Development and validation of a new diabetes index for the risk classification of present and new-onset diabetes: multicohort study

Abstract In this study, we aimed to propose a novel diabetes index for the risk classification based on machine learning techniques with a high accuracy for diabetes mellitus. Upon analyzing their demographic and biochemical data, we classified the 2013–16 Korea National Health and Nutrition Examina...

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Autores principales:	Shinje Moon, Ji-Yong Jang, Yumin Kim, Chang-Myung Oh
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/eaad442488a84264817bee576589602c
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spelling	oai:doaj.org-article:eaad442488a84264817bee576589602c2021-12-02T14:53:42ZDevelopment and validation of a new diabetes index for the risk classification of present and new-onset diabetes: multicohort study10.1038/s41598-021-95341-82045-2322https://doaj.org/article/eaad442488a84264817bee576589602c2021-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-95341-8https://doaj.org/toc/2045-2322Abstract In this study, we aimed to propose a novel diabetes index for the risk classification based on machine learning techniques with a high accuracy for diabetes mellitus. Upon analyzing their demographic and biochemical data, we classified the 2013–16 Korea National Health and Nutrition Examination Survey (KNHANES), the 2017–18 KNHANES, and the Korean Genome and Epidemiology Study (KoGES), as the derivation, internal validation, and external validation sets, respectively. We constructed a new diabetes index using logistic regression (LR) and calculated the probability of diabetes in the validation sets. We used the area under the receiver operating characteristic curve (AUROC) and Cox regression analysis to measure the performance of the internal and external validation sets, respectively. We constructed a gender-specific diabetes prediction model, having a resultant AUROC of 0.93 and 0.94 for men and women, respectively. Based on this probability, we classified participants into five groups and analyzed cumulative incidence from the KoGES dataset. Group 5 demonstrated significantly worse outcomes than those in other groups. Our novel model for predicting diabetes, based on two large-scale population-based cohort studies, showed high sensitivity and selectivity. Therefore, our diabetes index can be used to classify individuals at high risk of diabetes.Shinje MoonJi-Yong JangYumin KimChang-Myung OhNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Shinje Moon Ji-Yong Jang Yumin Kim Chang-Myung Oh Development and validation of a new diabetes index for the risk classification of present and new-onset diabetes: multicohort study
description	Abstract In this study, we aimed to propose a novel diabetes index for the risk classification based on machine learning techniques with a high accuracy for diabetes mellitus. Upon analyzing their demographic and biochemical data, we classified the 2013–16 Korea National Health and Nutrition Examination Survey (KNHANES), the 2017–18 KNHANES, and the Korean Genome and Epidemiology Study (KoGES), as the derivation, internal validation, and external validation sets, respectively. We constructed a new diabetes index using logistic regression (LR) and calculated the probability of diabetes in the validation sets. We used the area under the receiver operating characteristic curve (AUROC) and Cox regression analysis to measure the performance of the internal and external validation sets, respectively. We constructed a gender-specific diabetes prediction model, having a resultant AUROC of 0.93 and 0.94 for men and women, respectively. Based on this probability, we classified participants into five groups and analyzed cumulative incidence from the KoGES dataset. Group 5 demonstrated significantly worse outcomes than those in other groups. Our novel model for predicting diabetes, based on two large-scale population-based cohort studies, showed high sensitivity and selectivity. Therefore, our diabetes index can be used to classify individuals at high risk of diabetes.
format	article
author	Shinje Moon Ji-Yong Jang Yumin Kim Chang-Myung Oh
author_facet	Shinje Moon Ji-Yong Jang Yumin Kim Chang-Myung Oh
author_sort	Shinje Moon
title	Development and validation of a new diabetes index for the risk classification of present and new-onset diabetes: multicohort study
title_short	Development and validation of a new diabetes index for the risk classification of present and new-onset diabetes: multicohort study
title_full	Development and validation of a new diabetes index for the risk classification of present and new-onset diabetes: multicohort study
title_fullStr	Development and validation of a new diabetes index for the risk classification of present and new-onset diabetes: multicohort study
title_full_unstemmed	Development and validation of a new diabetes index for the risk classification of present and new-onset diabetes: multicohort study
title_sort	development and validation of a new diabetes index for the risk classification of present and new-onset diabetes: multicohort study
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/eaad442488a84264817bee576589602c
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Development and validation of a new diabetes index for the risk classification of present and new-onset diabetes: multicohort study

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