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基于心血管磁共振影像组学的生物心脏年龄估算。

Estimation of biological heart age using cardiovascular magnetic resonance radiomics.

机构信息

William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ, UK.

Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, West Smithfield, London, EC1A 7BE, UK.

出版信息

Sci Rep. 2022 Jul 27;12(1):12805. doi: 10.1038/s41598-022-16639-9.

DOI:10.1038/s41598-022-16639-9
PMID:35896705
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9329281/
Abstract

We developed a novel interpretable biological heart age estimation model using cardiovascular magnetic resonance radiomics measures of ventricular shape and myocardial character. We included 29,996 UK Biobank participants without cardiovascular disease. Images were segmented using an automated analysis pipeline. We extracted 254 radiomics features from the left ventricle, right ventricle, and myocardium of each study. We then used Bayesian ridge regression with tenfold cross-validation to develop a heart age estimation model using the radiomics features as the model input and chronological age as the model output. We examined associations of radiomics features with heart age in men and women, observing sex-differential patterns. We subtracted actual age from model estimated heart age to calculate a "heart age delta", which we considered as a measure of heart aging. We performed a phenome-wide association study of 701 exposures with heart age delta. The strongest correlates of heart aging were measures of obesity, adverse serum lipid markers, hypertension, diabetes, heart rate, income, multimorbidity, musculoskeletal health, and respiratory health. This technique provides a new method for phenotypic assessment relating to cardiovascular aging; further studies are required to assess whether it provides incremental risk information over current approaches.

摘要

我们开发了一种新的可解释的生物心脏年龄估算模型,该模型使用心血管磁共振放射组学测量心室形状和心肌特征。我们纳入了 29996 名无心血管疾病的英国生物库参与者。使用自动分析流水线对图像进行分割。我们从每个研究的左心室、右心室和心肌中提取了 254 个放射组学特征。然后,我们使用贝叶斯脊回归和十折交叉验证,使用放射组学特征作为模型输入,使用实际年龄作为模型输出,开发心脏年龄估算模型。我们观察了男性和女性放射组学特征与心脏年龄的关联,发现了性别差异模式。我们从模型估计的心脏年龄中减去实际年龄,计算出“心脏年龄差值”,我们认为这是衡量心脏老化的一个指标。我们对 701 种暴露因素与心脏年龄差值进行了表型全基因组关联研究。与心脏老化最密切相关的是肥胖、不良血清脂质标志物、高血压、糖尿病、心率、收入、多种合并症、肌肉骨骼健康和呼吸健康。这项技术为与心血管老化相关的表型评估提供了一种新方法;需要进一步的研究来评估它是否比当前方法提供了额外的风险信息。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ff9b/9329281/aa2752930783/41598_2022_16639_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ff9b/9329281/9ea8d68559fb/41598_2022_16639_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ff9b/9329281/4f3ea1864cad/41598_2022_16639_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ff9b/9329281/102e811bc053/41598_2022_16639_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ff9b/9329281/aa2752930783/41598_2022_16639_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ff9b/9329281/9ea8d68559fb/41598_2022_16639_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ff9b/9329281/4f3ea1864cad/41598_2022_16639_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ff9b/9329281/102e811bc053/41598_2022_16639_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ff9b/9329281/aa2752930783/41598_2022_16639_Fig4_HTML.jpg

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