Untargeted Metabolomics and Body Mass in Adolescents: A Cross-Sectional and Longitudinal Analysis

Author:

Singh Amarnath1ORCID,Kinnebrew Garrett2,Hsu Ping-Ching3ORCID,Weng Daniel Y.1,Song Min-Ae4ORCID,Reisinger Sarah A.5,McElroy Joseph P.6,Keller-Hamilton Brittney57,Ferketich Amy K.4,Freudenheim Jo L.8ORCID,Shields Peter G.1ORCID

Affiliation:

1. Comprehensive Cancer Center, The Ohio State University, Columbus, OH 43210-1240, USA

2. Department of Biomedical Informatics, Biomedical Informatics Shared Resources (BISR), The Ohio State University, Columbus, OH 43210-1240, USA

3. Department of Environmental Health Sciences, Fay W. Boozman College of Public Health, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA

4. College of Public Health, The Ohio State University, Columbus, OH 43210-1240, USA

5. Center for Tobacco Research, Comprehensive Cancer Center, The Ohio State University, Columbus, OH 43210-1240, USA

6. Center for Biostatistics, Department of Biomedical Informatics, The Ohio State University, Columbus, OH 43210-1240, USA

7. Department of Internal Medicine, College of Medicine, The Ohio State University, Columbus, OH 43210-1240, USA

8. Department of Epidemiology and Environmental Health, University at Buffalo, Buffalo, NY 14214, USA

Abstract

Obesity in children and adolescents has increased globally. Increased body mass index (BMI) during adolescence carries significant long-term adverse health outcomes, including chronic diseases such as cardiovascular disease, stroke, diabetes, and cancer. Little is known about the metabolic consequences of changes in BMI in adolescents outside of typical clinical parameters. Here, we used untargeted metabolomics to assess changing BMI in male adolescents. Untargeted metabolomic profiling was performed on urine samples from 360 adolescents using UPLC–QTOF-MS. The study includes a baseline of 235 subjects in a discovery set and 125 subjects in a validation set. Of them, a follow-up of 81 subjects (1 year later) as a replication set was studied. Linear regression analysis models were used to estimate the associations of metabolic features with BMI z-score in the discovery and validation sets, after adjusting for age, race, and total energy intake (kcal) at false-discovery-rate correction (FDR) ≤ 0.1. We identified 221 and 16 significant metabolic features in the discovery and in the validation set, respectively. The metabolites associated with BMI z-score in validation sets are glycylproline, citrulline, 4-vinylsyringol, 3′-sialyllactose, estrone sulfate, carnosine, formiminoglutamic acid, 4-hydroxyproline, hydroxyprolyl-asparagine, 2-hexenoylcarnitine, L-glutamine, inosine, N-(2-Hydroxyphenyl) acetamide glucuronide, and galactosylhydroxylysine. Of those 16 features, 9 significant metabolic features were associated with a positive change in BMI in the replication set 1 year later. Histidine and arginine metabolism were the most affected metabolic pathways. Our findings suggest that obesity and its metabolic outcomes in the urine metabolome of children are linked to altered amino acids, lipid, and carbohydrate metabolism. These identified metabolites may serve as biomarkers and aid in the investigation of obesity’s underlying pathological mechanisms. Whether these features are associated with the development of obesity, or a consequence of changing BMI, requires further study.

Funder

Ohio State University Comprehensive Cancer Center

National Cancer Institute and Food and Drug Administration’s Center

Publisher

MDPI AG

Subject

Molecular Biology,Biochemistry,Endocrinology, Diabetes and Metabolism

Reference81 articles.

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