Machine learning‐based clustering identifies obesity subgroups with differential multi‐omics profiles and metabolic patterns

Although the two identified clusters may represent progressive obesity-related pathologic processes measured at different stages, other mechanisms in combination could also underpin the identified clusters given no significant age difference between the comparative groups. For instance, clusters may reflect differences in dietary/behavioral patterns or differential rates of metabolic damage.

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Publication Details

Journal
Obesity
Published
2024-11-01
DOI
https://doi.org/10.1002/oby.24137
Citations
12
Primary Topic
Nutritional Studies and Diet
Type
article
Field-Weighted Citation Impact
6.52

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article

Machine learning‐based clustering identifies obesity subgroups with differential multi‐omics profiles and metabolic patterns

Kristin Young, Maggie A. Stanislawski, Peter Durda, Mark O. Goodarzi et al.
12 citations
Obesity
Nutritional Studies and Diet
6.52
article

Machine learning‐based clustering identifies obesity subgroups with differential multi‐omics profiles and metabolic patterns

Kristin Young, Maggie A. Stanislawski, Peter Durda, Mark O. Goodarzi, Craig Johnson, Gina M. Peloso, Robert E. Gerszten, Victoria L. Buchanan, Clary B. Clish, Kristin Ardlie, Anne E. Justice, Kari E. North, Mohammad Yaser Anwar, Stephen S. Rich, François Aguet, Ching‐Ti Liu, Heather M. Highland, Xiuqing Guo, Mariaelisa Graff, Jingzhong Ding, Leslie A. Lange, Kent D. Taylor, Jerome I. Rotter, Yii‐Der Ida Chen, Russell P. Tracy
article en
12 citations

Abstract

Although the two identified clusters may represent progressive obesity-related pathologic processes measured at different stages, other mechanisms in combination could also underpin the identified clusters given no significant age difference between the comparative groups. For instance, clusters may reflect differences in dietary/behavioral patterns or differential rates of metabolic damage.

ObesityVol. 32(11)
Broad Institute (US), Boston University (US), University of Vermont (US), University of North Carolina at Chapel Hill (US), Cedars-Sinai Medical Center (US), Harvard University (US), University of Washington (US), UCLA Medical Center (US), Massachusetts General Hospital (US), Duke Medical Center (US), Harbor–UCLA Medical Center (US), Wake Forest University (US), University of Virginia (US), University of Colorado Anschutz Medical Campus (US), Geisinger Health System (US)
American Diabetes Association, Broad Institute, National Heart, Lung, and Blood Institute, National Human Genome Research Institute, National Institute of Diabetes and Digestive and Kidney Diseases, Eunice Kennedy Shriver National Institute of Child Health and Human Development
Openalex Percentile: Top 3%
Nutritional Studies and Diet
6.52
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