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 patientÂ’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.
Public Health Relevance Statement: Project Narrative 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 sequencing and molecular technologies for studying blood and urine, there is a unique opportunity to utilize a multi-omic approach to study individuals and disease and improve options for monitoring and therapy.
Project Terms: Age; Animals; Anovulation; Artificial Intelligence; Bacteria; Bacteriophages; base; Biological Assay; Blood; cardiovascular health; Cardiovascular system; Clinical; Clinical Trials; Cohort Studies; Complex; Computational algorithm; Computer Models; Consensus; Control Groups; convolutional neural network; cost effective; Custom; Data; Data Reporting; Data Set; design; Development; Diagnosis; diagnostic biomarker; diet and exercise; Disease; DNA; Endocrine System Diseases; Evaluation; Functional disorder; General Population; genome sequencing; Genomics; Goals; gut microbiome; Health; Home environment; Hormones; Hyperandrogenism; improved; indexing; Individual; Infertility; Irritable Bowel Syndrome; Lead; Literature; Measures; Medical; Metabolic; metabolomics; metagenome; metagenomic sequencing; Metagenomics; Methods; microbiome; Modality; Molecular; Molecular Profiling; Monitor; Morphology; Multiomic Data; multiple omics; Nature; next generation; next generation sequencing; Non-Insulin-Dependent Diabetes Mellitus; novel; Obesity; Oligonucleotides; Ovarian; ovarian dysfunction; patient health information; Patient Self-Report; Patients; personalized intervention; Phenotype; Plants; Polycystic Ovary Syndrome; prebiotics; Pregnancy Complications; Prevalence; Prevention; Probiotics; prospective; Recommendation; reproductive; Risk; Sampling; Self Management; Serum; Shotguns; small molecule; Spontaneous abortion; symptom management; Symptoms; System; Technology; Testing; Urine; Variant; Virus; Vitamins; whole genome; Woman; Work
