Brundage James, Barrios Joshua P, Tison Geoffrey H, Pirruccello James P
Division of Cardiology, University of California San Francisco, San Francisco, CA, USA.
Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA, USA.
medRxiv. 2024 Aug 6:2024.08.02.24310874. doi: 10.1101/2024.08.02.24310874.
Heart structure and function change with age, and the notion that the heart may age faster for some individuals than for others has driven interest in estimating cardiac age acceleration. However, current approaches have limited feature richness (heart measurements; radiomics) or capture extraneous data and therefore lack cardiac specificity (deep learning [DL] on unmasked chest MRI). These technical limitations have been a barrier to efforts to understand genetic contributions to age acceleration. We hypothesized that a video-based DL model provided with heart-masked MRI data would capture a rich yet cardiac-specific representation of cardiac aging. In 61,691 UK Biobank participants, we excluded noncardiac pixels from cardiac MRI and trained a video-based DL model to predict age from one cardiac cycle in the 4-chamber view. We then computed cardiac age acceleration as the bias-corrected prediction of heart age minus the calendar age. Predicted heart age explained 71.1% of variance in calendar age, with a mean absolute error of 3.3 years. Cardiac age acceleration was linked to unfavorable cardiac geometry and systolic and diastolic dysfunction. We also observed links between cardiac age acceleration and diet, decreased physical activity, increased alcohol and tobacco use, and altered levels of 239 serum proteins, as well as adverse brain MRI characteristics. We found cardiac age acceleration to be heritable (h2g 26.6%); a genome-wide association study identified 8 loci related to linked to cardiomyopathy (near and ) and an additional 16 loci (near ). Of the discovered loci, 21 were not previously associated with cardiac age acceleration. Mendelian randomization revealed that lower genetically mediated levels of 6 circulating proteins (MSRA most strongly), as well as greater levels of 5 proteins (LXN most strongly) were associated with cardiac age acceleration, as were greater blood pressure and Lp(a). A polygenic score for cardiac age acceleration predicted earlier onset of arrhythmia, heart failure, myocardial infarction, and mortality. These findings provide a thematic understanding of cardiac age acceleration and suggest that heart- and vascular-specific factors are key to cardiac age acceleration, predominating over a more global aging program.
心脏结构和功能会随着年龄的增长而发生变化,而且有些人的心脏衰老速度可能比其他人更快,这一观点引发了人们对估计心脏年龄加速情况的兴趣。然而,目前的方法特征丰富度有限(心脏测量;放射组学),或者会捕捉到无关数据,因此缺乏心脏特异性(对未遮盖胸部MRI进行深度学习[DL])。这些技术限制一直是理解基因对年龄加速影响的障碍。我们假设,一个基于视频的DL模型,若配备心脏遮盖的MRI数据,将能够捕捉到丰富且具有心脏特异性的心脏衰老表征。在61691名英国生物银行参与者中,我们从心脏MRI中排除了非心脏像素,并训练了一个基于视频的DL模型,以根据四腔心视图中的一个心动周期来预测年龄。然后,我们将心脏年龄加速计算为经偏差校正后的心脏年龄预测值减去实际年龄。预测的心脏年龄解释了实际年龄71.1%的方差,平均绝对误差为3.3岁。心脏年龄加速与不良的心脏几何形状以及收缩和舒张功能障碍有关。我们还观察到心脏年龄加速与饮食、身体活动减少、酒精和烟草使用增加、239种血清蛋白水平改变以及不良的脑MRI特征之间存在关联。我们发现心脏年龄加速具有遗传性(遗传率h2g为26.6%);一项全基因组关联研究确定了8个与心肌病相关的基因座(靠近 和 )以及另外16个基因座(靠近 )。在发现的基因座中,有21个之前未与心脏年龄加速相关联。孟德尔随机化分析表明,6种循环蛋白(最显著的是MSRA)的遗传介导水平较低,以及5种蛋白(最显著的是LXN)的水平较高,与心脏年龄加速有关,血压升高和Lp(a)升高也与之有关。心脏年龄加速的多基因评分预测心律失常、心力衰竭、心肌梗死和死亡的发病时间更早。这些发现提供了对心脏年龄加速的主题性理解,并表明心脏和血管特异性因素是心脏年龄加速的关键,比更全面的衰老程序更为重要。