Elevated circulating Selenoprotein P levels in patients with polycystic ovary syndrome

Abstract

Selenoprotein P (SeP), an hepatokine that is primarily produced by liver, has been reported to affect glucose metabolism. In this study, we aimed to measure and compare serum SeP values in patients with polycystic ovary syndrome (PCOS) and a healthy control group, and to investigate whether there was a relationship between SeP values and insulin resistance in patients with PCOS. This prospective case-control study included 40 patients with PCOS and 39 healthy women (non-PCOS) matched for age and body mass index. SeP levels were significantly higher in the PCOS group compared with the healthy controls (7.48 ± 3.80 vs. 5.17 ± 3.20 mg/ml, p = .005). Serum insulin, hs-CRP, HOMA-IR, FBG, total-testosterone, and free-testosterone levels were higher in women with PCOS than in controls. In an unadjusted model and after adjusting for potential confounders, SeP had increased odds for PCOS (p = .007). ROC curve analysis showed that the area under the ROC curves were 0.691 (95% CI: 0.576–0.806, p < .003) for SeP levels. The optimal cut-off value of SeP for detecting PCOS was ≥5.87 mgl/ml. We showed, for the first time, that serum SeP levels were increased significantly in PCOS, Our results suggest that there is a potential link between PCOS and SeP levels IMPACT STATEMENT What is already known on this subject? Selenoprotein deficiency causes various dysfunctions associated with oxidative stress, but recent studies found that increased SeP levels were associated with insulin resistance. Circulating SeP levels have been found to be increased in patients with type 2 diabetes mellitus (T2DM). What the results of this study add? Our study is the first in the literature to examine the relationship between SeP levels and the presence of PCOS. Serum SeP levels were increased significantly in PCOS. What the implications are of these findings for clinical practice and/or further research? SeP seemed to have a role in PCOS. SeP can be used to predict metabolic disorders associated with PCOS and to determine treatment methods.