Kamineni Meghana, Raghu Vineet, Truong Buu, Alaa Ahmed, Schuermans Art, Friedman Sam, Reeder Christopher, Bhattacharya Romit, Libby Peter, Ellinor Patrick T, Maddah Mahnaz, Philippakis Anthony, Hornsby Whitney, Yu Zhi, Natarajan Pradeep
Harvard Medical School, Boston, MA.
Cardiovascular Imaging Research Center, Department of Radiology, MGH and HMS.
medRxiv. 2024 Aug 20:2024.08.16.24312129. doi: 10.1101/2024.08.16.24312129.
Despite advances in managing traditional risk factors, coronary artery disease (CAD) remains the leading cause of mortality. Circulating hematopoietic cells influence risk for CAD, but the role of a key regulating organ, spleen, is unknown. The understudied spleen is a 3-dimensional structure of the hematopoietic system optimally suited for unbiased radiologic investigations toward novel mechanistic insights.
Deep learning-based image segmentation and radiomics techniques were utilized to extract splenic radiomic features from abdominal MRIs of 42,059 UK Biobank participants. Regression analysis was used to identify splenic radiomics features associated with CAD. Genome-wide association analyses were applied to identify loci associated with these radiomics features. Overlap between loci associated with CAD and the splenic radiomics features was explored to understand the underlying genetic mechanisms of the role of the spleen in CAD.
We extracted 107 splenic radiomics features from abdominal MRIs, and of these, 10 features were associated with CAD. Genome-wide association analysis of CAD-associated features identified 219 loci, including 35 previously reported CAD loci, 7 of which were not associated with conventional CAD risk factors. Notably, variants at 9p21 were associated with splenic features such as run length non-uniformity.
Our study, combining deep learning with genomics, presents a new framework to uncover the splenic axis of CAD. Notably, our study provides evidence for the underlying genetic connection between the spleen as a candidate causal tissue-type and CAD with insight into the mechanisms of 9p21, whose mechanism is still elusive despite its initial discovery in 2007. More broadly, our study provides a unique application of deep learning radiomics to non-invasively find associations between imaging, genetics, and clinical outcomes.
尽管在传统危险因素管理方面取得了进展，但冠状动脉疾病（CAD）仍然是主要的死亡原因。循环造血细胞会影响CAD风险，但关键调节器官脾脏的作用尚不清楚。脾脏这一研究较少的器官是造血系统的三维结构，非常适合进行无偏倚的放射学研究以获得新的机制性见解。
利用基于深度学习的图像分割和放射组学技术，从42059名英国生物银行参与者的腹部MRI中提取脾脏放射组学特征。采用回归分析来识别与CAD相关的脾脏放射组学特征。应用全基因组关联分析来识别与这些放射组学特征相关的基因座。探索与CAD相关的基因座和脾脏放射组学特征之间的重叠，以了解脾脏在CAD中作用的潜在遗传机制。
我们从腹部MRI中提取了107个脾脏放射组学特征，其中10个特征与CAD相关。对与CAD相关特征的全基因组关联分析确定了219个基因座，包括35个先前报道的CAD基因座，其中7个与传统CAD危险因素无关。值得注意的是，9p21上的变异与脾脏特征如游程长度不均匀性相关。
我们的研究将深度学习与基因组学相结合，提出了一个揭示CAD脾脏轴的新框架。值得注意的是，我们的研究为脾脏作为候选因果组织类型与CAD之间的潜在遗传联系提供了证据，深入了解了9p21的机制，尽管其在2007年首次发现，但其机制仍然难以捉摸。更广泛地说，我们的研究提供了深度学习放射组学在非侵入性地发现成像、遗传学和临床结果之间关联方面的独特应用。
