Background
Nonalcoholic fatty liver disease (NAFLD) is the most common chronic liver disease, and can be found in non-obese individuals. The purpose of this study was to construct and validate a model for predicting NAFLD in the non-obese Chinese population.
Methods
A total of 13240 NAFLD-free individuals at baseline from a 4-y longitudinal study were allocated to a training cohort (n = 8872) and a validation cohort (n = 4368). Cox proportional hazards regression analyses were used to select significant variables, and a nomogram was developed to predict NAFLD incidence in the training cohort. Concordance index (C-index), receiver operating characteristic (ROC) curves and calibration curves were applied to assess the predictive modeling of the nomogram in training and validation cohorts. Individuals were categorized into high- or low-risk groups based on median risk scores from the nomogram. Then, a decision curve analysis (DCA) was used to estimate the clinical usefulness of the nomogram in the combined training and validation cohorts.
Results
The overall incidence of NAFLD was 13%. Nine significant predictors including age, gender, body mass index (BMI), fasting blood glucose (FBG), total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-c), uric acid (UA) and alanine aminotransferase (ALT) were identified and constructed for the nomogram. The C-index was 0.804 [95% CI, 0.792–0.852] and 0.802 [95% CI, 0.784–0.820] in the training and validation cohorts, respectively. In the training cohort, the area under the ROC curve (AUC) for 1-y, 2-y, 3-y and 4-y risk was 0.835, 0.825, 0.816 and 0.782, respectively. Likewise, in the validation cohort, the AUC for 1-y, 2-y, 3-y and 4-y risk was 0.817, 0.820, 0.814 and 0.813. The calibration curves for NAFLD risk showed excellent accuracy in the predictive modeling of the nomogram, internally and externally. The nomogram categorized individuals into high- and low-risk groups, and the DCA displayed the clinical usefulness of the nomogram for predicting NAFLD incidence.
Conclusions
Our nomogram can predict a personalized risk of NAFLD in the non-obese Chinese population. This nomogram can serve as a simple and affordable tool for stratifying individuals at a high risk of NAFLD, and thus serve to expedite treatment of NAFLD.