A nomogram for estimating the probability of nonalcoholic fatty liver disease in a Chinese population: A retrospective cohort study

Studies have showed that dyslipidemia is closely related to nonalcoholic fatty liver disease (NAFLD). However, less attention has been paid to the relationship between early dyslipidemia and long-term risk of NAFLD. Therefore, we aimed to develop a simple-to-use nomogram to predict early dyslipidemia and long-term risk of NAFLD onset. A retrospective cohort study including 3621 employees (including retirees) from 7 companies was conducted between 2012 and 2019. Anthropometric, potential laboratory parameters and abdominal ultrasound were performed at baseline and after a 5-year follow-up. Cox proportional hazards model was used to determine predictors for NAFLD onset. The effects of lipids, age, body mass index (BMI), and serum uric acid (UA) on NAFLD were evaluated with the use of Kaplan–Meier curves (log-rank test). A nomogram was developed based on the Cox proportional hazard model and a 2-piecewise linear regression model. The accuracy of model was evaluated according to the area under the receiver operating characteristic curves. A total of 1545 subjects were included in the final analysis. The mean follow-up time was 52 ± 6.6 months. Of the total subjects, 77.61% were male and 22.39% were female. The mean age at the time of initial visit was 45.21 ± 11.20 years. Five hundred fifty-five subjects (35.92% of all subjects) were finally diagnosed with NAFLD. Variables in the nomogram included age, BMI, triglycerides, high-density lipoprotein, low-density lipoprotein, and UA. The accuracy of the nomogram for predicting 5-year cumulative occurrence of NAFLD was 0.8135 (95% confidence interval: 0.7921–0.8349), and the sensitivity and specificity were 0.8108 and 0.6960, respectively. The combination of age, BMI, triglycerides, high-density lipoprotein, low-density lipoprotein, and UA translated into a nomogram can reliably estimate the incidence of NAFLD within 5 years. It may serve as a decision support tool to determine whether to intervene at an early stage.