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Title: | Synergizing Mouse and Human Studies to Understand the Heterogeneity of Obesity |
Authors: | Gordon-Larsen, Penny French, John E Moustaid-Moussa, Naima Voruganti, Venkata S Mayer-Davis, Elizabeth J Bizon, Christopher A Cheng, Zhiyong Stewart, Delisha A Easterbrook, John W Shaikh, Saame Raza |
Keywords: | heterogeneity obesities mouse human pre-clinical clinical prevention treatment nutrition symposium |
Issue Date: | 2021 |
Publisher: | Advances in Nutrition |
Series/Report no.: | Review;2023-2034 |
Abstract: | Obesity is routinely considered as a single disease state, which drives a “one-size-fits-all”approach to treatment.We recently convened the first annual University of North Carolina Interdisciplinary Nutrition Sciences Symposium to discuss the heterogeneity of obesity and the need for translational science to advance understanding of this heterogeneity. The symposium aimed to advance scientific rigor in translational studies from animal to human models with the goal of identifying underlying mechanisms and treatments. In this review, we discuss fundamental gaps in knowledge of the heterogeneity of obesity ranging from cellular to population perspectives. We also advocate approaches to overcoming limitations in the field. Examples include the use of contemporary mouse genetic reference population models such as the Collaborative Cross and Diversity Outbred mice that effectively model human genetic diversity and the use of translational models that integrate -omics and computational approaches from preclinical to clinical models of obesity. Finally, we suggest best scientific practices to ensure strong rigor that will allow investigators to delineate the sources of heterogeneity in the population with obesity. Collectively,we propose that it is critical to think of obesity as a heterogeneous diseasewith complex mechanisms and etiologies, requiring unique prevention and treatment strategies tailored to the individual |
URI: | http://localhost:8080/xmlui/handle/123456789/5029 |
Appears in Collections: | VOL 12 NO 5 (2021) |
Files in This Item:
File | Description | Size | Format | |
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2023-2034.pdf | 263.36 kB | Adobe PDF | View/Open |
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