Establishment of screening models for nonalcoholic fatty liver disease in the adult Blang population
10.3969/j.issn.1001-5256.2021.12.025
- VernacularTitle:布朗族非酒精性脂肪性肝病筛查模型的构建
- Author:
Yebei LIANG
1
;
Chunguang YANG
1
;
Huadong ZENG
2
;
Ruwei TAO
2
;
Qiuming HU
3
;
Xiaoying TANG
4
;
Huaxiang SHI
5
;
Wei WU
2
;
Xuhong HOU
1
;
Weiping JIA
1
Author Information
1. Department of Endocrinology and Metabolism, The Sixth People's Hospital Affiliated to Shanghai Jiao Tong University, Shanghai Clinical Center for Diabetes, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Diabetes Institute, Shanghai 200233, China
2. Executive Office, Xishuangbanna Agricultural Reclamation Hospital, Xishuangbanna Dai Autonomous Prefecture, Yunnan 666100, China
3. Department of Clinical Laboratory, Xishuangbanna Agricultural Reclamation Hospital, Xishuangbanna Dai Autonomous Prefecture, Yunnan 666100, China
4. Blang Township Hospital of Menghai County, Xishuangbanna Dai Autonomous Prefecture, Yunnan 666213, China
5. Executive Office, Menghai County People's Hospital, Xishuangbanna Dai Autonomous Prefecture, Yunnan 666200, China
- Publication Type:Original articles_Other liver diseases
- Keywords:
Non-alcoholic Fatty Liver Disease;
Logistic Models;
Screening
- From:
Journal of Clinical Hepatology
2021;37(12):2861-2868
- CountryChina
- Language:Chinese
-
Abstract:
Objective To establish simple screening models for nonalcoholic fatty liver disease (NAFLD) in the adult Blang population. Methods Based on the survey data of metabolic diseases in the Blang people aged 18 years or above in 2017, 2993 respondents were stratified by sex and age (at an interval of 5 years) and then randomly divided into modeling group with 1497 respondents and validation group with 1496 respondents. Related information was collected, including demographic data, smoking, drinking, family history of diseases and personal medical history, body height, body weight, waist circumference, and blood pressure, and related markers were measured, including fasting plasma glucose, 2-hour postprandial plasma glucose or blood glucose at 2 hours after glucose loading, triglyceride, total cholesterol, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, alanine aminotransferase (ALT), aspartate aminotransferase (AST), and gamma-glutamyl transpeptidase. The chi-square test was used for comparison of categorical data between two groups. Logistic regression analysis was used to establish the screening model. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, positive predictive value, and negative predictive value were used to evaluate the screening performance of established models versus existing models in the study population, and the DeLong method was used for comparison of AUC. Results Three screening models for NAFLD were established based on physical and biochemical measurements, i.e., simple noninvasive model 1 (age, body mass index, and waist circumference), noninvasive model 2 with the addition of blood pressure, and model 3 with the combination of hematological parameters (diabetes and ALT/AST). In the modeling group, the three models had an AUC of 0.881 (95% confidence interval [ CI ]: 0.864-0.897), 0.892 (95% CI : 0.875-0.907), and 0.894 (95% CI : 0.877-0.909), respectively, and there was a significant difference between model 1 and models 2/3 ( P =0.004 0 and P < 0.001); in the validation group, the three models had an AUC of 0.891 (95% CI : 0.874-0.906), 0.892 (95% CI : 0.875-0.907), and 0.893 (95% CI : 0.876-0.908), respectively, and there was no significant difference between the three groups ( P > 0.05). Based on the overall consideration of screening performance, invasiveness, and cost, the simple noninvasive model 1 was considered the optimal screening model for NAFLD in this population. Model 1 had the highest Youden index at the cut-off value of 5 points, and when the score of ≥5 points was selected as the criteria for NAFLD, the model had a sensitivity of 86.5%, a specificity of 79.7%, a positive predictive value of 50.3%, and a negative predictive value of 96.1% in the modeling group and a sensitivity of 85.6%, a specificity of 80.6%, a positive predictive value of 51.7%, and a negative predictive value of 95.8% in the validation group. Conclusion The NAFLD screening models established for the adult Blang population based on age and obesity indicators have relatively higher sensitivity, specificity, and negative predictive value, and this tool is of important practical significance for the intervention of NAFLD and its closely related metabolic diseases in this population.
