Xiao Lushan, Zeng Lin, Wang Jiaren, Hong Chang, Zhang Ziyong, Wu Chengkai, Cui Hao, Li Yan, Li Ruining, Liang Shengxing, Deng Qijie, Li Wenyuan, Zou Xuejing, Ma Pengcheng, Liu Li
Department of Health Management, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China.
Guangdong Provincial Key Laboratory of Viral Hepatitis Research, Department of Infectious Diseases, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China.
Adv Sci (Weinh). 2025 Sep;12(33):e10527. doi: 10.1002/advs.202410527. Epub 2025 May 28.
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.
非酒精性脂肪性肝病（NAFLD）是慢性肝病的主要原因，被认为是代谢综合征的肝脏表现，会引发不良后果。通过整合来自36490名英国生物银行参与者和9007名南方医院参与者的基因和临床数据，构建并验证了一个堆叠多模态机器学习模型，用于NAFLD的早期识别和预后分层，并提取其概率作为NAFLD的计算机模拟分数（ISNLD）。评估ISNLD对严重肝病（SeLD）的早期预测效果，并分析其与代谢相关结局的关联。该多模态模型在将个体分为NAFLD的低风险和高风险组方面表现令人满意，在训练集、内部测试集和外部测试集内的曲线下面积（AUC）分别达到0.843、0.840和0.872。在高风险组中，调整生活方式因素后，ISNLD与肝内及代谢相关并发症显著相关。此外，ISNLD显示出对SeLD的早期预测能力，并将高风险受试者进一步分为三个不良结局风险升高的风险亚组。研究结果强调了该模型整合多模态特征以生成ISNLD的能力，能够早期检测和预测NAFLD的预后。这有助于基于数字非侵入性标志物对NAFLD和代谢相关结局进行个性化分层，从而实现预防性干预。
