Polycystic ovary syndrome (PCOS) is a complex, multifactorial endocrine disorder characterized by hyperandrogenism, chronic anovulation, and polycystic ovaries. It is currently diagnosed by the Rotterdam criteria, which categorizes the presentation of these basic symptomologies into four main phenotypes labeled A, B, C, and D. While these phenotypes can define disease and inform clinical decision making in a broad sense, our ability to select the best precision treatments and appropriate cohorts for clinical trials remains limited because there is considerable heterogeneity within phenotypes and little data by which to define them with higher precision. Defining PCOS phenotypes with higher precision to thereby improve patient outcomes starts by incorporating additional, evidence-based diagnostic criteria into present day diagnostic guidelines. In this Phase I SBIR, we will address the need for greater precision by testing the hypothesis that using metabolomics data in conjunction with clinical symptoms can define PCOS phenotypes with higher precision than clinical symptoms alone. Metabolites are the small molecule intermediates and products of metabolism upon which the inputs from the genome, the environment, and lifestyle factors converge. Given their unique position in the central dogma of biology they are considered to be the closest reflection of an individualâs real-time health status. Metabolites reflect disease activity through changes in their abundance, which can be quantified using ultra-high performance liquid chromatography and tandem mass spectrometry (UHPLC-MS/MS). When used in an untargeted manner, UPLC-MS/MS can measure the entire collection of metabolites in a given biological sample (the metabolome), enabling broad screening of an individualâs biochemical profile to identify disease-causing metabolic perturbations (metabolic signatures of disease). We and others have shown that metabolic signatures associated with PCOS can provide deep phenotypic insight into disease activity. In collaboration with Dr. Richard Legro at Penn State Medical School, Metabolon will leverage its proprietary UHPLC-MS/MS platform, NGPTM, to interrogate metabolic signatures unique to PCOS phenotype A and phenotype B and utilize high level statistical analyses to determine whether metabolic profiling can identify novel, clinically relevant sub-phenotypes and thereby define phenotypes more precisely than clinical symptoms alone. In success, the findings of this project will justify a follow-up study in which we develop a diagnostic test that targets these phenotype-defining metabolic signatures. The ultimate outcome of a successful Phase I will be the development of a tool that allows PCOS phenotypes to be defined more precisely than todayâs diagnostic guidelines, which represents a step towards improving clinical decision making, patient stratification in clinical trials, and overall patient outcomes.
Public Health Relevance Statement: NARRATIVE The goal of this project is to test the hypothesis that metabolomics data used in conjunction with clinical symptoms can reveal novel and clinically relevant sub-phenotypes of polycystic ovary syndrome (PCOS). We will use Metabolonâs proprietary mass spectrometry platform to interrogate global metabolic profiles of serum collected from patients diagnosed with either PCOS phenotype A or phenotype B. We will then evaluate biomarker clustering using Random Forest Analysis to determine if metabolomics data can reveal novel phenotypes and thereby improve the precision of diagnostic classification.
Project Terms: Biology; High Pressure Liquid Chromatography; HPLC; High Performance Liquid Chromatography; High Speed Liquid Chromatography; Classification; Systematics; Clinical Trials; Statistical Data Interpretation; Statistical Data Analyses; Statistical Data Analysis; statistical analysis; Diagnosis; Diagnostic Imaging; Disease; Disorder; Endocrine System Diseases; Endocrine Diseases; Endocrine Diseases and Manifestations; endocrine disorder; Environment; Follow-Up Studies; Followup Studies; Genome; Goals; Health Status; Level of Health; Heterogeneity; Laboratories; Metabolism; Intermediary Metabolism; Metabolic Processes; Patients; Phenotype; Polycystic Ovary Syndrome; Polycystic Ovarian Disease; Polycystic Ovarian Syndrome; Sclerocystic Ovarian Degeneration; Sclerocystic Ovary Syndrome; polycystic ovary; polycystic ovary disease; polycystic ovary disorder; medical schools; medical college; school of medicine; Mass Spectrum Analysis; Mass Photometry/Spectrum Analysis; Mass Spectrometry; Mass Spectroscopy; Mass Spectrum; Mass Spectrum Analyses; Testing; Time; Measures; Diagnostic tests; Guidelines; Label; improved; Clinical; Phase; biologic; Biological; Biochemical; Hyperandrogenism; Blood Serum; Serum; insight; Individual; Collaborations; Metabolic; tool; Diagnostic; Complex; Techniques; success; tandem mass spectrometry; cohort; novel; Categories; Positioning Attribute; Position; Sampling; metabolomics; metabolism measurement; metabonomics; small molecule; Symptoms; Data; Clinical Data; Collection; lifestyle factors; life-style factor; Patient-Focused Outcomes; Patient outcome; Patient-Centered Outcomes; patient oriented outcomes; Small Business Innovation Research Grant; SBIR; Small Business Innovation Research; follow-up; Active Follow-up; active followup; follow up; followed up; followup; Development; developmental; Outcome; clinical relevance; clinically relevant; evidence base; bio-markers; biologic marker; biomarker; Biological Markers; clinical decision-making; screenings; screening; precision therapies; precision treatment; Precision therapeutics; personalized diagnosis; precise diagnostics; precision diagnostics; personalized diagnostics; stratified patient; patient stratification; phenotypic marker; phenotypic biomarker; metabolic profile; metabonome; metabolome; random forest; biomarker signature; chronic anovulation; diagnostic c
