Columbia University, Vagelos College of Physicians & Surgeons, New York, New York, USA.
National Cancer Institute, National Institutes of Health, Rockville, Maryland, USA.
Obesity (Silver Spring). 2018 Apr;26 Suppl 2(Suppl 2):S25-S34. doi: 10.1002/oby.22156.
The responses to behavioral, pharmacological, or surgical obesity treatments are highly individualized. The Accumulating Data to Optimally Predict obesity Treatment (ADOPT) project provides a framework for how obesity researchers, working collectively, can generate the evidence base needed to guide the development of tailored, and potentially more effective, strategies for obesity treatment.
The objective of the ADOPT biological domain subgroup is to create a list of high-priority biological measures for weight-loss studies that will advance the understanding of individual variability in response to adult obesity treatments. This list includes measures of body composition, energy homeostasis (energy intake and output), brain structure and function, and biomarkers, as well as biobanking procedures, which could feasibly be included in most, if not all, studies of obesity treatment. The recommended high-priority measures are selected to balance needs for sensitivity, specificity, and/or comprehensiveness with feasibility to achieve a commonality of usage and increase the breadth and impact of obesity research.
The accumulation of data on key biological factors, along with behavioral, psychosocial, and environmental factors, can generate a more precise description of the interplay and synergy among them and their impact on treatment responses, which can ultimately inform the design and delivery of effective, tailored obesity treatments.
行为、药理学或手术肥胖治疗的反应具有高度个体差异性。Accumulating Data to Optimally Predict obesity Treatment（ADOPT）项目为肥胖研究人员提供了一个框架，使他们能够共同生成指导个体化、潜在更有效的肥胖治疗策略的发展所需的证据基础。
ADOPT 生物学领域分组的目的是创建一份高优先级的减肥研究生物学测量指标清单，以促进对成人肥胖治疗反应个体差异的理解。该清单包括身体成分、能量稳态（能量摄入和输出）、大脑结构和功能以及生物标志物的测量指标，以及生物银行程序，这些指标在大多数（如果不是全部）肥胖治疗研究中都有可能被纳入。推荐的高优先级测量指标是在灵敏度、特异性和/或全面性与可行性之间进行平衡，以实现通用性并增加肥胖研究的广度和影响。
随着关键生物学因素、行为、心理社会和环境因素数据的积累，可以更精确地描述它们之间的相互作用和协同作用，以及它们对治疗反应的影响，最终为有效、个体化的肥胖治疗的设计和实施提供信息。
