Effects of Sitagliptin on Metabolic Indices and Hormone Levels in Polycystic Ovary Syndrome: a Meta-analysis of Randomized Controlled Trials.

To evaluate the effects of sitagliptin on the metabolic indices and hormone levels in patients with polycystic ovary syndrome (PCOS). PubMed, EMBASE, Web of Science, Cochrane Library, WanFang Data, and China National Knowledge Infrastructure (CNKI) were searched for randomized controlled trials (RCTs) published up to March 2022. Eligible studies were identified based on the inclusion criteria. The primary outcomes included the homeostasis model assessment of insulin resistance (HOMA-IR), body mass index (BMI), and total testosterone level (TT). Other outcomes included levels of sex hormones, glucose, and lipid metabolism. Forty-five studies were initially identified, and 6 RCTs with 394 patients were finally included in this study. The meta-analysis results suggest that sitagliptin improved HOMA-IR (WMD = - 0.35; 95% CI (- 0.62, - 0.08); P = 0.01), BMI (WMD = - 1.27; 95% CI (- 1.76, - 0.77); P < 0.00001), TT (SMD = - 0.66; 95% CI (- 1.25, - 0.07); P = 0.03), and HDL-C (SMD = 0.11; 95% CI (0.03, 0.18); P = 0.005). No significant differences were observed between the sitagliptin and control groups in other outcomes and in terms of adverse events. Evidence from meta-analyses suggests that sitagliptin was superior in improving insulin sensitivity, total serum testosterone, high-density lipoprotein, and body mass index. However, due to the limitations of published studies, it is difficult to draw a definite conclusion. Larger, higher-quality studies are needed to evaluate the efficacy of sitagliptin in women with PCOS.

Value of the triglyceride-glucose index and non-traditional blood lipid parameters in predicting metabolic syndrome in women with polycystic ovary syndrome.

PURPOSE: Insulin resistance (IR) is common in patients with polycystic ovary syndrome (PCOS). Metabolic syndrome (MS) includes, inter alia, IR, hypertension, dyslipidemia, and disturbances in glucose metabolism. The triglyceride-glucose (TyG) index and non-traditional lipid parameters are strong predictors of IR and cardiovascular disease and can be considered as screening indicators for MS. This study aimed to evaluate the predictive potential of non-traditional lipid parameters and the TyG index to identify MS in PCOS. METHODS: This cross-sectional study included 134 women diagnosed with PCOS (50 patients with comorbid MS and 84 patients without MS). Biochemical indices were collected, and triglycerides (TG)/high-density lipoprotein cholesterol (HDL-C), total cholesterol (TC)/HDL-C, low-density lipoprotein cholesterol (LDL-C)/HDL-C, non-HDL-C, TyG, and TyG-BMI indices were calculated. Logistic regression analysis was used to compare and determine the association of the six parameters with MS, and the receiver operating characteristic (ROC) curve was used to evaluate the performance of each parameter in identifying MS in the PCOS population. RESULTS: After adjusting for age and body mass index (BMI), TG/HDL-C, TC/HDL-C, LDL-C/HDL-C, non-HDL-C, TyG, and TyG-BMI were associated with MS (all P<0.05). The odds ratios were 4.075 (0.891, 1.107), 3.121 (1.844, 5.282), 3.106 (1.734, 5.561), 2.238 (1.302, 3.848), 13.422 (4.364, 41.282), and 1.102 (1.056, 1.150), respectively. TG/HDL-C, TC/HDL-C, LDL-C/HDL-C, non-HDL-C, TyG, and TyG-BMI are effective predictors of MS in PCOS, and their cut-off values can be used for the early detection of MS. TyG-BMI had the strongest performance in predicting MS (area under the curve 0.905, 95% CI 0.855-0.956), and its optimal critical value for predicting MS was 202.542. CONCLUSIONS: TG/HDL-C, TC/HDL-C, LDL-C/HDL-C, non-HDL-C, TyG, and TyG-BMI are novel, clinically convenient and practical markers for the early identification of MS risk in PCOS patients.