EARLY DETECTION OF DISEASE ONSET USING NEW METABOLOME PHASE PORTRAITS

使用新的代谢组相图早期检测疾病发作

• 批准号: 8168980
• 负责人: FARIBA M ASSADI-PORTER
• 金额: $ 3.84万
• 依托单位: UNIVERSITY OF WISCONSIN-MADISON
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-03-01 至 2011-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8168980
• 关键词: Acute; Adult; Androgens; Animal Model; Biochemical; Biochemical Pathway; Biological Markers; Biology; Cardiovascular Diseases; Chronic; Classification; Clinical; Clinical Research; Complement; Complex; Computer Retrieval of Information on Scientific Projects Database; Data; Detection; Development; Diagnosis; Diagnostic; Diet; Disease; Disease Progression; Early Diagnosis; Endometrial Carcinoma; Engineering; Etiology; Exclusion; Female; Funding; Genes; Grant; Health; Hospitals; Hyperlipidemia; Inflammation; Inflammatory Response; Institution; Intervention; Knowledge; Life; Macaca mulatta; Mass Spectrum Analysis; Menstrual cycle; Metabolic; Metabolic Pathway; Methodology; Modeling; Monkeys; Non-Insulin-Dependent Diabetes Mellitus; Nuclear Magnetic Resonance; Obesity; Onset of illness; Patients; Pattern; Phase; Phenotype; Polycystic Ovary Syndrome; Portraits; Prevention approach; Preventive; Regimen; Research; Research Personnel; Resources; Sleep Apnea Syndromes; Source; Staging; Syndrome; Therapeutic; Ultrasonography; United States National Institutes of Health; Woman; aged; base; design; early onset; fetal programming; human disease; improved; innovation; multidisciplinary; outcome forecast; pathogen; prevent; reproductive; success

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. We propose to apply a multidisciplinary team approach to develop an innovative platform (metabolome phase portrait) to detect early onset of complex disease that capitalizes on our earlier success identifying biomarkers of acute inflammatory response. We plan to systematically enhance our current metabolic analysis methodology to rapidly identify early and reliable biomarkers of diseases with seemingly unrelated complex phenotypes using polycystic ovary syndrome (PCOS) as our case model. Our ability to overcome human disease is conventionally based on understanding causation. Such enlightenment usually generates therapeutic designs and diagnostic approaches for prevention, treatment, or elimination of the health disorder. Complex diseases (e.g. PCOS), however, do not follow such first order causes and effect. They are not limited to specific genes, pathogens, toxicoses, or identifiable environmental influences (e.g., diet). Biomarker patterns after disease onset may not be the same as biomarkers at early stages. PCOS is the most prevalent endocrinopathy in reproductive aged women and presents a complex and difficult to diagnose phenotype. PCOS diagnosis includes intermittent or absent menstrual cycles, clinical and/or biochemical signs of androgen excess, and ultrasound imaging of polycystic ovaries. PCOS is also a diagnosis of exclusion, since clinicians must demonstrate a lack of disorders that mimics PCOS. The syndrome's major health complications arise outside its diagnostic criteria and include obesity, type 2 diabetes, hyperlipidemia, cardiovascular disease, endometrial cancer, sleep apnea, and chronic inflammation. Early diagnosis and intervention is thus crucial to prevent the life-threatening consequences of PCOS. Identification, modeling, and detection of metabolite changes that are reliable early indicators of PCOS will significantly improve both the diagnosis of and prognosis for women afflicted with the syndrome. We propose to utilize the prenatally androgenized (PA) female rhesus monkey model for PCOS, developed by Dr. Abbott in our group. The animal model indicates fetal programming as the common etiology for the adult phenotype. It provides our team with a unique ability to manipulate and track the development of PCOS. Combined with an ongoing clinical study at UW Hospital that is geared toward determining energetic mechanisms underlying obesity in PCOS patients, we will develop a metabolome phase portrait of PCOS patients and PA monkeys that will elucidate the dynamics of biomarker patterns. Knowledge of dynamics is required for reverse-engineering the biomarker patterns in early disease states. Our metabolome phase portrait is a model of the metabolic dynamics (WARF REF; P93081, P05416, P05420), constructed from experimental metabolic pathway data obtained from comprehensive and unbiased nuclear magnetic resonance (NMR) and mass-spectrometry (MS) analyses complemented by knowledge of existing and putative biochemical pathways. This model will facilitate accurate sub-classification of a complex disease such as PCOS, staging of the disease progression, and elucidation of preventive regimens. In contrast to statistical approaches for clustering patterns of data, our ability to construct a mtabolome phase portrait of PCOS provides the needed understanding of the underlying biology with a means to modulate it for therapeutic purposes.

