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 多囊卵巢综合征（PCOS）是一种复杂的多因素内分泌疾病，其特征是 超雄激素，慢性创新和多囊卵巢。目前由鹿特丹诊断 标准，这些标准将这些基本症状的呈现分类为四种标有A的主要表型 B，C和D。尽管这些表型可以定义疾病并在广义上为临床决策提供信息，但 我们选择最佳的精度治疗和适当的临床试验队列能力仍然有限 因为表型中存在相当大的异质性，并且很少有数据来定义它们 精确。定义具有更高精度的PCOS表型，从而改善了患者的预后 将基于证据的诊断标准纳入当今的诊断准则。 在此阶段I SBIR中，我们将通过测试使用的假设来满足更高精度的需求 代谢组学数据与临床症状结合使用，可以定义PCOS表型的精度更高 比仅临床症状。 代谢产物是小分子中间体和代谢产物 基因组，环境和生活方式因素融合。考虑到它们在中央教条中的独特位置 生物学被认为是个人实时健康状况的最接近反映。代谢物 通过其丰度变化来反映疾病活动，可以使用超高的性能进行量化 液相色谱和串联质谱法（UHPLC-MS/MS）。当以非目标方式使用时 UPLC-MS/MS可以在给定的生物学样本（代谢组）中测量整个代谢产物的集合， 能够广泛筛查个人的生化概况，以识别引起疾病的代谢 扰动（疾病的代谢特征）。我们和其他人表明，代谢特征与 使用PCOS可以对疾病活性提供深刻的表型洞察力。与Richard Legro博士合作 宾夕法尼亚州立医学院，Metabolon将利用其专有的UHPLC-MS/MS平台NGPTM 询问代谢特征A和表型B和表型B，并利用高水平的统计 分析以确定代谢分析是否可以识别新型，临床相关的亚表格型和 因此，仅比单独的临床症状更精确地定义表型。 在成功的过程中，该项目的发现将证明一项后续研究是合理的，我们在该研究中开发了一项诊断测试， 针对这些表型定义的代谢特征。成功阶段的最终结果我将是 开发允许PCOS表型的工具比当今的诊断更精确地定义 指南，这是改善临床决策的一步，临床中的患者分层 试验和整体患者结果。