Department of Health Management Center, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China.
Department of Health Management Center, People's Hospital of Karamay, Karamay, 834000, Xinjiang, China.
Sci Rep. 2022 Aug 16;12(1):13877. doi: 10.1038/s41598-022-17511-6.
In recent years, nonalcoholic fatty liver disease (NAFLD) has become the most important chronic liver disease worldwide. The prevalence of NAFLD in China has also increased year by year. This study aimed to detect NAFLD early by developing a nomogram model in Chinese individuals. A total of 8861 subjects who underwent physical examination in Karamay and were 18 to 62 years old were enrolled. Clinical information, laboratory results and ultrasound findings were retrieved. The participants were randomly assigned to the development set (n = 6203) and the validation set (n = 2658). Significant variables independently associated with NAFLD were identified by least absolute shrinkage and selection operator (LASSO) regression and the multiple logistic regression model. Six variables were selected to construct the nomogram: age, sex, waist circumference (WC), body mass index (BMI), alanine aminotransferase (ALT), triglycerides and glucose index (TyG). The area under the receiver operating characteristic curve (AUROC) of the development set and validation set was 0.886 and 0.894, respectively. The calibration curves showed excellent accuracy of the nomogram model. This physical examination and laboratory test-based nomogram can predict the risk of NAFLD intuitively and individually.
近年来，非酒精性脂肪性肝病（NAFLD）已成为全球最重要的慢性肝病。中国的 NAFLD 患病率也逐年增加。本研究旨在通过建立中国人的列线图模型来早期发现 NAFLD。共纳入 8861 名在克拉玛依体检且年龄在 18 至 62 岁的个体。收集了临床信息、实验室结果和超声检查结果。参与者被随机分配到开发集（n=6203）和验证集（n=2658）。通过最小绝对收缩和选择算子（LASSO）回归和多因素逻辑回归模型确定与 NAFLD 独立相关的显著变量。选择了 6 个变量来构建列线图：年龄、性别、腰围（WC）、体重指数（BMI）、丙氨酸氨基转移酶（ALT）、甘油三酯和葡萄糖指数（TyG）。开发集和验证集的受试者工作特征曲线（AUROC）下面积分别为 0.886 和 0.894。校准曲线显示该列线图模型具有出色的准确性。该基于体检和实验室检查的列线图可直观、个体化地预测 NAFLD 的风险。
