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 clinicalpractice.