Predictive Risk Factors of Nonalcoholic Fatty Liver Disease in a Lean Chinese Population

Background: Although nonalcoholic fatty liver disease (NAFLD) is related to obesity, it may also affect lean individuals. Recent data suggest that lean NAFLD patients can develop the whole spectrum of NASH. However, the NAFLD predictive model for lean populations remains lacking. Methods: A total of 5037 lean individuals were included in this study, and the data were separated for training and validation. The logistic regression method was used, and a nomogram, a type of prediction model, was constructed according to the logistic regression analysis and the significant clinical factors. The performance of this model was evaluated based on its discrimination, calibration, and clinical utility. Results: The individuals were divided into the training (n = 4068) or validation (n = 969) cohorts at a ratio of 8 to 2. The overall prevalence of NAFLD in the lean cohort was 6.43%. The nomogram was constructed based on seven predictors: alanine aminotransferase, total cholesterol, triglycerides, low-density lipoprotein cholesterol, creatinine, uric acid, and hemoglobin A1C. The model based on these factors showed good predictive accuracy in the training set and in the internal validation set, with areas under the curve (AUCs) of 0.870 and 0.887, respectively. The calibration curves and decision curve analysis (DCA) displayed good clinical utility. Conclusion: the nomogram model provides a simple and reliable ability to predict the risk of NAFLD in lean subjects. The model can predict lean NAFLD and can help physicians screen and identify lean subjects at a high risk of NAFLD.