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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 
 Endocrinology and Metabolism(대한내분비학회지), Vol.36(4) : 823-834, 2021-08 
Journal Title
Endocrinology and Metabolism(대한내분비학회지)
Issue Date
Diabetes mellitus, type 2 ; Non-alcoholic fatty liver disease ; Screening ; Transient elastography
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.
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1. College of Medicine (의과대학) > Dept. of Internal Medicine (내과학교실) > 1. Journal Papers
Yonsei Authors
Kang, Eun Seok(강은석) ORCID logo https://orcid.org/0000-0002-0364-4675
Kim, Kwang Joon(김광준) ORCID logo https://orcid.org/0000-0002-5554-8255
Kim, Jiwon(김지원)
Kim, Ji-Hye(김지혜) ORCID logo https://orcid.org/0000-0002-5719-8180
Nam, Ji Sun(남지선) ORCID logo https://orcid.org/0000-0001-8655-5258
Lee, Minyoung(이민영) ORCID logo https://orcid.org/0000-0002-9333-7512
Lee, Yong Ho(이용호) ORCID logo https://orcid.org/0000-0002-6219-4942
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