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Original Article
- Diabetes, Obesity and Metabolism
- Non-Laboratory-Based Simple Screening Model for Nonalcoholic Fatty Liver Disease in Patients with Type 2 Diabetes Developed Using Multi-Center Cohorts
- Jiwon Kim, Minyoung Lee, Soo Yeon Kim, Ji-Hye Kim, Ji Sun Nam, Sung Wan Chun, Se Eun Park, Kwang Joon Kim, Yong-ho Lee, Joo Young Nam, Eun Seok Kang
- Endocrinol Metab. 2021;36(4):823-834. Published online August 27, 2021
- DOI: https://doi.org/10.3803/EnM.2021.1074
- 10,073 View
- 169 Download
- 4 Web of Science
- 5 Crossref
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Abstract
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Supplementary Material
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ePub - 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. -
Citations
Citations to this article as recorded by
- An interpretable machine learning model for predicting metabolic dysfunction‐associated steatotic liver disease in patients with type 2 diabetes
Zhuolin Zhou, Nan Gao, Jiaojiao Liu, Xuerong Ma, Zhijuan Ge, Cheng Ji
Diabetes, Obesity and Metabolism.2026; 28(1): 122. CrossRef - Surveying the features of screening strategies for non-alcoholic fatty liver disease: A scoping review
Nazanin Mir, Hamid R Baradaran, Seidamir Pasha Tabaeian, Nasrin Abolhasanbeigi Gallehzan, Aziz Rezapour
Journal of Education and Health Promotion.2026;[Epub] CrossRef - Prevalence, Sonographic Characteristics, and Metabolic Predictors of Nonalcoholic Fatty Liver Disease in Adults With Type 2 Diabetes in Tanzania
Zubeir Zubeir, Zuhura Nkrumbih, Salama Ally, Yasser H. Hadi
Dr. Sulaiman Al Habib Medical Journal.2025; 7(3): 180. CrossRef - Insulin Resistance, Non-Alcoholic Fatty Liver Disease and Type 2 Diabetes Mellitus: Clinical and Experimental Perspective
Inha Jung, Dae-Jeong Koo, Won-Young Lee
Diabetes & Metabolism Journal.2024; 48(3): 327. CrossRef - Non-Alcoholic Fatty Liver Disease or Type 2 Diabetes Mellitus—The Chicken or the Egg Dilemma
Marcin Kosmalski, Agnieszka Śliwińska, Józef Drzewoski
Biomedicines.2023; 11(4): 1097. CrossRef
- An interpretable machine learning model for predicting metabolic dysfunction‐associated steatotic liver disease in patients with type 2 diabetes
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