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
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 Healt...
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Dove Medical Press
2021
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oai:doaj.org-article:45826ecffa2942a696168cf4ad8db7242021-12-02T13:30:40ZA Non-Invasive Prediction Model for Non-Alcoholic Fatty Liver Disease in Adults with Type 2 Diabetes Based on the Population of Northern Urumqi, China1178-7007https://doaj.org/article/45826ecffa2942a696168cf4ad8db7242021-02-01T00:00:00Zhttps://www.dovepress.com/a-non-invasive-prediction-model-for-non-alcoholic-fatty-liver-disease--peer-reviewed-article-DMSOhttps://doaj.org/toc/1178-7007Mingyue 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 ChinaCorrespondence: Hua Yao; Shuxia Wang Email yaohua01@sina.com; 2724443591@qq.comBackground: 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, nomogramXue MYang XZou YLiu TSu YLi CYao HWang SDove Medical Pressarticletype 2 diabetes mellitusnon-alcoholic fatty liver diseasescreening toolnomogram;Specialties of internal medicineRC581-951ENDiabetes, Metabolic Syndrome and Obesity: Targets and Therapy, Vol Volume 14, Pp 443-454 (2021) |
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type 2 diabetes mellitus non-alcoholic fatty liver disease screening tool nomogram; Specialties of internal medicine RC581-951 |
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type 2 diabetes mellitus non-alcoholic fatty liver disease screening tool nomogram; Specialties of internal medicine RC581-951 Xue M Yang X Zou Y Liu T Su Y Li C Yao H Wang S 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 |
| description |
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 ChinaCorrespondence: Hua Yao; Shuxia Wang Email yaohua01@sina.com; 2724443591@qq.comBackground: 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 |
| format |
article |
| author |
Xue M Yang X Zou Y Liu T Su Y Li C Yao H Wang S |
| author_facet |
Xue M Yang X Zou Y Liu T Su Y Li C Yao H Wang S |
| author_sort |
Xue M |
| title |
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 |
| title_short |
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 |
| title_full |
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 |
| title_fullStr |
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 |
| title_full_unstemmed |
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 |
| title_sort |
non-invasive prediction model for non-alcoholic fatty liver disease in adults with type 2 diabetes based on the population of northern urumqi, china |
| publisher |
Dove Medical Press |
| publishDate |
2021 |
| url |
https://doaj.org/article/45826ecffa2942a696168cf4ad8db724 |
| work_keys_str_mv |
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