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The Accumulating Data to Optimally Predict Obesity Treatment (ADOPT): Recommendations from the Biological Domain
BACKGROUND: The responses to behavioral, pharmacological, or surgical obesity treatments are highly individualized. ADOPT 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...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6945498/ https://www.ncbi.nlm.nih.gov/pubmed/29575784 http://dx.doi.org/10.1002/oby.22156 |
Sumario: | BACKGROUND: The responses to behavioral, pharmacological, or surgical obesity treatments are highly individualized. ADOPT 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. OBJECTIVE: 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 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. |
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