Moriah P Bellissimo1,2,3, Qingpo Cai4, Thomas R Ziegler2,3,5, Ken H Liu6,7, Phong H Tran2, Miriam B Vos8, Greg S Martin7, Dean P Jones3,6,7, Tianwei Yu4, Jessica A Alvarez2,3. 1. Nutrition and Health Sciences Doctoral Program, Laney Graduate School, Emory University, Atlanta, Georgia, USA. 2. Division of Endocrinology, Metabolism, and Lipids, Department of Medicine, School of Medicine, Emory University, Atlanta, Georgia, USA. 3. Emory Center for Clinical and Molecular Nutrition, Emory University, Atlanta, Georgia, USA. 4. Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA. 5. Section of Endocrinology, Atlanta Veterans Affairs Medical Center, Decatur, Georgia, USA. 6. Clinical Biomarkers Laboratory, Department of Medicine, School of Medicine, Emory University, Atlanta, Georgia, USA. 7. Division of Pulmonary, Allergy, Critical Care, and Sleep Medicine, Department of Medicine, School of Medicine, Emory University, Atlanta, Georgia, USA. 8. Division of Gastroenterology, Hepatology, and Nutrition, Department of Pediatrics, School of Medicine, Emory University, Atlanta, Georgia, USA.
Abstract
OBJECTIVE: This study explored underlying metabolism-related dysfunction by examining metabolomic profiles in adults categorized as lean, as having normal weight obesity (NWO), or as having overweight/obesity. METHODS: Participants (N = 179) had fasting plasma analyzed by liquid chromatography and high-resolution mass spectrometry for high-resolution metabolomics. Body composition was assessed by dual-energy x-ray absorptiometry. NWO was defined as BMI < 25 and body fat > 30% for women and > 23% for men. Differentiating metabolomic features were determined by using linear regression models and likelihood ratio tests with false discovery rate correction. Mummichog was used for pathway and network analyses. RESULTS: A total of 222 metabolites significantly differed between the groups at a false discovery rate of q = 0.2. Linoleic acid, β-alanine, histidine, and aspartate/asparagine metabolism pathways were significantly enriched (all P < 0.01) by metabolites that were similarly upregulated in the NWO and overweight/obesity groups compared with the lean group. A module analysis linked branched-chain amino acids and amino acid metabolites as elevated in the NWO and overweight/obesity groups compared with the lean group (all P < 0.05). CONCLUSIONS: Metabolomic profiles of individuals with NWO reflected similar metabolic disruption as those of individuals with overweight/obesity. High-resolution metabolomics may help identify people at risk for developing obesity-related disease, despite normal BMI.
OBJECTIVE: This study explored underlying metabolism-related dysfunction by examining metabolomic profiles in adults categorized as lean, as having normal weight obesity (NWO), or as having overweight/obesity. METHODS:Participants (N = 179) had fasting plasma analyzed by liquid chromatography and high-resolution mass spectrometry for high-resolution metabolomics. Body composition was assessed by dual-energy x-ray absorptiometry. NWO was defined as BMI < 25 and body fat > 30% for women and > 23% for men. Differentiating metabolomic features were determined by using linear regression models and likelihood ratio tests with false discovery rate correction. Mummichog was used for pathway and network analyses. RESULTS: A total of 222 metabolites significantly differed between the groups at a false discovery rate of q = 0.2. Linoleic acid, β-alanine, histidine, and aspartate/asparagine metabolism pathways were significantly enriched (all P < 0.01) by metabolites that were similarly upregulated in the NWO and overweight/obesity groups compared with the lean group. A module analysis linked branched-chain amino acids and amino acid metabolites as elevated in the NWO and overweight/obesity groups compared with the lean group (all P < 0.05). CONCLUSIONS: Metabolomic profiles of individuals with NWO reflected similar metabolic disruption as those of individuals with overweight/obesity. High-resolution metabolomics may help identify people at risk for developing obesity-related disease, despite normal BMI.
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