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Accumulating Data to Optimally Predict Obesity Treatment (ADOPT): Recommendations from the Biological Domain
Author(s) -
Rosenbaum Michael,
AgursCollins Tanya,
Bray Molly S.,
Hall Kevin D.,
Hopkins Mark,
Laughlin Maren,
MacLean Paul S.,
Maruvada Padma,
Savage Cary R.,
Small Dana M.,
Stoeckel Luke
Publication year - 2018
Publication title -
obesity
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.438
H-Index - 199
eISSN - 1930-739X
pISSN - 1930-7381
DOI - 10.1002/oby.22156
Subject(s) - obesity , domain (mathematical analysis) , medicine , medline , computer science , mathematics , biology , mathematical analysis , biochemistry
Background 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. Objectives 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. Significance 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.