Derivation and Validation of a Prediction Model for Predicting the 5-Year Incidence of Type 2 Diabetes in Non-Obese Adults: A Population-Based Cohort Study

Xin-Tian Cai,1 Li-Wei Ji,2 Sha-Sha Liu,1 Meng-Ru Wang,1 Mulalibieke Heizhati,1 Nan-Fang Li1 1Hypertension Center of People’s Hospital of Xinjiang Uygur Autonomous Region, Xinjiang Hypertension Institute, National Health Committee Key Laboratory of Hypertension Clinical Research, Urumqi, Pe...

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Autores principales:	Cai XT, Ji LW, Liu SS, Wang MR, Heizhati M, Li NF
Formato:	article
Lenguaje:	EN
Publicado:	Dove Medical Press 2021
Materias:	type 2 diabetes prediction model nomogram risk factor Specialties of internal medicine RC581-951
Acceso en línea:	https://doaj.org/article/5092b2a8f0cc4c49bff826074adb1fd4
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spelling	oai:doaj.org-article:5092b2a8f0cc4c49bff826074adb1fd42021-12-02T15:36:38ZDerivation and Validation of a Prediction Model for Predicting the 5-Year Incidence of Type 2 Diabetes in Non-Obese Adults: A Population-Based Cohort Study1178-7007https://doaj.org/article/5092b2a8f0cc4c49bff826074adb1fd42021-05-01T00:00:00Zhttps://www.dovepress.com/derivation-and-validation-of-a-prediction-model-for-predicting-the-5-y-peer-reviewed-fulltext-article-DMSOhttps://doaj.org/toc/1178-7007Xin-Tian Cai,1 Li-Wei Ji,2 Sha-Sha Liu,1 Meng-Ru Wang,1 Mulalibieke Heizhati,1 Nan-Fang Li1 1Hypertension Center of People’s Hospital of Xinjiang Uygur Autonomous Region, Xinjiang Hypertension Institute, National Health Committee Key Laboratory of Hypertension Clinical Research, Urumqi, People’s Republic of China; 2Laboratory of Mitochondrial and Metabolism, National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, People’s Republic of ChinaCorrespondence: Nan-Fang LiHypertension Center of People’s Hospital of Xinjiang Uygur Autonomous Region, Xinjiang Hypertension Institute, National Health Committee Key Laboratory of Hypertension Clinical Research, Urumqi, Xinjiang, People’s Republic of ChinaTel +86 991 8564818Email lnanfang2016@sina.comPurpose: The aim of this study was to derivate and validate a nomogram based on independent predictors to better evaluate the 5-year risk of T2D in non-obese adults.Patients and Methods: This is a historical cohort study from a collection of databases that included 12,940 non-obese participants without diabetes at baseline. All participants were randomised to a derivation cohort (n = 9651) and a validation cohort (n = 3289). In the derivation cohort, the least absolute shrinkage and selection operator (LASSO) regression model was used to determine the optimal risk factors for T2D. Multivariate Cox regression analysis was used to establish the nomogram of T2D prediction. The receiver operating characteristic (ROC) curve, C-index, calibration curve, and decision curve analysis were performed by 1000 bootstrap resamplings to evaluate the discrimination ability, calibration, and clinical practicability of the nomogram.Results: After LASSO regression analysis of the derivation cohort, it was found that age, fatty liver, γ-glutamyltranspeptidase, triglycerides, glycosylated hemoglobin A1c and fasting plasma glucose were risk predictors, which were integrated into the nomogram. The C-index of derivation cohort and validation cohort were 0.906 [95% confidence interval (CI), 0.878– 0.934] and 0.837 (95% CI, 0.760– 0.914), respectively. The AUC of 5-year T2D risk in the derivation cohort and validation cohort was 0.916 (95% CI, 0.889– 0.943) and 0.829 (95% CI, 0.753– 0.905), respectively. The calibration curve indicated that the predicted probability of nomogram is in good agreement with the actual probability. The decision curve analysis demonstrated that the predicted nomogram was clinically useful.Conclusion: Our nomogram can be used as a reasonable, affordable, simple, and widely implemented tool to predict the 5-year risk of T2D in non-obese adults. With this model, early identification of high-risk individuals is helpful to timely intervene and reduce the risk of T2D in non-obese adults.Keywords: type 2 diabetes, prediction model, nomogram, risk factorCai XTJi LWLiu SSWang MRHeizhati MLi NFDove Medical Pressarticletype 2 diabetesprediction modelnomogramrisk factorSpecialties of internal medicineRC581-951ENDiabetes, Metabolic Syndrome and Obesity: Targets and Therapy, Vol Volume 14, Pp 2087-2101 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	type 2 diabetes prediction model nomogram risk factor Specialties of internal medicine RC581-951
