Chen Yazhou, Laevens Benjamin P M, Lemainque Teresa, Müller-Franzes Gustav Anton, Seibel Tobias, Dlugosch Carola, Clusmann Jan, Koop Paul-Henry, Gong Rongpeng, Liu Yuanyuan, Jakhar Niharika, Cao Feng, Schophaus Simon, Raju Thriveni Basavanapura, Raptis Anastasia Artemis, van Haag Felix, Joy Joel, Loomba Rohit, Valenti Luca, Kather Jakob Nikolas, Brinker Titus J, Herzog Moritz, Costa Ivan G, Hernando Diego, Schneider Kai Markus, Truhn Daniel, Schneider Carolin V
Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany.
Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany.
Liver Int. 2025 Jul;45(7):e70164. doi: 10.1111/liv.70164.
Steatotic liver disease (SLD) is the most common liver disease worldwide, affecting 30% of the global population. It is strongly associated with the interplay of genetic and lifestyle-related risk factors. The genetic variant accounting for the largest fraction of SLD heritability is PNPLA3 I148M, which is carried by 23% of the western population and increases the risk of SLD two to three-fold. However, identification of variant carriers is not part of routine clinical care and prevents patients from receiving personalised care.
We analysed MRI images and common genetic variants in PNPLA3, TM6SF2, MTARC1, HSD17B13 and GCKR from a cohort of 45 603 individuals from the UK Biobank. Proton density fat fraction (PDFF) maps were generated using a water-fat separation toolbox, applied to the magnitude and phase MRI data. The liver region was segmented using a U-Net model trained on 600 manually segmented ground truth images. The resulting liver masks and PDFF maps were subsequently used to calculate liver PDFF values. Individuals with (PDFF ≥ 5%) and without SLD (PDFF < 5%) were selected as the study cohort and used to train and test a Vision Transformer classification model with five-fold cross validation. We aimed to differentiate individuals who are homozygous for the PNPLA3 I148M variant from non-carriers, as evaluated by the area under the receiver operating characteristic curve (AUROC). To ensure a clear genetic contrast, all heterozygous individuals were excluded. To interpret our model, we generated attention maps that highlight the regions that are most predictive of the outcomes.
Homozygosity for the PNPLA3 I148M variant demonstrated the best predictive performance among five variants with AUROC of 0.68 (95% CI: 0.64-0.73) in SLD patients and 0.57 (95% CI: 0.52-0.61) in non-SLD patients. The AUROCs for the other SNPs ranged from 0.54 to 0.57 in SLD patients and from 0.52 to 0.54 in non-SLD patients. The predictive performance was generally higher in SLD patients compared to non-SLD patients. Attention maps for PNPLA3 I148M carriers showed that fat deposition in regions adjacent to the hepatic vessels, near the liver hilum, plays an important role in predicting the presence of the I148M variant.
Our study marks novel progress in the non-invasive detection of homozygosity for PNPLA3 I148M through the application of deep learning models on MRI images. Our findings suggest that PNPLA3 I148M might affect the liver fat distribution and could be used to predict the presence of PNPLA3 variants in patients with fatty liver. The findings of this research have the potential to be integrated into standard clinical practice, particularly when combined with clinical and biochemical data from other modalities to increase accuracy, enabling easier identification of at-risk individuals and facilitating the development of tailored interventions for PNPLA3 I148M-associated liver disease.
脂肪性肝病（SLD）是全球最常见的肝脏疾病，影响着全球30%的人口。它与遗传和生活方式相关风险因素的相互作用密切相关。占SLD遗传力最大比例的基因变异是PNPLA3 I148M，西方人群中有23%携带该变异，其使SLD风险增加两到三倍。然而，识别变异携带者并非常规临床护理的一部分，这阻碍了患者接受个性化护理。
我们分析了来自英国生物银行的45603名个体队列的MRI图像以及PNPLA3、TM6SF2、MTARC1、HSD17B13和GCKR中的常见基因变异。使用水脂分离工具箱生成质子密度脂肪分数（PDFF）图，并应用于幅度和相位MRI数据。使用在600张手动分割的真实图像上训练的U-Net模型对肝脏区域进行分割。随后，使用所得的肝脏掩码和PDFF图来计算肝脏PDFF值。选择患有SLD（PDFF≥5%）和未患SLD（PDFF<5%）的个体作为研究队列，并用于训练和测试具有五重交叉验证的视觉Transformer分类模型。我们旨在通过接受者操作特征曲线下面积（AUROC）评估，区分PNPLA3 I148M变异纯合子个体与非携带者。为确保清晰的遗传对比，所有杂合个体均被排除。为解释我们的模型，我们生成了注意力图，突出显示对结果最具预测性的区域。
在SLD患者中，PNPLA3 I148M变异纯合子在五个变异中表现出最佳预测性能，AUROC为0.68（95%CI：0.64 - 0.73），在非SLD患者中为0.57（95%CI：0.52 - 0.61）。其他单核苷酸多态性（SNP）在SLD患者中的AUROC范围为0.54至0.57，在非SLD患者中为0.52至0.54。与非SLD患者相比，SLD患者的预测性能总体更高。PNPLA3 I148M携带者的注意力图显示，肝门附近肝血管相邻区域的脂肪沉积在预测I148M变异的存在中起重要作用。
我们的研究通过在MRI图像上应用深度学习模型，在无创检测PNPLA3 I148M纯合子方面取得了新进展。我们的研究结果表明，PNPLA3 I148M可能影响肝脏脂肪分布，并可用于预测脂肪肝患者中PNPLA3变异的存在。本研究结果有可能整合到标准临床实践中，特别是与来自其他模式的临床和生化数据相结合以提高准确性时，能够更轻松地识别高危个体，并促进针对PNPLA3 I148M相关肝病的个性化干预措施的制定。
