Identification of metabolic markers predictive of prediabetes in a Korean population

Abstract Prediabetes (PD) is a high-risk state of developing type 2 diabetes, and cardiovascular and metabolic diseases. Metabolomics-based biomarker studies can provide advanced opportunities for prediction of PD over the conventional methods. Here, we aimed to identify metabolic markers and verify...

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Autores principales:	Heun-Sik Lee, Tae-Joon Park, Jeong-Min Kim, Jun Ho Yun, Ho-Yeong Yu, Yeon-Jung Kim, Bong-Jo Kim
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/1a108d976a64439c8f3896d421c13bf3
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spelling	oai:doaj.org-article:1a108d976a64439c8f3896d421c13bf32021-12-02T13:58:10ZIdentification of metabolic markers predictive of prediabetes in a Korean population10.1038/s41598-020-78961-42045-2322https://doaj.org/article/1a108d976a64439c8f3896d421c13bf32020-12-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-78961-4https://doaj.org/toc/2045-2322Abstract Prediabetes (PD) is a high-risk state of developing type 2 diabetes, and cardiovascular and metabolic diseases. Metabolomics-based biomarker studies can provide advanced opportunities for prediction of PD over the conventional methods. Here, we aimed to identify metabolic markers and verify their abilities to predict PD, as compared to the performance of the traditional clinical risk factor (CRF) and previously reported metabolites in other population-based studies. Targeted metabolites quantification was performed in 1723 participants in the Korea Association REsource (KARE) cohort, from which 500 normal individuals were followed up for 6 years. We selected 12 significant metabolic markers, including five amino acids, four glycerophospholipids, two sphingolipids, and one acylcarnitine, at baseline, resulting in a predicted incidence of PD with an area under the curve (AUC) of 0.71 during follow-up. The performance of these metabolic markers compared to that of fasting glucose was significantly higher in obese patients (body mass index: BMI ≥ 25 kg/m2, 0.79 vs. 0.58, P < 0.001). The combination with metabolic markers, CRF, and fasting glucose yielded the best prediction performance (AUC = 0.86). Our results revealed that metabolic markers were not only associated with the risk of PD, but also improved the prediction performance in combination with conventional approaches.Heun-Sik LeeTae-Joon ParkJeong-Min KimJun Ho YunHo-Yeong YuYeon-Jung KimBong-Jo KimNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 10, Iss 1, Pp 1-9 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Heun-Sik Lee Tae-Joon Park Jeong-Min Kim Jun Ho Yun Ho-Yeong Yu Yeon-Jung Kim Bong-Jo Kim Identification of metabolic markers predictive of prediabetes in a Korean population
description	Abstract Prediabetes (PD) is a high-risk state of developing type 2 diabetes, and cardiovascular and metabolic diseases. Metabolomics-based biomarker studies can provide advanced opportunities for prediction of PD over the conventional methods. Here, we aimed to identify metabolic markers and verify their abilities to predict PD, as compared to the performance of the traditional clinical risk factor (CRF) and previously reported metabolites in other population-based studies. Targeted metabolites quantification was performed in 1723 participants in the Korea Association REsource (KARE) cohort, from which 500 normal individuals were followed up for 6 years. We selected 12 significant metabolic markers, including five amino acids, four glycerophospholipids, two sphingolipids, and one acylcarnitine, at baseline, resulting in a predicted incidence of PD with an area under the curve (AUC) of 0.71 during follow-up. The performance of these metabolic markers compared to that of fasting glucose was significantly higher in obese patients (body mass index: BMI ≥ 25 kg/m2, 0.79 vs. 0.58, P < 0.001). The combination with metabolic markers, CRF, and fasting glucose yielded the best prediction performance (AUC = 0.86). Our results revealed that metabolic markers were not only associated with the risk of PD, but also improved the prediction performance in combination with conventional approaches.
format	article
author	Heun-Sik Lee Tae-Joon Park Jeong-Min Kim Jun Ho Yun Ho-Yeong Yu Yeon-Jung Kim Bong-Jo Kim
author_facet	Heun-Sik Lee Tae-Joon Park Jeong-Min Kim Jun Ho Yun Ho-Yeong Yu Yeon-Jung Kim Bong-Jo Kim
author_sort	Heun-Sik Lee
title	Identification of metabolic markers predictive of prediabetes in a Korean population
title_short	Identification of metabolic markers predictive of prediabetes in a Korean population
title_full	Identification of metabolic markers predictive of prediabetes in a Korean population
title_fullStr	Identification of metabolic markers predictive of prediabetes in a Korean population
title_full_unstemmed	Identification of metabolic markers predictive of prediabetes in a Korean population
title_sort	identification of metabolic markers predictive of prediabetes in a korean population
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/1a108d976a64439c8f3896d421c13bf3
work_keys_str_mv	AT heunsiklee identificationofmetabolicmarkerspredictiveofprediabetesinakoreanpopulation AT taejoonpark identificationofmetabolicmarkerspredictiveofprediabetesinakoreanpopulation AT jeongminkim identificationofmetabolicmarkerspredictiveofprediabetesinakoreanpopulation AT junhoyun identificationofmetabolicmarkerspredictiveofprediabetesinakoreanpopulation AT hoyeongyu identificationofmetabolicmarkerspredictiveofprediabetesinakoreanpopulation AT yeonjungkim identificationofmetabolicmarkerspredictiveofprediabetesinakoreanpopulation AT bongjokim identificationofmetabolicmarkerspredictiveofprediabetesinakoreanpopulation
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Identification of metabolic markers predictive of prediabetes in a Korean population

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