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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)...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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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 |
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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 |
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