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

Endocrinology and Metabolism

Soo Yeon Kim¹  Jiwon Kim²  Eun Seok Kang²  Kwang Joon Kim²  Yong-ho Lee²  Minyoung Lee²  Ji Sun Nam³  Se Eun Park⁴  Joo Young Nam⁵  Sung Wan Chun⁶  Ji-Hye Kim⁷ 

[1] Department of Education and Training, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea;Department of Internal Medicine, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea;Division of Endocrinology and Metabolism, Department of Internal Medicine, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea;Division of Endocrinology and Metabolism, Department of Internal Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Korea;Division of Endocrinology and Metabolism, Department of Internal Medicine, National Health Insurance Service Ilsan Hospital, Goyang, Korea;Division of Endocrinology and Metabolism, Department of Internal Medicine, Soonchunhyang University Cheonan Hospital, Cheonan, Korea;Severance Health Check-up, Severance Hospital, Yonsei University Health System, Seoul, Korea;

关键词: non-alcoholic fatty liver disease;    diabetes mellitus;    type 2;    transient elastography;    screening;   

DOI  :  10.3803/EnM.2021.1074

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown