A model for predicting metabolic syndrome in postmenopausal women

Objective: to create an effective predictive model for the development of metabolic syndrome (MS) in women in their early postmenopausal period. Materials and methods. Our case-control study included 82 women aged 46–60 years; Group 1 consisted of 35 women with MS, while Group 2 comprised 47 women without MS. We collected anamnesis on the basis of outpatient medical records using a specially developed form, performed anthropometry and laboratory tests, and assessed the severity of climacteric syndrome using the modified menopausal index (MMI). Results. In patients with MS vs. the control group, the homeostasis model assessment of insulin resistance (HOMA-IR) index, visceral adiposity index (VAI) and MMI were significantly higher (p<0.001, p<0.001 and p=0.003, respectively), while the levels of follicle-stimulating hormone (FSH) and prolactin were lower (p=0.008 and p=0.011, correspondingly). Using the multiple logistic regression method, we developed a screening model for assessing the probability of developing MS in women. The model included five parameters: the presence of somatic comorbidity, VAI, MMI, FSH content and prolactin level. The ROC curve was used to evaluate the model. The area under the ROC curve (AUC) of the model was 0.909 with a 95% confidence interval (CI) 0.835–0.984 (sensitivity: 87.1%; specificity: 78.6%). Conclusion. Our predictive model for the development of metabolic syndrome in women in the early postmenopausal period allowed identifying patients with increased cardiometabolic risk, thereby contributing to timely initiation of personalized preventive measures and treatment.