EARLY DETECTION OF DISEASE ONSET USING NEW METABOLOME PHASE PORTRAITS

这个子项目是许多利用资源的研究子项目之一 由NIH/NCRR资助的中心拨款提供。子项目的主要支持 而子项目的主要调查员可能是由其他来源提供的， 包括其它NIH来源。 列出的子项目总成本可能 代表子项目使用的中心基础设施的估计数量， 而不是由NCRR赠款提供给子项目或子项目工作人员的直接资金。 我们建议应用多学科团队方法开发一个创新平台（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的生物标志物。我们计划系统地增强我们目前的代谢分析方法，以多囊卵巢综合征（PCOS）作为我们的病例模型，快速识别具有看似无关的复杂表型的疾病的早期和可靠的生物标志物。 我们战胜人类疾病的能力通常是基于对因果关系的理解。这种启发通常会产生预防，治疗或消除健康障碍的治疗设计和诊断方法。然而，复杂的疾病（例如PCOS）并不遵循这种一阶因果关系。它们不限于特定的基因、病原体、毒物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。 多囊卵巢综合征是育龄妇女最常见的内分泌疾病，表现为复杂和难以诊断的表型。PCOS诊断包括间歇性或无月经周期，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。PCOS也是一种排除诊断，因为临床医生必须证明缺乏类似PCOS的疾病。该综合征的主要健康并发症出现在其诊断标准之外，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，将显著改善患有该综合征的妇女的诊断和预后。 我们建议利用产前雄激素化（PA）雌性恒河猴模型PCOS，由Abbott博士在我们的小组。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UW医院正在进行的一项临床研究，该研究旨在确定PCOS患者肥胖的能量机制，我们将开发PCOS患者和PA猴的代谢组相图，以阐明生物标志物模式的动态。动力学的知识是需要在早期疾病状态的生物标志物模式的逆向工程。我们的代谢组相图是代谢动力学模型（WARF REF; P93081，P05416，P05420），由实验代谢途径数据构建，这些数据是从全面和无偏的核磁共振（NMR）和质谱（MS）分析中获得的，并辅以现有和推定的生化途径知识。该模型将有助于对复杂疾病（如PCOS）进行准确的子分类，对疾病进展进行分期，并阐明预防方案。与数据聚类模式的统计方法相反，我们构建PCOS代谢组相图的能力提供了对潜在生物学的必要理解，并提供了一种用于治疗目的的调节方法。

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. Primary support for the subproject and the subproject's principal investigator may have been provided by other sources, including other NIH sources. The Total Cost listed for the subproject likely represents the estimated amount of Center infrastructure utilized by the subproject, not direct funding provided by the NCRR grant to the subproject or subproject staff. 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.