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Untargeted Metabolomics and Body Mass in Adolescents: A Cross-Sectional and Longitudinal Analysis
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 consequen...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10456720/ https://www.ncbi.nlm.nih.gov/pubmed/37623843 http://dx.doi.org/10.3390/metabo13080899 |
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author | Singh, Amarnath Kinnebrew, Garrett Hsu, Ping-Ching Weng, Daniel Y. Song, Min-Ae Reisinger, Sarah A. McElroy, Joseph P. Keller-Hamilton, Brittney Ferketich, Amy K. Freudenheim, Jo L. Shields, Peter G. |
author_facet | Singh, Amarnath Kinnebrew, Garrett Hsu, Ping-Ching Weng, Daniel Y. Song, Min-Ae Reisinger, Sarah A. McElroy, Joseph P. Keller-Hamilton, Brittney Ferketich, Amy K. Freudenheim, Jo L. Shields, Peter G. |
author_sort | Singh, Amarnath |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-10456720 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-104567202023-08-26 Untargeted Metabolomics and Body Mass in Adolescents: A Cross-Sectional and Longitudinal Analysis Singh, Amarnath Kinnebrew, Garrett Hsu, Ping-Ching Weng, Daniel Y. Song, Min-Ae Reisinger, Sarah A. McElroy, Joseph P. Keller-Hamilton, Brittney Ferketich, Amy K. Freudenheim, Jo L. Shields, Peter G. Metabolites Article 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. MDPI 2023-07-30 /pmc/articles/PMC10456720/ /pubmed/37623843 http://dx.doi.org/10.3390/metabo13080899 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Singh, Amarnath Kinnebrew, Garrett Hsu, Ping-Ching Weng, Daniel Y. Song, Min-Ae Reisinger, Sarah A. McElroy, Joseph P. Keller-Hamilton, Brittney Ferketich, Amy K. Freudenheim, Jo L. Shields, Peter G. Untargeted Metabolomics and Body Mass in Adolescents: A Cross-Sectional and Longitudinal Analysis |
title | Untargeted Metabolomics and Body Mass in Adolescents: A Cross-Sectional and Longitudinal Analysis |
title_full | Untargeted Metabolomics and Body Mass in Adolescents: A Cross-Sectional and Longitudinal Analysis |
title_fullStr | Untargeted Metabolomics and Body Mass in Adolescents: A Cross-Sectional and Longitudinal Analysis |
title_full_unstemmed | Untargeted Metabolomics and Body Mass in Adolescents: A Cross-Sectional and Longitudinal Analysis |
title_short | Untargeted Metabolomics and Body Mass in Adolescents: A Cross-Sectional and Longitudinal Analysis |
title_sort | untargeted metabolomics and body mass in adolescents: a cross-sectional and longitudinal analysis |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10456720/ https://www.ncbi.nlm.nih.gov/pubmed/37623843 http://dx.doi.org/10.3390/metabo13080899 |
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