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A Multi-Omic Platform for Polycystic Ovary Syndrome Characterization and Management

A Multi-Omic Platform for Polycystic Ovary Syndrome Characterization and Management
用于多囊卵巢综合征特征和管理的多组学平台
批准号:
10080951
负责人:
Christopher Mason
金额:
$15.55万
依托单位:
依托单位国家:
美国
项目类别:
财政年份:
2020
资助国家:
美国
项目状态:
已结题
起止时间:
2020-09-22 至 2022-02-28

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Project Summary/Abstract Polycystic ovarian syndrome (PCOS) is the most common endocrine disorder in women of reproductive age. Due to the complex nature of PCOS, diagnosis is often delayed and options for treatment and self-management are limited. With recent developments in high-throughput, next- generation and metabolomics technologies, there is now a unique opportunity to utilize a cost- effective, multi-omic approach to study individuals with PCOS. In this application, we propose to apply these technologies to examine women with PCOS, developing a platform that is cost-effective, scalable, integrative, and highly accurate. Our multi- omic platform (FemBio) will integrate patient’s medical records, genomics, microbiome (shotgun metagenomics), and metabolomics to create a molecular profile of women with and without PCOS. This approach has the potential to provide a more comprehensive characterization of PCOS, to lead to the identification of improved diagnostic markers, and to allow for the discovery of novel targets for treatment. Moreover, we propose a prospective observational cohort study including whole genome sequencing, shotgun metagenomic sequencing of the gut microbiome, fecal metabolomics, serum and urine hormone levels, and clinical indices in a sample of individuals with PCOS and a healthy control group. This platform (FemBio) will enable broader accessibility to the latest in molecular assays, multi- omic quantification, and computational modeling for the general public, which currently is lacking. In turn, this will improve options for PCOS characterization, long-term management, and open potentially new treatment options for the millions of women suffering from this disorder, as well as serve as a large, annotated multi-omic data set that can help the PCOS field more broadly.
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