Precision Medicine Approaches to Obesity Pharmacotherapy in Youth with Severe Obesity

项目总结 儿童重度肥胖(定义为体重指数≥为第95百分位数的1.2倍或体重指数≥为35公斤/平方米)为 增长最快的肥胖类别，影响到美国约6%的年轻人。患有严重肥胖症的年轻人 发生肥胖相关健康后果的重大风险，包括2型糖尿病和心血管疾病 疾病。肥胖症药物治疗是生活方式改变(LSM)治疗的一种有前途的辅助治疗 儿童肥胖治疗，因为仅靠LSM通常无法在临床上显著和持久地减轻体重 新陈代谢/减肥手术是侵入性的，带有手术风险，而且并不广泛使用。虽然肥胖 药物治疗与总体平均体重减轻有关，他们的个体有很大的变异性- 层级效能。美国国立卫生研究院(NIH)已经认识到识别 用药物基因组学方法研究与药物反应相关的表型特征 开发精准的药物治疗方法，并根据个人的情况优化用药剂量 特点，以提高儿童重度肥胖的治疗水平。这项建议的目的是 利用可用于精确医学的技术来识别相关的特定于人的特征 对肥胖药物治疗的减肥反应和影响剂量。具体来说，我们将使用(1) 电子健康记录(EHR)使临床发现能够识别预测体重的表型特征 减肥反应，(2)确定遗传易感性在减肥反应中所起作用的遗传风险分数， 以及(3)药代动力学/药效学(PK/PD)建模以开始识别个体化给药 肥胖症药物治疗方案在患有严重肥胖的青少年中的应用。我们将把这些技术应用于 托吡酯，一种通常用于患有严重肥胖的年轻人的减肥药物，已经被 与高度可变的个人层面的有效性相关。该项目将生成关键的初步数据 为R01评估托吡酯和其他肥胖反应的预测因素的设计提供信息 严重肥胖青年的药物治疗，包括目前正在使用的药物(例如，胰高血糖素样肽- 1(GLP1)受体激动剂)和将是(例如，胰升糖素-GLP1共激动剂，葡萄糖钠共转运体2/1 抑制剂)在未来可用。完成这项K23培训计划将使我能够建立电子病历的技能- 支持临床发现，并将遗传风险评分和PK/PD建模应用于临床研究，我将 需要成为肥胖管理精准医学领域的领先者。此外， 在这个职业发展奖期间，我将得到多学科的指导，这将为我成为 一位外部资助的内科科学家，能够实现独立和协作的大型 扩大临床研究规模。

PROJECT SUMMARY Pediatric severe obesity (defined as body mass index (BMI) ≥1.2 times the 95th percentile or BMI ≥35 kg/m2) is the fastest growing obesity category, affecting ~6% of youth in the U.S. Youth with severe obesity are at significant risk for developing obesity-related health consequences including type 2 diabetes and cardiovascular disease. Obesity pharmacotherapy is a promising adjunct to lifestyle modification (LSM) therapy for severe pediatric obesity treatment, as LSM alone generally fails to result in clinically significant and durable weight loss and metabolic/bariatric surgery is invasive, carries surgical risks, and is not widely available. While obesity pharmacotherapies are associated with overall mean weight loss, there is substantial variability in their individual- level effectiveness. The National Institutes of Health (NIH) has recognized the importance of identifying phenotypic characteristics associated with medication responsiveness, using pharmacogenomics approaches to develop precision pharmacologic treatments, and optimizing medication dosing based upon a person’s characteristics, in order to improve treatment of pediatric severe obesity. The objective of this proposal is to utilize techniques that can be used in precision medicine to identify person-specific characteristics associated with weight loss response to and affecting dosing of obesity pharmacotherapies. Specifically, we will use (1) electronic health record (EHR)-enabled clinical discovery to identify phenotypic characteristics predicting weight loss response, (2) genetic risk scores to determine the role genetic susceptibility plays in weight loss response, and (3) pharmacokinetic/pharmacodynamic (PK/PD) modeling to begin identifying individualized dosing regimens of obesity pharmacotherapies in youth with severe obesity. We will be applying these techniques to topiramate, a medication commonly prescribed for weight loss in youth with severe obesity that has been associated with highly variable individual-level effectiveness. This project will generate critical preliminary data to inform the design of an R01 evaluating predictors of response to topiramate and other obesity pharmacotherapies in youth with severe obesity, including agents that are currently (e.g., glucagon-like peptide- 1 (GLP1) receptor agonists) and will be (e.g., glucagon-GLP1 co-agonists, sodium glucose co-transporter 2/1 inhibitors) available in the future. Completing this K23 training program will allow me to establish skills in EHR- enabled clinical discovery and applying genetic risk scores and PK/PD modeling to clinical research that I will need in order to become a leader in the field of precision medicine for obesity management. Further, the multidisciplinary mentorship that I will receive during this career development award will prepare me to become an extramurally funded physician scientist capable of implementing both independent and collaborative large- scale clinical studies.