Development and Validation of Machine Learning-Based Marker for Early Detection and Prognosis Stratification of Nonalcoholic Fatty Liver Disease.

Abstract

Nonalcoholic fatty liver disease (NAFLD) is the leading cause of chronic liver disease and is considered the hepatic manifestation of metabolic syndrome, triggering out adverse outcomes. A stacked multimodal machine learning model is constructed and validated for early identification and prognosis stratification of NAFLD by integrating genetic and clinical data sourced from 36 490 UK Biobank and 9 007 Nanfang Hospital participants and extracted its probabilities as in-silico scores for NAFLD (ISNLD). The efficacy of ISNLD is evaluated for the early prediction of severe liver disease (SeLD) and analyzed its association with metabolism-related outcomes. The multimodal model performs satisfactorily in classifying individuals into low- and high-risk groups for NAFLD, achieving area under curves (AUCs) of 0.843, 0.840, and 0.872 within training, internal, and external test sets, respectively. Among high-risk group, ISNLD is significantly associated with intrahepatic and metabolism-related complications after lifestyle factors adjustment. Further, ISNLD demonstrates notable capability for early prediction of SeLD and further stratifies high-risk subjects into three risk subgroups of elevated risk for adverse outcomes. The findings emphasize the model's ability to integrate multimodal features to generate ISNLD, enabling early detection and prognostic prediction of NAFLD. This facilitates personalized stratification for NAFLD and metabolism-related outcomes based on digital non-invasive markers, enabling preventive interventions.