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

Sebastião Freitas de Medeiros; Ana Lin Winck Yamamoto de Medeiros; Matheus Antônio Souto de Medeiros; Anna Bethany da Silva Carvalho; Marcia W. Yamamoto; José M. Soares; Edmund C. Baracat

doi:10.1055/a-2207-0739

Hormone and Metabolic Research, Table of Contents

Horm Metab Res 2024; 56(06): 445-454
DOI: 10.1055/a-2207-0739

Original Article: Endocrine Care

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

Sebastião Freitas de Medeiros

¹First Department of Gynecology and Obstetrics, Medical School – Brazil, Federal University of Mato Grosso – Brazil, Cuiabá, Brazil

,

Ana Lin Winck Yamamoto de Medeiros

²Gynecology and Obstetrics, University of Cuiabá, Cuiabá MT, Brazil

,

Matheus Antônio Souto de Medeiros

³Tropical Institute of Reproductive Medicine, Cuiabá, Brazil

,

Anna Bethany da Silva Carvalho

⁴Biomedicina, UNIVAG, Varzea Grande, Brazil

,

Marcia W. Yamamoto

³Tropical Institute of Reproductive Medicine, Cuiabá, Brazil

,

José M. Soares

⁵Department of Obstetrics and Gynecology, Medical School, University of São Paulo, São Paulo, Brazil

,

Edmund C. Baracat

⁵Department of Obstetrics and Gynecology, Medical School, University of São Paulo, São Paulo, Brazil

› Author Affiliations

Abstract

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.

Key words

polycystic ovary syndrome - biomarkers - androgens - insulin resistance - dyslipidemia

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References

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