From the Framingham Heart Study, Framingham, MA (X.Y., S.S., S.J.H., C.J.O., C.S.F., P.C., M.G.L., D.L.); Department of Biostatistics, Boston University, Boston, MA (M.G.L., X.Y.); Division of Intramural Research and Population Sciences Branch, National Heart, Lung, and Blood Institute, Bethesda, MD (S.S., S.J.H., C.J.O., C.S.F., D.L.); BG Medicine, Inc, Waltham, MA (P.J., P.M., N.G., A.A.); Department of Mathematics and Statistics, Boston University, Boston, MA (M.G.L.); and Department of Medicine and the Cardiology Division, Boston Medical Center, Boston, MA (D.L.).
Arterioscler Thromb Vasc Biol. 2014 Apr;34(4):939-45. doi: 10.1161/ATVBAHA.113.302918. Epub 2014 Feb 13.
Incorporation of novel plasma protein biomarkers may improve current models for prediction of atherosclerotic cardiovascular disease (ASCVD) risk.
We used discovery mass spectrometry (MS) to determine plasma concentrations of 861 proteins in 135 myocardial infarction (MI) cases and 135 matched controls. Then, we measured 59 markers by targeted MS in 336 ASCVD case-control pairs. Associations with MI or ASCVD were tested in single-marker and multiple-marker analyses adjusted for established ASCVD risk factors. Twelve single markers from discovery MS were associated with MI incidence (at P<0.01), adjusting for clinical risk factors. Seven proteins in aggregate (cyclophilin A, cluster of differentiation 5 molecule [CD5] antigen-like, cell-surface glycoprotein mucin cell surface associated protein 18 [MUC-18], collagen-α 1 [XVIII] chain, salivary α-amylase 1, C-reactive protein, and multimerin-2) were highly associated with MI (P<0.0001) and significantly improved its prediction compared with a model with clinical risk factors alone (C-statistic of 0.71 versus 0.84). Through targeted MS, 12 single proteins were predictors of ASCVD (at P<0.05) after adjusting for established risk factors. In multiple-marker analyses, 4 proteins in combination (α-1-acid glycoprotein 1, paraoxonase 1, tetranectin, and CD5 antigen-like) predicted incident ASCVD (P<0.0001) and moderately improved the C-statistic from the model with clinical covariates alone (C-statistic of 0.69 versus 0.73).
Proteomics profiling identified single- and multiple-marker protein panels that are associated with new-onset ASCVD and may lead to a better understanding of underlying disease mechanisms. Our findings include many novel protein biomarkers that, if externally validated, may improve risk assessment for MI and ASCVD.
纳入新型血浆蛋白生物标志物可能会改进目前用于预测动脉粥样硬化性心血管疾病(ASCVD)风险的模型。
我们使用发现质谱(MS)来确定 135 例心肌梗死(MI)病例和 135 例匹配对照者的 861 种血浆蛋白的浓度。然后,我们在 336 例 ASCVD 病例对照对中通过靶向 MS 测量了 59 个标志物。在调整了 ASCVD 既定风险因素后,通过单标记和多标记分析来测试与 MI 或 ASCVD 的相关性。来自发现 MS 的 12 个单标记物与 MI 发生率相关(P<0.01),且调整了临床风险因素。七种蛋白聚集物(亲环蛋白 A、CD5 抗原样、细胞表面糖蛋白黏蛋白细胞表面相关蛋白 18(MUC-18)、胶原-α1[XVIII]链、唾液α-淀粉酶 1、C 反应蛋白和多聚蛋白-2)与 MI 密切相关(P<0.0001),且与仅具有临床风险因素的模型相比,其对 MI 的预测有显著改善(C 统计量为 0.71 与 0.84)。通过靶向 MS,在调整了既定风险因素后,有 12 个单蛋白可预测 ASCVD(P<0.05)。在多标记分析中,4 种蛋白组合(α-1-酸性糖蛋白 1、对氧磷酶 1、四旋蛋白和 CD5 抗原样)可预测 ASCVD 的发生(P<0.0001),并适度提高了仅具有临床协变量模型的 C 统计量(C 统计量为 0.69 与 0.73)。
蛋白质组学分析鉴定了与新发 ASCVD 相关的单标记物和多标记物蛋白谱,这可能有助于更好地理解潜在的疾病机制。我们的发现包括许多新型蛋白生物标志物,如果经过外部验证,可能会改善 MI 和 ASCVD 的风险评估。