该子项目是利用该技术的众多研究子项目之一 资源由 NIH/NCRR 资助的中心拨款提供。子项目及 研究者 (PI) 可能已从 NIH 的另一个来源获得主要资金， 因此可以在其他 CRISP 条目中表示。列出的机构是 对于中心来说，它不一定是研究者的机构。 我们建议应用多学科团队方法来开发一个创新平台（代谢组相图）来检测复杂疾病的早期发作，该平台利用我们早期成功识别急性炎症反应生物标志物的成果。我们计划系统地增强我们当前的代谢分析方法，以使用多囊卵巢综合征（PCOS）作为我们的案例模型，快速识别具有看似不相关的复杂表型的疾病的早期和可靠的生物标志物。 我们克服人类疾病的能力通常基于对因果关系的理解。这种启示通常会产生用于预防、治疗或消除健康障碍的治疗设计和诊断方法。然而，复杂疾病（例如多囊卵巢综合症）并不遵循这种第一顺序的因果关系。它们不限于特定基因、病原体、中毒或可识别的环境影响（例如饮食）。疾病发作后的生物标志物模式可能与早期阶段的生物标志物不同。 多囊卵巢综合症是育龄妇女中最常见的内分泌疾病，表现出复杂且难以诊断的表型。 PCOS 诊断包括月经周期间歇或缺失、雄激素过多的临床和/或生化体征以及多囊卵巢的超声成像。多囊卵巢综合症也是一种排除性诊断，因为临床医生必须证明不存在类似多囊卵巢综合症的疾病。该综合征的主要健康并发症超出了其诊断标准，包括肥胖、2 型糖尿病、高脂血症、心血管疾病、子宫内膜癌、睡眠呼吸暂停和慢性炎症。因此，早期诊断和干预对于预防 PCOS 危及生命的后果至关重要。代谢物变化的识别、建模和检测是 PCOS 的可靠早期指标，将显着改善患有该综合征的女性的诊断和预后。 我们建议利用由我们小组的 Abbott 博士开发的产前雄激素化 (PA) 雌性恒河猴模型来治疗 PCOS。动物模型表明胎儿编程是成人表型的常见病因。它为我们的团队提供了操纵和跟踪 PCOS 发展的独特能力。结合华盛顿大学医院正在进行的一项旨在确定 PCOS 患者肥胖背后的能量机制的临床研究，我们将开发 PCOS 患者和 PA 猴子的代谢组相图，以阐明生物标志物模式的动态。对早期疾病状态的生物标志物模式进行逆向工程需要动力学知识。我们的代谢组相图是代谢动力学模型（WARF REF；P93081、P05416、P05420），由通过全面且无偏的核磁共振 (NMR) 和质谱 (MS) 分析获得的实验代谢途径数据构建，并辅以知识现有和假定的生化途径。该模型将有助于对 PCOS 等复杂疾病进行准确的细分、疾病进展的分期以及预防方案的阐明。与数据聚类模式的统计方法相比，我们构建 PCOS 代谢组相图的能力提供了对基础生物学的必要理解，并提供了一种调节其用于治疗目的的方法。