Prediction of non-alcoholic fatty liver in patients with type 2 diabetes mellitus
ZHENG Shuaiyin1,2, LI Lidan3, CHEN Peidi1, Xieerwaniguli·Abulimiti4, LI Di5,6,7
1. School of Public Health, Xinjiang Second Medical College, Karamay, Xinjiang 834000, China; 2. Academic Affairs Office of Xinjiang Second Medical College, Karamay, Xinjiang 834000, China; 3. Xinjiang Second Medical University, Karamay, Xinjiang 834000, China; 4. Kashgar University School of Medicine, Kashgar, Xinjiang 844000, China; 5. Karamay Hospital of People′s Hospital of Xinjiang Uygur Autonomous Region, Karamay, Xinjiang 834000, China; 6. Xinjiang Digestive System Tumor Precision Medical Clinical Medical Research Center, Karamay, Xinjiang 834000, China; 7. Xinjiang Key Laboratory of Clinical Genetic Testing and Biomedical Information, Karamay, Xinjiang 834000, China
Abstract：Objective To construct a prediction model of non-alcoholic fatty liver disease (NAFLD) in middle-aged and elderly patients with type 2 diabetes mellitus (T2DM), so as to provide basis for early screening and prevention of T2DM complicated with NAFLD. Methods Patients aged 45 years and above and diagnosed with T2DM in Karamay Hospital of People's Hospital of Xinjiang Uygur Autonomous Region in 2021 were collected as the study subjects. The data of general demographic characteristics and biochemical test results were collected. The patients were randomly divided into training group (n=3 241) and validation group (n=1 389) according to the ratio of 7∶3. LASSO regression and multivariable logistic regression model were used to select predictive factors. The nomograph model for prediction of NAFLD risk in T2DM patients was established. The predictive value of the model was evaluated using the receiver operating characteristic (ROC), adjusted curve and decision clinical analysis. Results Totally 4 630 T2DM cases were included, including 1 279 cases (27.62%) complicated with NAFLD. LASSO regression and multivariable logistic regression analysis identified gender, age, diastolic blood pressure, body mass index, alanine transaminase, triglycerides, low density lipoprotein cholesterol and platelet count as risk prediction factors for NAFLD in T2DM patients. The area under the ROC curve was 0.823 (95%CI: 0.814-0.832) for the training group and 0.809 (95%CI: 0.799-0.818) for the validation group, and Hosmer-Lemeshow test showed a good fitting effect (P>0.05). Decision curve analysis showed higher net clinical benefit of using the predictive model to predict NAFLD risk when the risk threshold probability was 0.27 to 0.85. Conclusion The nomogram model established has a good predictive value for the risk of NAFLD in T2DM patients aged 45 years and above.
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