A Personalized Metabolomic Approach to Human Obesity and Weight Loss

 描述（由申请人提供）：该提案的主要目标是鉴定和改进新型代谢生物标志物，其逐步改善CVD风险预测模型并帮助预测改善代谢健康的最佳肥胖干预，以开始向预防CVD的更个性化的肥胖管理方法迈进。虽然肥胖是心血管疾病相关发病率和死亡率的公认风险因素，归因于中等风险因素的患病率增加，但肥胖个体中这些风险因素的患病率存在异质性。5这种脱节导致心血管疾病风险预测模型不完整，并且难以识别最需要强化治疗干预以预防心血管疾病事件的肥胖个体。新兴的分子分析技术已经开始填补这一空白。在我们以前工作的基础上，我们现在建议使用代谢组学分析来开发生物标志物，用于识别那些患有CVD风险最大的肥胖个体，重要的是，建立整合的“临床代谢”， 模型，这将有助于个性化的肥胖干预措施，以防止心血管疾病。我们的具体目标是：（1）检验先前鉴定的代谢特征与肥胖个体中CVD相关临床指标和肥胖干预相关的假设;（2）使用“无偏倚”代谢组学方法鉴定与这些生物标志物相关的新型生物标志物;（3）开发整合临床变量和代谢生物标志物的“临床-分子”模型。

 DESCRIPTION (provided by applicant): The primary goal of this proposal is to identify and refine novel metabolic biomarkers that incrementally improve CVD risk prediction models and help predict the best obesity intervention to improve metabolic health, to begin to move towards a more personalized approach to obesity management for prevention of CVD. While obesity is a well-recognized risk factor for CVD-related morbidity and mortality, attributed to the increased prevalence of intermediate risk factors, there is heterogeneity in the prevalence of these risk factors in obese individuals.5 This disconnect leads to incomplete CVD risk prediction models and difficulty in identification of those obese individuals at greatest need for intensive therapeutic interventions for prevention of CVD events. Emerging molecular profiling technologies have begun to fill this gap. Building on a foundation of our previous work, we now propose here to use metabolomics profiling to develop biomarkers for identification of those obese individuals at greatest risk of CVD and, importantly, to build integrated "clinico-metabolic" models that would facilitate personalization of obesity interventions to prevent CVD. Our Specific Aims are (1) to test the hypothesis that previously identified metabolic signatures are associated with CVD-related clinical measures in obese individuals and with obesity interventions; (2) to use "unbiased" metabolomics approaches to identify novel biomarkers associated with these biomarkers; and (3) to develop an integrated "clinico-molecular" model incorporating clinical variables and metabolic biomarkers.