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.

抽象的 多囊卵巢综合症（PCOS）是一种复杂的、多因素的内分泌疾病，其特征是 雄激素过多症、慢性无排卵和多囊卵巢目前由鹿特丹诊断。 标准，将这些基本症状的表现分类为四种主要表型，标记为 A， B、C 和 D。虽然这些表型可以从广义上定义疾病并为临床决策提供信息， 我们为临床试验选择最佳精准治疗和适当队列的能力仍然有限 因为表型之间存在相当大的异质性，并且很少有数据可以用更高的标准来定义它们 更精确地定义 PCOS 表型，从而改善患者的治疗结果 将额外的基于证据的诊断标准纳入当今的诊断指南。 在第一阶段 SBIR 中，我们将通过测试以下假设来解决对更高精确度的需求： 代谢组学数据与临床症状相结合可以更精确地定义 PCOS 表型 比单纯的临床症状更重要。 代谢物是小分子中间体和代谢产物，来自代谢物的输入依赖于代谢产物。 基因组、环境和生活方式因素在中心法则中具有独特的地位。 生物学上它们被认为是最接近个人实时健康状况的反映。 通过丰度的变化反映疾病活动，可以使用超高性能进行量化 液相色谱和串联质谱 (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品中代谢物的全部集合（代谢组）， 能够广泛筛查个体的生化特征，以识别引起疾病的代谢 我们和其他人已经证明代谢特征相关。 与 PCOS 的研究可以提供对疾病活动的深入表型洞察。 宾夕法尼亚州立大学医学院的 Metabolon 将利用其专有的 UHPLC-MS/MS 平台 NGPTM 询问 PCOS 表型 A 和表型 B 特有的代谢特征，并利用高水平统计数据 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 从而比单独的临床症状更准确地定义表型。 如果成功的话，该项目的结果将证明后续研究的合理性，在该研究中我们开发了一种诊断测试， 针对这些表型定义的代谢特征，第一阶段成功的最终结果将是 开发一种工具，可以比当今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、临床患者分层迈出的一步 试验和总体患者结果。