Department of Gastroenterology and Hepatology, Institute of Digestive Disease, Tongji Hospital, Tongji University School of Medicine, Shanghai, China.
Ann Palliat Med. 2022 Jul;11(7):2349-2359. doi: 10.21037/apm-21-2988. Epub 2022 May 10.
Although the close relationship between nonalcoholic fatty liver disease (NAFLD) and insulin resistance has been clarified and there is a five-fold higher prevalence of NAFLD in patients with diabetes compared to that in patients without diabetes, this is not a reason to focus only on the incidence of NAFLD in people with diabetes because people who are insulin resistant are not necessarily diagnosed with diabetes, which leads to the overlook of NAFLD in non-diabetic population. Actually, we are obligated to pay more attention to the non-diabetic population for early detection and intervention of NAFLD. There is a lack of a convenient tool for predicting NAFLD in non-diabetic adults, and thus we aim to develop and validate a novel clinical nomogram to predict NAFLD among non-diabetic population to save more medical resources and make less missed diagnosis.
Researchers initially enrolled 20,944 patients and excluded those with history of drinking, known medication usage, viral hepatitis, known liver disease, missing covariant data, age <18 years, and impaired fasting blood glucose, leaving 14,251 adults participating in the baseline analysis, who were randomly divided in a ratio of 3:1 into a training dataset with 10,689 participants and a validation dataset with 3,562 participants, using the classification and regression training (caret) package in R software v. 4.0.3. Variables for prediction were selected by multivariable logistic regression analysis, the LASSO method, and clinical experience. Based on these, we constructed a prediction model. Performance of this model was validated by the area under the receiver operator characteristic curve, calibration curve, and decision curve analysis.
We used 6 variables to construct the prediction model: body mass index (BMI), aspartate aminotransferase (AST), triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), hemoglobin A1c (HbA1c), and diastolic blood pressure (DBP). In the training and validation datasets, the AUROC value of this prediction was 0.891 [95% confidence interval (CI): 0.884 to 0.899] and 0.902 (95% CI: 0.890 to 0.914), respectively. The calibration plots and the decision curve analysis (DCA) demonstrated that the accuracy of this model was good, with high clinical practicability.
The nomogram could screen non-diabetic adults for NAFLD and may aid clinical decision-making.
非酒精性脂肪性肝病（NAFLD）与胰岛素抵抗密切相关，糖尿病患者中 NAFLD 的患病率比非糖尿病患者高五倍，但这并不是只关注糖尿病患者中 NAFLD 发生率的理由，因为胰岛素抵抗的人不一定被诊断为糖尿病，这导致了非糖尿病人群中 NAFLD 的漏诊。实际上，我们有义务更加关注非糖尿病人群，以便早期发现和干预 NAFLD。目前缺乏一种方便的工具来预测非糖尿病成人的 NAFLD，因此，我们旨在开发和验证一种新的临床列线图，以预测非糖尿病人群中的 NAFLD，从而节省更多的医疗资源，减少漏诊。
研究人员最初纳入了 20944 名患者，并排除了有饮酒史、已知用药史、病毒性肝炎、已知肝病、协变量数据缺失、年龄<18 岁以及空腹血糖受损的患者，最终纳入了 14251 名成年人进行基线分析，他们被随机分为 3:1 的训练数据集和验证数据集，其中训练数据集包含 10689 名参与者，验证数据集包含 3562 名参与者，使用 R 软件 v. 4.0.3 中的分类和回归训练（caret）包进行多变量逻辑回归分析、LASSO 方法和临床经验进行变量选择。在此基础上，我们构建了一个预测模型。通过受试者工作特征曲线下面积、校准曲线和决策曲线分析来验证该模型的性能。
我们使用 6 个变量构建了预测模型：体重指数（BMI）、天门冬氨酸氨基转移酶（AST）、三酰甘油（TG）、高密度脂蛋白胆固醇（HDL-C）、糖化血红蛋白（HbA1c）和舒张压（DBP）。在训练集和验证集中，该预测模型的 AUROC 值分别为 0.891（95%置信区间：0.884 至 0.899）和 0.902（95%置信区间：0.890 至 0.914）。校准图和决策曲线分析（DCA）表明，该模型的准确性较好，具有较高的临床实用性。
该列线图可用于筛查非糖尿病成年人的 NAFLD，并可能有助于临床决策。
