneity in the prevalence of metabolic syndrome. Women with PCOS have increased risk of metabolic syndrome which was associated with obesity and metabolic features but not with indices of hyperandrogenism.

Methodological quality

Publication Type : Meta-Analysis

Metadata

MeSH terms : Insulin Resistance