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.

National Institute of Health (NIH)
National Center for Research Resources (NCRR)
Biotechnology Resource Grants (P41)
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Special Emphasis Panel (ZRG1-BCMB-H (40))
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University of Wisconsin Madison
Schools of Earth Sciences/Natur
United States
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