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A Non-Invasive Prediction Model for Non-Alcoholic Fatty Liver Disease in Adults with Type 2 Diabetes Based on the Population of Northern Urumqi, China
Authors Xue M, Yang X, Zou Y, Liu T, Su Y, Li C, Yao H, Wang S
Received 12 July 2020
Accepted for publication 7 January 2021
Published 2 February 2021 Volume 2021:14 Pages 443—454
DOI https://doi.org/10.2147/DMSO.S271882
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 3
Editor who approved publication: Professor Ming-Hui Zou
Mingyue Xue,1,2 Xiaoping Yang,3 Yuan Zou,3 Tao Liu,3 Yinxia Su,3 Cheng Li,4 Hua Yao,3 Shuxia Wang3
1Hospital of Traditional Chinese Medicine Affiliated to the Fourth Clinical Medical College of Xinjiang Medical University, Urumqi 830011, People’s Republic of China; 2College of Public Health, Xinjiang Medical University, Urumqi, Xinjiang 830011, People’s Republic of China; 3Health Management Institute, Xinjiang Medical University, Urumqi 830011, People’s Republic of China; 4The First Affiliated Hospital of Xinjiang Medical University, Urumqi 830011, People’s Republic of China
Correspondence: Hua Yao; Shuxia Wang Email yaohua01@sina.com; 2724443591@qq.com
Background: High prevalence of non-alcoholic fatty liver disease (NAFLD) occurs in type 2 diabetes mellitus (T2DM), and about 13% of diabetic patients eventually die of liver cirrhosis or liver cancer. The purpose of our research was to develop a non-invasive predictive model of NAFLD in adults with T2DM.
Patients and Methods: Adult patients diagnosed with T2DM during physical examination in 2018 in Urumqi were recruited, in total 40,921 cases. We chose questionnaire and physical measurement variables to build a simple, low-cost model. Variables were selected by the least absolute shrinkage and selection operator regression (LASSO). The features chosen by LASSO were used to build the nomogram prediction model of NAFLD. The receiver operating curve (ROC) and calibration were used for model validation.
Results: Determinants in the nomogram included age, ethnicity, sex, exercise, smoking, dietary ratio, heart rate, systolic blood pressure (SBP), BMI, waist circumference, and atherosclerotic vascular disease (ASCVD). The area under ROC of developing group and validation group was 0.756 (95% confidence interval 0.750– 0.761) and 0.755 (95% confidence interval 0.746– 0.763), respectively, and the P values of the two calibration curves were 0.694 and 0.950, suggesting that the nomogram had good disease recognition ability and calibration.
Conclusion: A nomogram constructed with accuracy can calculate the possibility of NAFLD in adults with T2DM. If validated externally, this tool could be utilized as a non-invasive method to diagnose non-alcoholic fatty liver in adults with T2DM.
Keywords: type 2 diabetes mellitus, non-alcoholic fatty liver disease, screening tool, nomogram
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