The purpose of this study was to construct and validate a model for predicting nonalcoholic fatty liver disease (NAFLD) in the non-obese Chinese population. 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). The overall incidence of NAFLD was 13%. Nine significant predictors including age, gender, body mass index, fasting blood glucose, total cholesterol, triglycerides, high-density lipoprotein cholesterol, uric acid and alanine aminotransferase were identified and constructed for the nomogram using cox proportional hazards regression analyses. The concordance index was 0.804 and 0.802 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. 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.
Keywords: Nonalcoholic fatty liver disease; nomogram; predictive modeling; risk factor.
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