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. 2023 Jul 30;13(8):899.
doi: 10.3390/metabo13080899.

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

Amarnath Singh  1 , Garrett Kinnebrew  2 , Ping-Ching Hsu  3 , Daniel Y Weng  1 , Min-Ae Song  4 , Sarah A Reisinger  5 , Joseph P McElroy  6 , Brittney Keller-Hamilton  5   7 , Amy K Ferketich  4 , Jo L Freudenheim  8 , Peter G Shields  1
Affiliations

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

Amarnath Singh et al. Metabolites. .

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.

Keywords: BMI z-score; adolescent obesity; metabolome; obesity; untargeted metabolomics.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
The flow diagram describes the discovery set, validation set, and replication set (1-year follow-up), data processing, statistical analysis, and a number of significant metabolites determined in each experiment that were used for pathway enrichment analysis.
Figure 2
Figure 2
The association between metabolites and BMI Z−score. Volcano plot: each point on the volcano plot was based on estimates of beta (β) from linear regression analysis on the x−axis versus the log10 FDR adj. p−value on the y−axis of the putatively identified metabolites. (A) Volcano plot of association of metabolites with BMI z−score in the discovery set; (B) volcano plot of association of metabolites with BMI z−score in the validation set (red dot for a positive association and blue dot for a negative association, horizontal dotted lines set false discovery rate 10% corrected p−value).
Figure 3
Figure 3
A pie chart summarizing the range and classes of compounds (super-class annotated by HMDB) demonstrates a significant association of metabolites with BMI z-score in the discovery set of urine samples from adolescents.
Figure 4
Figure 4
Pathway analysis as generated by MetaboAnalyst 5.0 software package for metabolites significantly expressed in adolescent children. The enrichment ratio is calculated as the number of hits within a particular metabolic pathway divided by the expected number of hits. Metabolite set enrichment analysis (MSEA). Top perturbed pathways are shown. (A) Metabolic pathway analysis using the list of 221 significant features in the discovery set, and (B) a list of 16 significant features in the validation set. The color depth and column length indicate the disturbance degree of the pathway.
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