Cargando…

Metabolomic Predictors of Dysglycemia in Two U.S. Youth Cohorts

Here, we seek to identify metabolite predictors of dysglycemia in youth. In the discovery analysis among 391 youth in the Exploring Perinatal Outcomes among CHildren (EPOCH) cohort, we used reduced rank regression (RRR) to identify sex-specific metabolite predictors of impaired fasting glucose (IFG)...

Descripción completa

Detalles Bibliográficos
Autores principales: Perng, Wei, Hivert, Marie-France, Michelotti, Gregory, Oken, Emily, Dabelea, Dana
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9147862/
https://www.ncbi.nlm.nih.gov/pubmed/35629908
http://dx.doi.org/10.3390/metabo12050404
_version_ 1784716911546728448
author Perng, Wei
Hivert, Marie-France
Michelotti, Gregory
Oken, Emily
Dabelea, Dana
author_facet Perng, Wei
Hivert, Marie-France
Michelotti, Gregory
Oken, Emily
Dabelea, Dana
author_sort Perng, Wei
collection PubMed
description Here, we seek to identify metabolite predictors of dysglycemia in youth. In the discovery analysis among 391 youth in the Exploring Perinatal Outcomes among CHildren (EPOCH) cohort, we used reduced rank regression (RRR) to identify sex-specific metabolite predictors of impaired fasting glucose (IFG) and elevated fasting glucose (EFG: Q4 vs. Q1 fasting glucose) 6 years later and compared the predictive capacity of four models: Model 1: ethnicity, parental diabetes, in utero exposure to diabetes, and body mass index (BMI); Model 2: Model 1 covariates + baseline waist circumference, insulin, lipids, and Tanner stage; Model 3: Model 2 + baseline fasting glucose; Model 4: Model 3 + baseline metabolite concentrations. RRR identified 19 metabolite predictors of fasting glucose in boys and 14 metabolite predictors in girls. Most compounds were on lipid, amino acid, and carbohydrate metabolism pathways. In boys, no improvement in aurea under the receiver operating characteristics curve AUC occurred until the inclusion of metabolites in Model 4, which increased the AUC for prediction of IFG (7.1%) from 0.81 to 0.97 (p = 0.002). In girls, %IFG was too low for regression analysis (3.1%), but we found similar results for EFG. We replicated the results among 265 youth in the Project Viva cohort, focusing on EFG due to low %IFG, suggesting that the metabolite profiles identified herein have the potential to improve the prediction of glycemia in youth.
format Online
Article
Text
id pubmed-9147862
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-91478622022-05-29 Metabolomic Predictors of Dysglycemia in Two U.S. Youth Cohorts Perng, Wei Hivert, Marie-France Michelotti, Gregory Oken, Emily Dabelea, Dana Metabolites Article Here, we seek to identify metabolite predictors of dysglycemia in youth. In the discovery analysis among 391 youth in the Exploring Perinatal Outcomes among CHildren (EPOCH) cohort, we used reduced rank regression (RRR) to identify sex-specific metabolite predictors of impaired fasting glucose (IFG) and elevated fasting glucose (EFG: Q4 vs. Q1 fasting glucose) 6 years later and compared the predictive capacity of four models: Model 1: ethnicity, parental diabetes, in utero exposure to diabetes, and body mass index (BMI); Model 2: Model 1 covariates + baseline waist circumference, insulin, lipids, and Tanner stage; Model 3: Model 2 + baseline fasting glucose; Model 4: Model 3 + baseline metabolite concentrations. RRR identified 19 metabolite predictors of fasting glucose in boys and 14 metabolite predictors in girls. Most compounds were on lipid, amino acid, and carbohydrate metabolism pathways. In boys, no improvement in aurea under the receiver operating characteristics curve AUC occurred until the inclusion of metabolites in Model 4, which increased the AUC for prediction of IFG (7.1%) from 0.81 to 0.97 (p = 0.002). In girls, %IFG was too low for regression analysis (3.1%), but we found similar results for EFG. We replicated the results among 265 youth in the Project Viva cohort, focusing on EFG due to low %IFG, suggesting that the metabolite profiles identified herein have the potential to improve the prediction of glycemia in youth. MDPI 2022-04-29 /pmc/articles/PMC9147862/ /pubmed/35629908 http://dx.doi.org/10.3390/metabo12050404 Text en © 2022 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
Perng, Wei
Hivert, Marie-France
Michelotti, Gregory
Oken, Emily
Dabelea, Dana
Metabolomic Predictors of Dysglycemia in Two U.S. Youth Cohorts
title Metabolomic Predictors of Dysglycemia in Two U.S. Youth Cohorts
title_full Metabolomic Predictors of Dysglycemia in Two U.S. Youth Cohorts
title_fullStr Metabolomic Predictors of Dysglycemia in Two U.S. Youth Cohorts
title_full_unstemmed Metabolomic Predictors of Dysglycemia in Two U.S. Youth Cohorts
title_short Metabolomic Predictors of Dysglycemia in Two U.S. Youth Cohorts
title_sort metabolomic predictors of dysglycemia in two u.s. youth cohorts
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9147862/
https://www.ncbi.nlm.nih.gov/pubmed/35629908
http://dx.doi.org/10.3390/metabo12050404
work_keys_str_mv AT perngwei metabolomicpredictorsofdysglycemiaintwousyouthcohorts
AT hivertmariefrance metabolomicpredictorsofdysglycemiaintwousyouthcohorts
AT michelottigregory metabolomicpredictorsofdysglycemiaintwousyouthcohorts
AT okenemily metabolomicpredictorsofdysglycemiaintwousyouthcohorts
AT dabeleadana metabolomicpredictorsofdysglycemiaintwousyouthcohorts