A multimodal approach to cardiovascular risk stratification in patients with type 2 diabetes incorporating retinal, genomic and clinical features
A multimodal approach to cardiovascular risk stratification in patients with type 2 diabetes incorporating retinal, genomic and clinical features
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DOI：
10.1038/s41598-019-40403-1
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发表时间：
2019-03-05
影响因子：
4.6
通讯作者：
Trucco, Emanuele
中科院分区：
文献类型：
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作者：
Fetit, Ahmed E.;Doney, Alexander S.;Trucco, Emanuele
心血管疾病是一个公共卫生问题；它们仍然是2型糖尿病患者发病和死亡的主要原因。除了基因组数据外，来自视网膜眼底图像和临床测量的表型信息可以识别心血管健康的相关生物标志物。在这项研究中，我们评估了这些生物标志物是否对主要不良心脏事件（MACE）的风险进行了分层。对2型糖尿病参与者（n = 3891）的Tayside GoDARTS生物资源提取物进行了回顾性分析。总共纳入了519个特征，总结了视网膜血管的形态测量特性、各种单核苷酸多态性（snp）以及常规临床测量。在输入缺失特征后，使用l1正则化逻辑回归（lasso）在随机抽样集（n = 2,918）上建立预测模型。该模型在一个独立的集合（n = 973）上进行评估，其性能与审查后的总体危险率相关（log-rank p < 0.0001），这表明多模态特征能够捕获MACE风险评估的重要知识。我们进一步通过自举分析表明，所有三个信息来源（视网膜，遗传，常规临床）提供了强大的信号。特别稳健的特征包括：扭曲、宽度梯度和分支点视网膜分组；已知与血压和心血管表型性状相关的snp；成像年龄；临床测量如血压和高密度脂蛋白。这种新方法可用于快速、灵敏地确定与MACE相关的未来风险。
Cardiovascular diseases are a public health concern; they remain the leading cause of morbidity and mortality in patients with type 2 diabetes. Phenotypic information available from retinal fundus images and clinical measurements, in addition to genomic data, can identify relevant biomarkers of cardiovascular health. In this study, we assessed whether such biomarkers stratified risks of major adverse cardiac events (MACE). A retrospective analysis was carried out on an extract from the Tayside GoDARTS bioresource of participants with type 2 diabetes (n = 3,891). A total of 519 features were incorporated, summarising morphometric properties of the retinal vasculature, various single nucleotide polymorphisms (SNPs), as well as routine clinical measurements. After imputing missing features, a predictive model was developed on a randomly sampled set (n = 2,918) using L1-regularised logistic regression (lasso). The model was evaluated on an independent set (n = 973) and its performance associated with overall hazard rate after censoring (log-rank p < 0.0001), suggesting that multimodal features were able to capture important knowledge for MACE risk assessment. We further showed through a bootstrap analysis that all three sources of information (retinal, genetic, routine clinical) offer robust signal. Particularly robust features included: tortuousity, width gradient, and branching point retinal groupings; SNPs known to be associated with blood pressure and cardiovascular phenotypic traits; age at imaging; clinical measurements such as blood pressure and high density lipoprotein. This novel approach could be used for fast and sensitive determination of future risks associated with MACE.