Cardiovascular measures from abdominal MRI provide insights into abdominal vessel genetic architecture

Comprehensive Summary

This population imaging and genetic association study aimed to determine whether it is possible to derive cardiovascular measures from routine abdominal MRI scans and whether these measures are related to underlying genetic and disease mechanisms. The researchers used deep learning segmentation models of abdominal MRI scans of 44,541 individuals in the UK Biobank resource and used these models to derive six cardiovascular image-derived phenotypes, including heart volume and the cross-sectional areas of the major abdominal vessels, the aorta, and vena cava. These image-derived phenotypes were then compared with conventional cardiac MRI traits and their associations with cardiovascular disease outcomes. The study found strong correlations between abdominal MRI-derived cardiac traits and conventional cardiac imaging characteristics. Infrarenal aortic cross-sectional area was related to abdominal aortic aneurysm risk, and heart volume was related to arrhythmias, heart failure, and other adverse cardiac outcomes. In addition, genome-wide association analysis identified 59 loci associated with these cardiovascular image-derived phenotypes, including 15 novel loci, and polygenic risk scores based on these associations were predictive of aneurysmal and dysrhythmia phenotypes. The study concludes that abdominal MRI contains clinically and genetically important cardiovascular information, which can be used for the assessment of risk.

Outcomes and Implications

This study demonstrates the potential for abdominal MRI, commonly used for non-cardiovascular diagnostic purposes, as a tool for cardiovascular screening without the need for additional dedicated cardiac imaging. The possibility of integrating the use of AI segmentation with genetic analysis may aid clinicians in identifying individuals with a predisposition to conditions like abdominal aortic aneurysm, heart failure, and arrhythmias earlier in the course of the disease process. The development of polygenic risk scores also offers promise for the development of precision medicine approaches, as evidenced by the use of imaging markers and genetic stratification. The use of existing imaging datasets may also aid in the development of population-based surveillance of cardiovascular health without the added burden of healthcare costs and patient fatigue.