William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, London, UK.
Departament de Matemàtiques i Informàtica, Universitat de Barcelona, Artificial Intelligence in Medicine Lab (BCN-AIM), Barcelona, Spain.
J Magn Reson Imaging. 2023 Dec;58(6):1797-1812. doi: 10.1002/jmri.28675. Epub 2023 Mar 16.
Biological heart age estimation can provide insights into cardiac aging. However, existing studies do not consider differential aging across cardiac regions.
To estimate biological age of the left ventricle (LV), right ventricle (RV), myocardium, left atrium, and right atrium using magnetic resonance imaging radiomics phenotypes and to investigate determinants of aging by cardiac region.
Cross-sectional.
A total of 18,117 healthy UK Biobank participants including 8338 men (mean age = 64.2 ± 7.5) and 9779 women (mean age = 63.0 ± 7.4).
FIELD STRENGTH/SEQUENCE: A 1.5 T/balanced steady-state free precession.
An automated algorithm was used to segment the five cardiac regions, from which radiomic features were extracted. Bayesian ridge regression was used to estimate biological age of each cardiac region with radiomics features as predictors and chronological age as the output. The "age gap" was the difference between biological and chronological age. Linear regression was used to calculate associations of age gap from each cardiac region with socioeconomic, lifestyle, body composition, blood pressure and arterial stiffness, blood biomarkers, mental well-being, multiorgan health, and sex hormone exposures (n = 49).
Multiple testing correction with false discovery method (threshold = 5%).
The largest model error was with RV and the smallest with LV age (mean absolute error in men: 5.26 vs. 4.96 years). There were 172 statistically significant age gap associations. Greater visceral adiposity was the strongest correlate of larger age gaps, for example, myocardial age gap in women (Beta = 0.85, P = 1.69 × 10 ). Poor mental health associated with large age gaps, for example, "disinterested" episodes and myocardial age gap in men (Beta = 0.25, P = 0.001), as did a history of dental problems (eg LV in men Beta = 0.19, P = 0.02). Higher bone mineral density was the strongest associate of smaller age gaps, for example, myocardial age gap in men (Beta = -1.52, P = 7.44 × 10 ).
This work demonstrates image-based heart age estimation as a novel method for understanding cardiac aging.
Stage 1.
生物心脏年龄估测可以提供心脏老化的见解。然而，现有研究并未考虑到心脏不同区域的差异老化。
使用磁共振成像放射组学表型来估计左心室（LV）、右心室（RV）、心肌、左心房和右心房的生物年龄，并研究心脏区域的老化决定因素。
横断面研究。
总共 18117 名英国生物库健康参与者，包括 8338 名男性（平均年龄 64.2±7.5）和 9779 名女性（平均年龄 63.0±7.4）。
场强/序列：1.5T/平衡稳态自由进动。
使用自动算法分割五个心脏区域，从这些区域中提取放射组学特征。贝叶斯脊回归用于估计每个心脏区域的生物年龄，以放射组学特征作为预测因子，以实际年龄作为输出。“年龄差距”是生物年龄与实际年龄之间的差异。线性回归用于计算每个心脏区域的年龄差距与社会经济、生活方式、身体成分、血压和动脉僵硬、血液生物标志物、心理健康、多器官健康和性激素暴露之间的关联（n=49）。
使用错误发现率方法（阈值=5%）进行多次检验校正。
RV 的模型误差最大，LV 年龄的模型误差最小（男性平均绝对误差：5.26 岁 vs. 4.96 岁）。有 172 个统计学上显著的年龄差距关联。更大的内脏脂肪是更大年龄差距的最强相关因素，例如女性的心肌年龄差距（Beta=0.85，P=1.69×10-8）。心理健康状况较差与较大的年龄差距相关，例如男性的“无兴趣”发作和心肌年龄差距（Beta=0.25，P=0.001），以及牙齿问题史（例如，男性 LV 的 Beta=0.19，P=0.02）。较高的骨密度是较小年龄差距的最强相关因素，例如男性的心肌年龄差距（Beta=-1.52，P=7.44×10-8）。
这项工作展示了基于图像的心脏年龄估测作为一种了解心脏老化的新方法。
1 级。
阶段 1。
