Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer (A.D.C., D.A.G., Y.C., E.B., A.C., D.L.M., K.L.E., R.E.M.), The University of Edinburgh, United Kingdom.
School of Psychology, University of Exeter, United Kingdom (R.M.W.).
Circ Genom Precis Med. 2024 Feb;17(1):e004265. doi: 10.1161/CIRCGEN.123.004265. Epub 2024 Jan 30.
Cardiovascular disease (CVD) is among the leading causes of death worldwide. The discovery of new omics biomarkers could help to improve risk stratification algorithms and expand our understanding of molecular pathways contributing to the disease. Here, ASSIGN-a cardiovascular risk prediction tool recommended for use in Scotland-was examined in tandem with epigenetic and proteomic features in risk prediction models in ≥12 657 participants from the Generation Scotland cohort.
Previously generated DNA methylation-derived epigenetic scores (EpiScores) for 109 protein levels were considered, in addition to both measured levels and an EpiScore for cTnI (cardiac troponin I). The associations between individual protein EpiScores and the CVD risk were examined using Cox regression (n≥1274; n≥11 383) and visualized in a tailored R application. Splitting the cohort into independent training (n=6880) and test (n=3659) subsets, a composite CVD EpiScore was then developed.
Sixty-five protein EpiScores were associated with incident CVD independently of ASSIGN and the measured concentration of cTnI (<0.05), over a follow-up of up to 16 years of electronic health record linkage. The most significant EpiScores were for proteins involved in metabolic, immune response, and tissue development/regeneration pathways. A composite CVD EpiScore (based on 45 protein EpiScores) was a significant predictor of CVD risk independent of ASSIGN and the concentration of cTnI (hazard ratio, 1.32; =3.7×10; 0.3% increase in C-statistic).
EpiScores for circulating protein levels are associated with CVD risk independent of traditional risk factors and may increase our understanding of the etiology of the disease.
心血管疾病（CVD）是全球主要死因之一。新的组学生物标志物的发现有助于改善风险分层算法，并扩展我们对导致疾病的分子途径的理解。在这里，与苏格兰推荐使用的心血管风险预测工具 ASSIGN 一起，在苏格兰基因世代队列中≥12657 名参与者的风险预测模型中检查了表观遗传和蛋白质组学特征。
考虑了先前生成的与 109 种蛋白质水平相关的 DNA 甲基化衍生的表观遗传评分（EpiScores），以及测量水平和 cTnI（心肌肌钙蛋白 I）的 EpiScore。使用 Cox 回归（n≥1274；n≥11383）检查单个蛋白质 EpiScores 与 CVD 风险之间的关联，并在定制的 R 应用程序中可视化。将队列分为独立的训练（n=6880）和测试（n=3659）子集，然后开发了综合 CVD EpiScore。
在长达 16 年的电子健康记录链接随访中，65 种蛋白质 EpiScores 与 CVD 事件独立于 ASSIGN 和 cTnI 的测量浓度相关（<0.05）。最显著的 EpiScores 是涉及代谢、免疫反应和组织发育/再生途径的蛋白质。基于 45 种蛋白质 EpiScores 的综合 CVD EpiScore 是 CVD 风险的独立预测因子，独立于 ASSIGN 和 cTnI 浓度（风险比，1.32；=3.7×10；C 统计量增加 0.3%）。
循环蛋白水平的 EpiScores 与 CVD 风险独立于传统危险因素相关，可能增加我们对疾病病因的理解。
