Anthropometric, Metabolic, and Endocrine Parameters as Predictors of Estimated Average Glucose and Other Biomarkers of Dysglycemia in Women with Different Phenotypes of Polycystic Ovary Syndrome.

Autor:	de Medeiros SF; First Department of Gynecology and Obstetrics, Medical School - Brazil, Federal University of Mato Grosso - Brazil, Cuiabá, Brazil., Winck Yamamoto de Medeiros AL; Gynecology and Obstetrics, University of Cuiabá, Cuiabá MT, Brazil., Souto de Medeiros MA; Tropical Institute of Reproductive Medicine, Cuiabá, Brazil., da Silva Carvalho AB; Biomedicina, UNIVAG, Varzea Grande, Brazil., Yamamoto MW; Tropical Institute of Reproductive Medicine, Cuiabá, Brazil., M Soares J; Department of Obstetrics and Gynecology, Medical School, University of São Paulo, São Paulo, Brazil., Baracat EC; Department of Obstetrics and Gynecology, Medical School, University of São Paulo, São Paulo, Brazil.
Jazyk:	angličtina
Zdroj:	Hormone and metabolic research = Hormon- und Stoffwechselforschung = Hormones et metabolisme [Horm Metab Res] 2024 Jun; Vol. 56 (6), pp. 445-454. Date of Electronic Publication: 2023 Nov 08.
DOI:	10.1055/a-2207-0739
Abstrakt:	The aim of the study was to evaluate the efficacy of anthropometric, metabolic, and endocrine abnormalities as predictors of estimated average glucose and other biomarkers of dysglycemia in women with different phenotypes of polycystic ovary syndrome (PCOS). This cross-sectional study included 648 women with PCOS and 330 controls. A single protocol of investigation was applied for all subjects. PCOS women were divided by phenotypes according to the Rotterdam criteria. Biomarkers of dysglycemia were considered dependent variables and anthropometric, lipid, and hormone alterations as independent variables using univariate and multivariate logistic regressions. Univariate logistic regression analysis, controlled for age and BMI, showed that many biomarkers of dysglycemia could be predicted by anthropometric, lipid, and endocrine variables. Multivariate logistic models showed that in non-PCOS women estimated average glucose (eAG) was predicted by lower TSH levels (OR=0.39; p=0.045); fasting glucose was predicted by increased T (OR=2.3). For PCOS, phenotype A, eAG was predicted by decreased HDL-C (OR=0.17, p=0.023) and high levels of free estradiol (OR=7.1, p<0.001). Otherwise, in PCOS, phenotype D, eAG was predicted by higher levels of HDL-C. The current study demonstrated that eAG was poorly predicted by anthropometric, lipid, and hormone parameters. Nevertheless, without adding significant benefits, it was comparable with other established markers of dysglycemia in women with different PCOS phenotypes. Competing Interests: The authors declare that they have no conflict of interest. (Thieme. All rights reserved.)
Databáze:	MEDLINE
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