Department of Gastroenterology, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu, 610041, Sichuan, China.
Health Management Center, West China Hospital, General Practice Medical Center, Sichuan University, No. 37 Guoxue Alley, Chengdu, 610041, Sichuan, China.
Lipids Health Dis. 2023 Sep 6;22(1):145. doi: 10.1186/s12944-023-01902-3.
The absence of distinct symptoms in the majority of individuals with metabolic dysfunction-associated fatty liver disease (MAFLD) poses challenges in identifying those at high risk, so we need simple, efficient and cost-effective noninvasive scores to aid healthcare professionals in patient identification. While most noninvasive scores were developed for the diagnosis of nonalcoholic fatty liver disease (NAFLD), consequently, the objective of this study was to systematically assess the diagnostic ability of 12 noninvasive scores (METS-IR/TyG/TyG-WC/TyG-BMI/TyG-WtHR/VAI/HSI/FLI/ZJU/FSI/K-NAFLD) for MAFLD.
The study recruited eligible participants from two sources: the National Health and Nutrition Examination Survey (NHANES) 2017-2020.3 cycle and the database of the West China Hospital Health Management Center. The performance of the model was assessed using various metrics, including area under the receiver operating characteristic curve (AUC), net reclassification index (NRI), integrated discrimination improvement (IDI), decision curve analysis (DCA), and subgroup analysis.
A total of 7398 participants from the NHANES cohort and 4880 patients from the Western China cohort were included. TyG-WC had the best predictive power for MAFLD risk in the NHANES cohort (AUC 0.863, 95% CI 0.855-0.871), while TyG-BMI had the best predictive ability in the Western China cohort (AUC 0.903, 95% CI 0.895-0.911), outperforming other models, and in terms of IDI, NRI, DCA, and subgroup analysis combined, TyG-WC remained superior in the NAHANES cohort and TyG-BMI in the Western China cohort.
TyG-BMI demonstrated satisfactory diagnostic efficacy in identifying individuals at a heightened risk of MAFLD in Western China. Conversely, TyG-WC exhibited the best diagnostic performance for MAFLD risk recognition in the United States population. These findings suggest the necessity of selecting the most suitable predictive models based on regional and ethnic variations.
大多数代谢相关脂肪性肝病（MAFLD）患者缺乏明显的症状，这给高危人群的识别带来了挑战，因此我们需要简单、高效、经济实惠的非侵入性评分来帮助医疗保健专业人员进行患者识别。虽然大多数非侵入性评分是为诊断非酒精性脂肪性肝病（NAFLD）而开发的，但本研究的目的是系统评估 12 种非侵入性评分（METS-IR/TyG/TyG-WC/TyG-BMI/TyG-WtHR/VAI/HSI/FLI/ZJU/FSI/K-NAFLD）对 MAFLD 的诊断能力。
该研究从两个来源招募符合条件的参与者：2017-2020 年国家健康和营养检查调查（NHANES）第三周期和华西医院健康管理中心数据库。使用各种指标评估模型的性能，包括接收者操作特征曲线（ROC）下的面积（AUC）、净重新分类指数（NRI）、综合判别改善（IDI）、决策曲线分析（DCA）和亚组分析。
NHANES 队列纳入 7398 名参与者，华西队列纳入 4880 名患者。在 NHANES 队列中，TyG-WC 对 MAFLD 风险的预测能力最强（AUC 0.863，95%CI 0.855-0.871），而在华西队列中，TyG-BMI 的预测能力最强（AUC 0.903，95%CI 0.895-0.911），优于其他模型，且在 IDI、NRI、DCA 和联合亚组分析方面，TyG-WC 在 NHANES 队列中表现较好，TyG-BMI 在华西队列中表现较好。
TyG-BMI 在中国西部地区识别 MAFLD 高危人群的诊断效果令人满意。相反，TyG-WC 在美国人群中对 MAFLD 风险识别的诊断性能最佳。这些发现表明，根据地域和种族差异，选择最合适的预测模型是必要的。
