Division of Cardiology, University of California San Francisco, 555 Mission Bay Blvd South #3118, San Francisco, CA 94158, USA.
Institute for Human Genetics, University of California San Francisco, San Francisco, CA 94158, USA.
Eur Heart J. 2024 Oct 21;45(40):4318-4332. doi: 10.1093/eurheartj/ehae474.
This study assessed whether a model incorporating clinical features and a polygenic score for ascending aortic diameter would improve diameter estimation and prediction of adverse thoracic aortic events over clinical features alone.
Aortic diameter estimation models were built with a 1.1 million-variant polygenic score (AORTA Gene) and without it. Models were validated internally in 4394 UK Biobank participants and externally in 5469 individuals from Mass General Brigham (MGB) Biobank, 1298 from the Framingham Heart Study (FHS), and 610 from All of Us. Model fit for adverse thoracic aortic events was compared in 401 453 UK Biobank and 164 789 All of Us participants.
AORTA Gene explained more of the variance in thoracic aortic diameter compared to clinical factors alone: 39.5% (95% confidence interval 37.3%-41.8%) vs. 29.3% (27.0%-31.5%) in UK Biobank, 36.5% (34.4%-38.5%) vs. 32.5% (30.4%-34.5%) in MGB, 41.8% (37.7%-45.9%) vs. 33.0% (28.9%-37.2%) in FHS, and 34.9% (28.8%-41.0%) vs. 28.9% (22.9%-35.0%) in All of Us. AORTA Gene had a greater area under the receiver operating characteristic curve for identifying diameter ≥ 4 cm: 0.836 vs. 0.776 (P < .0001) in UK Biobank, 0.808 vs. 0.767 in MGB (P < .0001), 0.856 vs. 0.818 in FHS (P < .0001), and 0.827 vs. 0.791 (P = .0078) in All of Us. AORTA Gene was more informative for adverse thoracic aortic events in UK Biobank (P = .0042) and All of Us (P = .049).
A comprehensive model incorporating polygenic information and clinical risk factors explained 34.9%-41.8% of the variation in ascending aortic diameter, improving the identification of ascending aortic dilation and adverse thoracic aortic events compared to clinical risk factors.
本研究旨在评估在临床特征基础上加入升主动脉直径的多基因评分模型是否能提高直径评估和预测不良胸主动脉事件的能力。
使用包含 110 万个变异的多基因评分（AORTA Gene）和不包含它的模型来构建主动脉直径估计模型。模型在英国生物银行的 4394 名参与者中进行内部验证，并在马萨诸塞州综合医院生物银行的 5469 名参与者、弗雷明汉心脏研究的 1298 名参与者和 All of Us 的 610 名参与者中进行外部验证。在英国生物银行的 401453 名参与者和 All of Us 的 164789 名参与者中比较了不良胸主动脉事件的模型拟合情况。
与仅基于临床因素的模型相比，AORTA Gene 能更好地解释胸主动脉直径的变异：在英国生物银行中，解释的变异比例为 39.5%（95%置信区间 37.3%-41.8%），而仅基于临床因素的模型为 29.3%（27.0%-31.5%）；在马萨诸塞州综合医院生物银行中，解释的变异比例为 36.5%（34.4%-38.5%），而仅基于临床因素的模型为 32.5%（30.4%-34.5%）；在弗雷明汉心脏研究中，解释的变异比例为 41.8%（37.7%-45.9%），而仅基于临床因素的模型为 33.0%（28.9%-37.2%）；在 All of Us 中，解释的变异比例为 34.9%（28.8%-41.0%），而仅基于临床因素的模型为 28.9%（22.9%-35.0%）。AORTA Gene 对识别直径≥4cm 的主动脉直径有更好的受试者工作特征曲线下面积：在英国生物银行中为 0.836 与 0.776（P <.0001），在马萨诸塞州综合医院生物银行中为 0.808 与 0.767（P <.0001），在弗雷明汉心脏研究中为 0.856 与 0.818（P <.0001），在 All of Us 中为 0.827 与 0.791（P =.0078）。在英国生物银行（P =.0042）和 All of Us （P =.049）中，AORTA Gene 对不良胸主动脉事件的预测更具信息性。
纳入多基因信息和临床危险因素的综合模型解释了升主动脉直径变异的 34.9%-41.8%，与临床危险因素相比，该模型提高了对升主动脉扩张和不良胸主动脉事件的识别能力。
