本文由小咖机器人翻译整理

期刊来源：Nat Med

原文链接：https://doi.org/10.1038/s41591-024-03010-w

摘要内容如下：

纤维化疾病影响多个器官，并与发病率和死亡率相关。为了研究不同器官纤维化的器官特异性和共同的生物学机制，我们开发了机器学习模型来量化43881名接受磁共振成像的英国生物库参与者的肝脏、胰腺、心脏和肾脏的T1时间（间质纤维化的标志物）。在全表型关联分析中，我们证明了器官特异性T1时间增加（反映间质纤维化增加）与多个器官系统的流行疾病之间的关联。在全基因组关联分析中，我们分别确定了27个、18个、11个和10个与肝脏、胰腺、心肌和肾皮质T1时间相关的独立基因位点。所检查的器官之间存在适度的遗传相关性。几个基因座在所检测的器官中重叠，涉及参与多种生物途径的基因，包括金属离子转运（SLC39A8、HFE和TMPRSS6）、葡萄糖代谢（PCK2）、血型抗原（ABO和FUT2）、免疫功能（BANK1和PPP3CA）、炎症（NFKB1）和有丝分裂（CENPE）。最后，我们发现，T1时间落在前五位的器官数量增加与人群死亡率增加有关。在多变量校正分析中，在≥3个器官中具有高纤维化负担的个体与在所有检查器官中具有低纤维化负担的个体相比，死亡率增加3倍（风险比=3.31，95%置信区间1.77-6.19；P=1.78×10-4）。通过利用机器学习来量化多个器官的T1时间，我们发现了可能提供治疗靶点的纤维化的新的器官特异性和共享的生物途径。

英文原文如下：

Abstracts

Fibrotic diseases affect multiple organs and are associated with morbidity and mortality. To examine organ-specific and shared biologic mechanisms that underlie fibrosis in different organs, we developed machine learning models to quantify T1 time, a marker of interstitial fibrosis, in the liver, pancreas, heart and kidney among 43,881 UK Biobank participants who underwent magnetic resonance imaging. In phenome-wide association analyses, we demonstrate the association of increased organ-specific T1 time, reflecting increased interstitial fibrosis, with prevalent diseases across multiple organ systems. In genome-wide association analyses, we identified 27, 18, 11 and 10 independent genetic loci associated with liver, pancreas, myocardial and renal cortex T1 time, respectively. There was a modest genetic correlation between the examined organs. Several loci overlapped across the examined organs implicating genes involved in a myriad of biologic pathways including metal ion transport (SLC39A8, HFE and TMPRSS6), glucose metabolism (PCK2), blood group antigens (ABO and FUT2), immune function (BANK1 and PPP3CA), inflammation (NFKB1) and mitosis (CENPE). Finally, we found that an increasing number of organs with T1 time falling in the top quintile was associated with increased mortality in the population. Individuals with a high burden of fibrosis in ≥3 organs had a 3-fold increase in mortality compared to those with a low burden of fibrosis across all examined organs in multivariable-adjusted analysis (hazard ratio = 3.31, 95% confidence interval 1.77-6.19; P = 1.78 × 10-4). By leveraging machine learning to quantify T1 time across multiple organs at scale, we uncovered new organ-specific and shared biologic pathways underlying fibrosis that may provide therapeutic targets.

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