A metabolomics-based laboratory developed test to improve the diagnostic precision of Polycystic Ovary Syndrome

基于代谢组学的实验室开发了测试以提高多囊卵巢综合症的诊断精度

• 批准号: 10820801
• 负责人: Adam David Kennedy
• 金额: $ 29.77万
• 依托单位: METABOLON, INC.
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-18 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10820801
• 关键词: Biochemical; Biological; Biological Markers; Biology; Categories; Classification; Clinical; Clinical Data; Clinical Trials; Collaborations; Collection; Complex; Data; Development; Diagnosis; Diagnostic; Diagnostic Imaging; Diagnostic tests; Disease; Endocrine System Diseases; Environment; Follow-Up Studies; Genome; Goals; Guidelines; Health Status; Heterogeneity; High Pressure Liquid Chromatography; Hyperandrogenism; Individual; Label; Laboratories; Mass Spectrum Analysis; Measures; Metabolic; Metabolism; Outcome; Patient-Focused Outcomes; Patients; Phase; Phenotype; Polycystic Ovary Syndrome; Positioning Attribute; Precision therapeutics; Sampling; Serum; Small Business Innovation Research Grant; Statistical Data Interpretation; Symptoms; Techniques; Testing; Time; biomarker signature; chronic anovulation; clinical decision-making; clinically relevant; cohort; diagnostic criteria; evidence base; follow-up; improved; insight; lifestyle factors; medical schools; metabolic profile; metabolome; metabolomics; novel; patient stratification; personalized diagnostics; phenotypic biomarker; random forest; screening; small molecule; success; tandem mass spectrometry; tool

Abstract 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 individual’s 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 individual’s 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 today’s diagnostic guidelines, which represents a step towards improving clinical decision making, patient stratification in clinical trials, and overall patient outcomes.

摘要 多囊卵巢综合征(PCOS)是一种复杂的、多因素的内分泌疾病，其特征是 高雄激素血症、慢性无排卵和多囊卵巢。目前由鹿特丹诊断为 标准，将这些基本症状的表现分类为四种主要的表型，标记为A， B、C和D虽然这些表型可以定义疾病并在广义上为临床决策提供信息， 我们为临床试验选择最精确的治疗方法和合适的队列的能力仍然有限 因为表型之间有相当大的异质性，几乎没有数据可以用更高的 精确度。以更高的精度定义PCOS表型，从而改善患者的预后 将额外的循证诊断标准纳入当今的诊断指南。 在本阶段I SBIR中，我们将通过测试以下假设来满足对更高精度的需求 代谢组学数据结合临床症状可以更准确地确定PCOS表型 而不是仅仅有临床症状。 代谢物是小分子中间体和代谢产物，来自 基因组、环境和生活方式因素汇聚在一起。鉴于他们在核心教条中的独特地位 生物学上，它们被认为是一个人实时健康状态的最接近的反映。代谢物 通过其丰度的变化来反映疾病的活动，这可以使用超高性能来量化 高效液相色谱-串联质谱仪(UHPLC-MS/MS)。当以非目标方式使用时， UPLC-MS/MS可以测量给定生物样本(代谢组)中的整个代谢物集合， 能够对个人的生化特征进行广泛的筛查，以确定致病代谢 扰动(疾病的新陈代谢特征)。我们和其他人已经表明，新陈代谢特征与 多囊卵巢综合征可以提供对疾病活动的深入表型洞察。与理查德·莱格罗博士在 宾夕法尼亚州立医学院，新陈代谢公司将利用其专有的UHPLC-MS/MS平台NGPTM， 询问PCOS表型A和表型B独有的代谢特征，并利用高级统计 分析以确定代谢谱是否可以识别新的、临床相关的亚表型和 因此，定义表型比仅仅定义临床症状更准确。 在Success中，这个项目的发现将证明我们开发一种诊断测试的后续研究是合理的 针对这些定义表型的代谢特征。第一阶段成功的最终结果将是 开发一种工具，允许比目前的诊断更精确地定义PCOS表型 指南，这代表着朝着改善临床决策迈出的一步，临床中的患者分层 试验，以及患者的总体结果。