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Assessment of microRNAs and metabolites plasma profiles in patients with Polycystic Ovary Syndrome

Grant number: 16/23253-0
Support type:Scholarships in Brazil - Doctorate (Direct)
Effective date (Start): April 01, 2017
Effective date (End): September 30, 2020
Field of knowledge:Health Sciences - Medicine - Maternal and Child Health
Principal Investigator:Gustavo Arantes Rosa Maciel
Grantee:Giovana de Nardo Maffazioli
Home Institution: Faculdade de Medicina (FM). Universidade de São Paulo (USP). São Paulo , SP, Brazil


Polycystic Ovary Syndrome (PCOS) has been associated with insulin resistance, hypertension and dyslipidemia, which are responsible for the increase in cardiovascular risk. However, a subgroup of patients does not present these metabolic disturbances, being classified as 'metabolic health'. Studies with the objective to differentiate such patients from the ones with metabolic compromising are scare and have conflicting results. The aim of the present study is to differentiate healthy non-PCOS subjects from the ones with PCOS with and without metabolic disturbances by analyzing clinical, laboratorial, epigenetics and metabolic characteristics. Clinical and anthropometrical data as well as blood samples will be collected from 60 patients (20 of each group). An expression panel of selected plasma microRNAs related to PCOS, metabolism, insulin resistance and risk for type II diabetes will be analyzed by quantitative real time PCR array (qRT-PCR). Furthermore, profile of 186 plasma metabolites (target metabolomics) related with carbohydrates, lipids and proteins metabolism will be analyzed by Electrospray Ionization (ESI) Tandem Mass Spectrometry (MS/MS). All results will be statistically analyzed in order to differentiate the three patient groups in relation to clinical and laboratorial data as well as to the miRNAs and metabolites profiles. We also intend to identify correlations between nutritional status, physical activity status, menstrual patterns, sex steroids, lipids, glycemic profile and inflammation markers with miRNA and metabolites profiles. Multivariate analysis will be used to identify potential predictors of PCOS and PCOS with and without metabolic compromising. (AU)

Matéria(s) publicada(s) na Agência FAPESP sobre a bolsa:
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