EARLY DETECTION OF DISEASE ONSET USING NEW METABOLOME PHASE PORTRAITS

这个子项目是许多研究子项目中利用 资源由NIH/NCRR资助的中心拨款提供。子项目和 调查员(PI)可能从NIH的另一个来源获得了主要资金， 并因此可以在其他清晰的条目中表示。列出的机构是 该中心不一定是调查人员的机构。 我们建议应用多学科团队的方法来开发一种创新的平台(代谢组时相图)来检测复杂疾病的早期发作，该平台利用我们早期成功地识别急性炎症反应的生物标记物。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)为病例模型，快速识别看似无关的复杂表型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(如多囊卵巢综合征)并不遵循这种一级因果关系。它们不限于特定的基因、病原体、中毒或可识别的环境影响(例如，饮食)。发病后的生物标记物模式可能与早期的生物标记物不同。 PCOS是育龄妇女最常见的内分泌疾病，其表型复杂，诊断困难。PCOS的诊断包括间歇性或无月经周期、临床和/或雄激素过多的生化迹象，以及多囊卵巢的超声成像。多囊卵巢综合征也是一种排斥诊断，因为临床医生必须证明没有类似多囊卵巢综合征的疾病。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多囊卵巢综合征危及生命的后果至关重要。作为PCOS可靠早期指标的代谢物变化的识别、建模和检测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Abbott博士在我们团队中开发的产前雄激素(PA)雌性恒河猴模型来研究多囊卵巢综合征。动物模型表明，胎儿程序化是成人表型的常见病因。它为我们的团队提供了操作和跟踪多囊卵巢综合征发展的独特能力。结合UW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，我们将开发一份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。需要动力学知识来反向设计早期疾病状态下的生物标记物模式。我们的代谢组相图是代谢动力学的模型(WARF参考；P93081，P05416，P05420)，由全面和无偏的核磁共振(NMR)和质谱分析(MS)获得的实验代谢途径数据构建，并辅之以现有和假定的生化途径的知识。该模型将有助于对复杂疾病(如多囊卵巢综合征)进行准确的细分，对疾病进展进行分期，并阐明预防方案。与数据聚集模式的统计方法不同，我们构建多囊卵巢综合征代谢组相图的能力为我们提供了对潜在生物学的必要理解，并为治疗目的调节它的手段。

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.