Lipid ratios and obesity indices are effective predictors of metabolic syndrome in women with polycystic ovary syndrome.

Abstract

Background: Insulin resistance (IR) is common in women with polycystic ovary syndrome (PCOS). Metabolic syndrome (MS) involves IR, arterial hypertension, dyslipidemia, and visceral fat accumulation. Therefore, fatness indices and blood lipid ratios can be considered as screening markers for MS. Our study aimed to evaluate the predictive potential of selected indirect metabolic risk parameters to identify MS in PCOS.

Methods: This cross-sectional study involved 596 women aged 18-40 years, including 404 PCOS patients diagnosed according to the Rotterdam criteria and 192 eumenorrheic controls (CON). Anthropometric and blood pressure measurements were taken, and blood samples were collected to assess glucose metabolism, lipid parameters, and selected hormone levels. Body mass index (BMI), waist-to-height ratio (WHtR), homeostasis model assessment for insulin resistance index (HOMA-IR), visceral adiposity index (VAI), lipid accumulation product (LAP), non-high-density lipoprotein cholesterol (non-HDL-C), and triglycerides-to-HDL cholesterol ratio (TG/HDL-C) were calculated. MS was assessed using the International Diabetes Federation (IDF) and the American Heart Association/National Heart, Lung, and Blood Institute (AHA/NHLBI) criteria.

Results: MS prevalence was significantly higher in PCOS versus CON. Patients with both MS and PCOS had more unfavorable anthropometric, hormonal, and metabolic profiles versus those with neither MS nor PCOS and versus CON with MS. LAP, TG/HDL-C, VAI, and WHtR were the best markers and strongest indicators of MS in PCOS, and their cut-off values could be useful for early MS detection. MS risk in PCOS increased with elevated levels of these markers and was the highest when TG/HDL-C was used.

Conclusions: LAP, TG/HDL-C, VAI, and WHtR are representative markers for MS assessment in PCOS. Their predictive power makes them excellent screening tools for internists and enables acquiring accurate diagnoses using fewer MS markers.