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

Abstract

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.

Application of a Novel Prediction Model for Predicting 2-Year Risk of Non-Alcoholic Fatty Liver Disease in the Non-Obese Population with Normal Blood Lipid Levels: A Large Prospective Cohort Study from China

“…These variables identified as risk factors for NAFLD are consistent with previous studies and are widely used in NAFLD risk scoring models. 23 , 26 , 27 In our prediction model, BMI is one of the main aspects of the NAFLD risk factor score. Numerous previous studies have confirmed obesity as a well-known risk factor for NAFLD.…”

Development of a prediction model for predicting the prevalence of nonalcoholic fatty liver disease in Chinese nurses: the first-year follow data of a web-based ambispective cohort study

“…The model displayed excellent discrimination and clinical value. Predictors in the model included BMI, FBG, TG, HDL, AST, ALT, Tbil, UA and monthly night shift, which contained some risk factors consistent with previous studies NAFLD [ 9 , 10 ]. and one factor reflecting the professional characteristics of nurses.…”

“…This may be the result of different department distributions, while only 7.65% of female nurses included in this study were working in emergency and critical care units. Furthermore, only females were recruited in this study, but males had been found to have a higher risk of NAFLD (OR 1.779, 95% CI 1.676–1.888) [ 9 ]. The participants included in this study were much younger than those in previous studies, while older age was an independent predictor of NAFLD occurance [ 9 , 16 ].…”

“…Previous studies have explored risk factors for NAFLD and found that age, sex, race, metabolic syndrome (MS), unhealthy lifestyle, such as unbalanced diet, sedentary and low-level physical activity and lack of sleep, were tightly associated with the prevalence of NAFLD [ 8 ]. Furthermore, several prediction models of NAFLD occurance have been developed, which found that age, ethnicity, sex, exercise, smoking, heart rate, blood pressure, body mass index (BMI), waist circumference, high-density lipoprotein-cholesterol (HDL-C) and bilirubin could independently predict the prevalence of NAFLD [ 9 , 10 ]. However, most of the current models only focus on laboratory tests while neglecting lifestyle could also influence the prevalence of NAFLD.…”

Development and application of a novel model to predict the risk of non-alcoholic fatty liver disease among lean pre-diabetics with normal blood lipid levels

“…To meet this unmet need, we thus developed a novel predictive nomogram to determine NAFLD risk among pre-diabetic lean Chinese individuals with normal blood lipid levels. There have already been several predictive models developed to determine the risk of individuals ending up with T2DM and NAFLD, such as from Zhang et al and Xue et al [ 20 , 32 ], both of which focused on assessing NAFLD risk among Chinese T2DM. Another model from Cai et al [ 33 ] is able to estimate 8-year incidence of T2DM among NAFLD populations.…”