Association of glucocorticoid receptor polymorphisms with clinical and metabolic profiles in polycystic ovary syndrome

AUTOR(ES)

Maciel, Gustavo A.Rosa, Moreira, Ricardo P.P., Bugano, Diogo D.G., Hayashida, Sylvia A.Y., Marcondes, José A.M., Gomes, Larissa G., Mendonça, Berenice B., Bachega, Tânia A.S.S., Baracat, Edmund C.

FONTE

Clinics

DATA DE PUBLICAÇÃO

2014-03

RESUMO

OBJECTIVES: We aimed to investigate whether glucocorticoid receptor gene polymorphisms are associated with clinical and metabolic profiles in patients with polycystic ovary syndrome. Polycystic ovary syndrome is a complex endocrine disease that affects 5-8% of women and may be associated with metabolic syndrome, which is a risk factor for cardiovascular disease. Cortisol action and dysregulation account for metabolic syndrome development in the general population. As glucocorticoid receptor gene (NR3C1) polymorphisms regulate cortisol sensitivity, we hypothesized that variants of this gene may be involved in the adverse metabolic profiles of patients with polycystic ovary syndrome. METHOD: Clinical, metabolic and hormonal profiles were evaluated in 97 patients with polycystic ovary syndrome who were diagnosed according to the Rotterdam criteria. The alleles of the glucocorticoid gene were genotyped. Association analyses were performed using the appropriate statistical tests. RESULTS: Obesity and metabolic syndrome were observed in 42.3% and 26.8% of patients, respectively. Body mass index was positively correlated with blood pressure, triglyceride, LDL-c, total cholesterol, glucose and insulin levels as well as HOMA-IR values and inversely correlated with HDL-c and SHBG levels. The BclI and A3669G variants were found in 24.7% and 13.4% of alleles, respectively. BclI carriers presented a lower frequency of insulin resistance compared with wild-type subjects. CONCLUSION: The BclI variant is associated with a lower frequency of insulin resistance in women with polycystic ovary syndrome. Glucocorticoid gene polymorphism screening during treatment of the syndrome may be useful for identifying subgroups of at-risk patients who would benefit the most from personalized treatment.

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