预测非酒精性脂肪性肝病肥胖患者2型糖尿病风险的列线图的建立与验证：一项纵向观察性研究
Establishment and validation of a nomogram that predicts the risk of type 2 diabetes in obese patients with non-alcoholic fatty liver disease: a longitudinal observational study.
作者信息
Cai Xintian, Wang Mengru, Liu Shasha, Yuan Yujuan, Hu Junli, Zhu Qing, Hong Jing, Tuerxun Guzailinuer, Ma Huimin, Li Nanfang
机构信息
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.
Xinjiang Medical University Urumqi, Xinjiang, China.
出版信息
Am J Transl Res. 2022 Jul 15;14(7):4505-4514. eCollection 2022.
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.
目的
本研究旨在基于独立预测因素建立并验证一种列线图，以更好地评估非酒精性脂肪性肝病（NAFLD）肥胖患者患2型糖尿病（T2D）的风险。
方法
本研究纳入了NAGALA队列中的1820名符合条件的参与者。采用多变量Cox回归构建列线图。通过受试者操作特征曲线下面积（AUC）、C指数、校准曲线、决策曲线分析和Kaplan-Meier分析来评估其性能。
结果
从17个变量中选择了5个预测因素。不同时间点的AUC值均表明，由这5个预测因素构建的模型具有良好的预测能力。决策曲线表明该模型可应用于临床。
结论
我们建立并验证了一种合理、经济的列线图，用于预测肥胖NAFLD患者患T2D的风险。这种简单的临床工具有助于进行风险分层，从而有助于制定针对肥胖NAFLD患者的有效的T2D预防方案。
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