英国伦敦帝国学院数据科学研究所Daniel Rueckert研究组取得一项新突破。他们进行了基于群体的心脏和主动脉结构与功能的全表型研究。相关论文于2020年8月24日发表于《自然-医学》。

他们使用基于机器学习的自动化分析方法，对基于人口的UK Biobank心血管磁共振图像进行了分析。他们报告了26,893名参与者的心脏和主动脉的综合结构和功能表型，并探讨了这些表型如何根据性别、年龄和主要心血管危险因素而变化。他们通过全表型关联研究扩展了该分析，在该研究中，他们测试了参与者广泛的非成像表型与成像表型之间的相关性。

他们通过观察分析和孟德尔随机化进一步探讨了成像表型与早期生活因素、心理健康和认知功能的关系。他们的研究说明了如何使用基于群体的心脏和主动脉成像表型，来更好地定义心血管疾病的风险以及心脑健康的相互作用，从而突出了研究疾病机制和开发基于图像的生物标记物的新机遇。

据了解，心脏和主动脉结构和功能的差异与心血管疾病和其他多种疾病有关。

附：英文原文

Title: A population-based phenome-wide association study of cardiac and aortic structure and function

Author: Wenjia Bai, Hideaki Suzuki, Jian Huang, Catherine Francis, Shuo Wang, Giacomo Tarroni, Florian Guitton, Nay Aung, Kenneth Fung, Steffen E. Petersen, Stefan K. Piechnik, Stefan Neubauer, Evangelos Evangelou, Abbas Dehghan, Declan P. ORegan, Martin R. Wilkins, Yike Guo, Paul M. Matthews, Daniel Rueckert

Issue&Volume: 2020-08-24

Abstract: Differences in cardiac and aortic structure and function are associated with cardiovascular diseases and a wide range of other types of disease. Here we analyzed cardiovascular magnetic resonance images from a population-based study, the UK Biobank, using an automated machine-learning-based analysis pipeline. We report a comprehensive range of structural and functional phenotypes for the heart and aorta across 26,893 participants, and explore how these phenotypes vary according to sex, age and major cardiovascular risk factors. We extended this analysis with a phenome-wide association study, in which we tested for correlations of a wide range of non-imaging phenotypes of the participants with imaging phenotypes. We further explored the associations of imaging phenotypes with early-life factors, mental health and cognitive function using both observational analysis and Mendelian randomization. Our study illustrates how population-based cardiac and aortic imaging phenotypes can be used to better define cardiovascular disease risks as well as heart–brain health interactions, highlighting new opportunities for studying disease mechanisms and developing image-based biomarkers.

DOI: 10.1038/s41591-020-1009-y

Source: https://www.nature.com/articles/s41591-020-1009-y