Endocrinol Metab.  2021 Aug;36(4):823-834. 10.3803/EnM.2021.1074.

Non-Laboratory-Based Simple Screening Model for Nonalcoholic Fatty Liver Disease in Patients with Type 2 Diabetes Developed Using Multi-Center Cohorts

• Kim J
• Lee M
• Kim SY
• Kim JH
• Nam JS
• Chun SW
• Park SE
• Kim KJ
• Lee Yh
• Nam JY
• Kang ES

• KMID: 2519670
• DOI: http://doi.org/10.3803/EnM.2021.1074

Abstract

Background
Nonalcoholic fatty liver disease (NAFLD) is the most prevalent cause of chronic liver disease worldwide. Type 2 diabetes mellitus (T2DM) is a risk factor that accelerates NAFLD progression, leading to fibrosis and cirrhosis. Thus, here we aimed to develop a simple model to predict the presence of NAFLD based on clinical parameters of patients with T2DM.
Methods
A total of 698 patients with T2DM who visited five medical centers were included. NAFLD was evaluated using transient elastography. Univariate logistic regression analyses were performed to identify potential contributors to NAFLD, followed by multivariable logistic regression analyses to create the final prediction model for NAFLD.
Results
Two NAFLD prediction models were developed, with and without serum biomarker use. The non-laboratory model comprised six variables: age, sex, waist circumference, body mass index (BMI), dyslipidemia, and smoking status. For a cutoff value of ≥60, the prediction accuracy was 0.780 (95% confidence interval [CI], 0.743 to 0.817). The second comprehensive model showed an improved discrimination ability of up to 0.815 (95% CI, 0.782 to 0.847) and comprised seven variables: age, sex, waist circumference, BMI, glycated hemoglobin, triglyceride, and alanine aminotransferase to aspartate aminotransferase ratio. Our non-laboratory model showed non-inferiority in the prediction of NAFLD versus previously established models, including serum parameters.
Conclusion
The new models are simple and user-friendly screening methods that can identify individuals with T2DM who are at high-risk for NAFLD. Additional studies are warranted to validate these new models as useful predictive tools for NAFLD in clinical practice.

Keyword

Non-alcoholic fatty liver disease; Diabetes mellitus; type 2; Transient elastography; Screening

Figure

• 

  Fig. 1 (A) Receiver-operating characteristic (ROC) curve of non-laboratory screening model for the prediction of nonalcoholic fatty liver disease. The area under ROC curve (AUC) is 0.780 (95% confidence interval [CI], 0.743 to 0.817). At cutoff value of 60, sensitivity is 74%, specificity is 69%, respectively. (B) ROC curve of comprehensive screening model for the prediction of nonalcoholic fatty liver disease. The AUC is 0.815 (95% CI, 0.782 to 0.847). At cutoff value of 62, sensitivity is 75%, specificity is 73%, respectively.

• 

  Fig. 2 Prevalence of nonalcoholic fatty liver according to score categories of screening models in development dataset. (A) Non-laboratory screening model and (B) comprehensive screening model.

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