spellingShingle	type 2 diabetes prediction model nomogram risk factor Specialties of internal medicine RC581-951 Cai XT Ji LW Liu SS Wang MR Heizhati M Li NF Derivation and Validation of a Prediction Model for Predicting the 5-Year Incidence of Type 2 Diabetes in Non-Obese Adults: A Population-Based Cohort Study
description	Xin-Tian Cai,1 Li-Wei Ji,2 Sha-Sha Liu,1 Meng-Ru Wang,1 Mulalibieke Heizhati,1 Nan-Fang Li1 1Hypertension Center of People’s Hospital of Xinjiang Uygur Autonomous Region, Xinjiang Hypertension Institute, National Health Committee Key Laboratory of Hypertension Clinical Research, Urumqi, People’s Republic of China; 2Laboratory of Mitochondrial and Metabolism, National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, People’s Republic of ChinaCorrespondence: Nan-Fang LiHypertension Center of People’s Hospital of Xinjiang Uygur Autonomous Region, Xinjiang Hypertension Institute, National Health Committee Key Laboratory of Hypertension Clinical Research, Urumqi, Xinjiang, People’s Republic of ChinaTel +86 991 8564818Email lnanfang2016@sina.comPurpose: The aim of this study was to derivate and validate a nomogram based on independent predictors to better evaluate the 5-year risk of T2D in non-obese adults.Patients and Methods: This is a historical cohort study from a collection of databases that included 12,940 non-obese participants without diabetes at baseline. All participants were randomised to a derivation cohort (n = 9651) and a validation cohort (n = 3289). In the derivation cohort, the least absolute shrinkage and selection operator (LASSO) regression model was used to determine the optimal risk factors for T2D. Multivariate Cox regression analysis was used to establish the nomogram of T2D prediction. The receiver operating characteristic (ROC) curve, C-index, calibration curve, and decision curve analysis were performed by 1000 bootstrap resamplings to evaluate the discrimination ability, calibration, and clinical practicability of the nomogram.Results: After LASSO regression analysis of the derivation cohort, it was found that age, fatty liver, γ-glutamyltranspeptidase, triglycerides, glycosylated hemoglobin A1c and fasting plasma glucose were risk predictors, which were integrated into the nomogram. The C-index of derivation cohort and validation cohort were 0.906 [95% confidence interval (CI), 0.878– 0.934] and 0.837 (95% CI, 0.760– 0.914), respectively. The AUC of 5-year T2D risk in the derivation cohort and validation cohort was 0.916 (95% CI, 0.889– 0.943) and 0.829 (95% CI, 0.753– 0.905), respectively. The calibration curve indicated that the predicted probability of nomogram is in good agreement with the actual probability. The decision curve analysis demonstrated that the predicted nomogram was clinically useful.Conclusion: Our nomogram can be used as a reasonable, affordable, simple, and widely implemented tool to predict the 5-year risk of T2D in non-obese adults. With this model, early identification of high-risk individuals is helpful to timely intervene and reduce the risk of T2D in non-obese adults.Keywords: type 2 diabetes, prediction model, nomogram, risk factor
format	article
author	Cai XT Ji LW Liu SS Wang MR Heizhati M Li NF
author_facet	Cai XT Ji LW Liu SS Wang MR Heizhati M Li NF
author_sort	Cai XT
title	Derivation and Validation of a Prediction Model for Predicting the 5-Year Incidence of Type 2 Diabetes in Non-Obese Adults: A Population-Based Cohort Study
title_short	Derivation and Validation of a Prediction Model for Predicting the 5-Year Incidence of Type 2 Diabetes in Non-Obese Adults: A Population-Based Cohort Study
title_full	Derivation and Validation of a Prediction Model for Predicting the 5-Year Incidence of Type 2 Diabetes in Non-Obese Adults: A Population-Based Cohort Study
title_fullStr	Derivation and Validation of a Prediction Model for Predicting the 5-Year Incidence of Type 2 Diabetes in Non-Obese Adults: A Population-Based Cohort Study
title_full_unstemmed	Derivation and Validation of a Prediction Model for Predicting the 5-Year Incidence of Type 2 Diabetes in Non-Obese Adults: A Population-Based Cohort Study
title_sort	derivation and validation of a prediction model for predicting the 5-year incidence of type 2 diabetes in non-obese adults: a population-based cohort study
publisher	Dove Medical Press
publishDate	2021
url	https://doaj.org/article/5092b2a8f0cc4c49bff826074adb1fd4
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Derivation and Validation of a Prediction Model for Predicting the 5-Year Incidence of Type 2 Diabetes in Non-Obese Adults: A Population-Based Cohort Study

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