Xintian Cai1,2, Mengru Wang1, Shasha Liu1, Yujuan Yuan1, Junli Hu1, Qing Zhu1, Jing Hong1, Guzailinuer Tuerxun1, Huimin Ma1, Nanfang Li1. 1. Hypertension Center of People's Hospital of Xinjiang Uygur Autonomous Region, Xinjiang Hypertension Institute, National Health Committee Key Laboratory of Hypertension Clinical Research, Key Laboratory of Xinjiang Uygur Autonomous Region, Xinjiang Clinical Medical Research Center for Hypertension Diseases Urumqi, Xinjiang, China. 2. Xinjiang Medical University Urumqi, Xinjiang, China.
Abstract
OBJECTIVE: This study aimed to establish and validate a nomogram for better assessment of the risk of type 2 diabetes (T2D) in obese patients with non-alcoholic fatty liver disease (NAFLD) based on independent predictors. METHODS: Of 1820 eligible participants from the NAGALA cohort enrolled in the study. Multivariate Cox regression was employed to construct the nomogram. The performance was assessed by area under the receiver operating characteristic curve (AUC), C-index, calibration curve, decision curve analysis, and Kaplan-Meier analysis. RESULTS: Five predictors were selected from 17 variables. The AUC values at different time points all indicated that the model constructed with these five predictors had good predictive power. Decision curves indicated that the model could be applied to clinical applications. CONCLUSIONS: We established and validated a reasonable, economical nomogram for predicting the risk of T2D in obese NAFLD patients. This simple clinical tool can help with risk stratification and thus contribute to the development of effective prevention programs against T2D in obese patients with NAFLD. AJTR
OBJECTIVE: This study aimed to establish and validate a nomogram for better assessment of the risk of type 2 diabetes (T2D) in obese patients with non-alcoholic fatty liver disease (NAFLD) based on independent predictors. METHODS: Of 1820 eligible participants from the NAGALA cohort enrolled in the study. Multivariate Cox regression was employed to construct the nomogram. The performance was assessed by area under the receiver operating characteristic curve (AUC), C-index, calibration curve, decision curve analysis, and Kaplan-Meier analysis. RESULTS: Five predictors were selected from 17 variables. The AUC values at different time points all indicated that the model constructed with these five predictors had good predictive power. Decision curves indicated that the model could be applied to clinical applications. CONCLUSIONS: We established and validated a reasonable, economical nomogram for predicting the risk of T2D in obese NAFLD patients. This simple clinical tool can help with risk stratification and thus contribute to the development of effective prevention programs against T2D in obese patients with NAFLD. AJTR
Authors: N H Cho; J E Shaw; S Karuranga; Y Huang; J D da Rocha Fernandes; A W Ohlrogge; B Malanda Journal: Diabetes Res Clin Pract Date: 2018-02-26 Impact factor: 5.602
Authors: Fumiaki Imamura; Amanda M Fretts; Matti Marklund; Andres V Ardisson Korat; Wei-Sin Yang; Maria Lankinen; Waqas Qureshi; Catherine Helmer; Tzu-An Chen; Jyrki K Virtanen; Kerry Wong; Julie K Bassett; Rachel Murphy; Nathan Tintle; Chaoyu Ian Yu; Ingeborg A Brouwer; Kuo-Liong Chien; Yun-Yu Chen; Alexis C Wood; Liana C Del Gobbo; Luc Djousse; Johanna M Geleijnse; Graham G Giles; Janette de Goede; Vilmundur Gudnason; William S Harris; Allison Hodge; Frank Hu; Albert Koulman; Markku Laakso; Lars Lind; Hung-Ju Lin; Barbara McKnight; Kalina Rajaobelina; Ulf Riserus; Jennifer G Robinson; Cecilia Samieri; Mackenzie Senn; David S Siscovick; Sabita S Soedamah-Muthu; Nona Sotoodehnia; Qi Sun; Michael Y Tsai; Tomi-Pekka Tuomainen; Matti Uusitupa; Lynne E Wagenknecht; Nick J Wareham; Jason H Y Wu; Renata Micha; Rozenn N Lemaitre; Dariush Mozaffarian; Nita G Forouhi Journal: PLoS Med Date: 2020-06-12 Impact factor: 11.069