AUTHOR=Pan Xinting , Xie Xiaoxu , Peng Hewei , Cai Xiaoling , Li Huiquan , Hong Qizhu , Wu Yunli , Lin Xu , Xu Shanghua , Peng Xian-e TITLE=Risk Prediction for Non-alcoholic Fatty Liver Disease Based on Biochemical and Dietary Variables in a Chinese Han Population JOURNAL=Frontiers in Public Health VOLUME=8 YEAR=2020 URL=https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2020.00220 DOI=10.3389/fpubh.2020.00220 ISSN=2296-2565 ABSTRACT=

Nonalcoholic fatty liver disease (NAFLD) is a common liver disease globally, but there are no optimal methods for its prediction or diagnosis. The present cross-sectional study proposes a non-invasive tool for NAFLD screening. The study included 2,446 individuals, of whom 574 were NAFLD patients. Multivariable logistic regression analysis was used to identify risk factors for NAFLD and incorporate them in a risk prediction nomogram model; the variables included both clinical and lifestyle-related variables. Following stepwise regression, BMI, waist circumference, serum triglyceride, high-density lipoprotein cholesterol, alanine aminotransferase, presence of diabetes and hyperuricemia, tuber and fried food consumption were identified as significant risk factors and used in the model. The final nomogram was found to have good discrimination ability (area under the receiver operating characteristic curve = 0.843 [95% CI: 0.819-0.867]), and reasonable accuracy for the prediction of NAFLD risk. A cut-off score of <180 for the nomogram was found to have high sensitivity and predictivity for the exclusion of individuals from screening. The model can be used as a non-invasive tool for mass screening.