Department of Laboratory Medicine, Faculty of Medicine and Health, Örebro University Hospital, Örebro, Sweden; School of Medicine, Faculty of Medicine and Health, Örebro University, Örebro, Sweden; Department of Medical Biosciences/Clinical Chemistry, Umeå University, Umeå, Sweden.
Clinical Epidemiology and Biostatistics, School of Medical Sciences, Örebro University, Örebro, Sweden.
Atherosclerosis. 2020 Nov;313:150-155. doi: 10.1016/j.atherosclerosis.2020.09.027. Epub 2020 Oct 7.
We aimed to identify plasma protein biomarkers related to inflammation that correlated with physiological measurements of vascular function and structure in healthy individuals.
We used the OLINK proteomics panel, which measures 92 inflammatory proteins, in 834 young, healthy non-smokers (ages 18-26). Principal component analysis (PCA) was employed to identify patterns of proteins. The following measurements were used: pulse-wave velocity (PWV), carotid intima-media thickness (cIMT) and augmentation index (AIX). Established cardiovascular risk factors were included in multivariable models.
PCA showed four principal components (PC 1, PC 2, PC 3, PC 4). PC 3, comprising proteins related to hemostasis, was significantly and inversely correlated with PWV. Among the proteins with the highest factor loadings on PC 3, uPA was negatively correlated with PWV in multivariable regression models. AIX was significantly correlated with PC 2, comprising inflammatory cytokines. Among the proteins with the highest factor loadings on PC 2, interleukin-6 was significantly correlated with AIX in the multivariable model. cIMT was significantly correlated with PC 4, comprising proteins related to chemotaxis. Among the proteins with the highest factor loadings on PC 4, fractalkine was significantly correlated with cIMT in the multivariable model.
In young, healthy individuals, OLINK inflammatory proteins correlated with measures of vascular status. Each of the three measures PWV, AIX, and cIMT, which target different parts of the vasculature, correlated with its own specific protein signature, indicating that different subsets of inflammatory mediators affect different parts of the vasculature and are detectable already in young healthy adults.
我们旨在确定与炎症相关的血浆蛋白生物标志物，这些标志物与健康个体的血管功能和结构的生理测量相关。
我们使用 OLINK 蛋白质组学面板，该面板测量了 834 名年轻健康的非吸烟者（年龄在 18-26 岁之间）的 92 种炎症蛋白。主成分分析（PCA）用于识别蛋白质模式。使用以下测量值：脉搏波速度（PWV）、颈动脉内膜中层厚度（cIMT）和增强指数（AIX）。纳入了心血管危险因素的多变量模型。
PCA 显示了四个主成分（PC1、PC2、PC3、PC4）。PC3 由与止血相关的蛋白质组成，与 PWV 呈显著负相关。在 PC3 上具有最高因子负荷的蛋白质中，uPA 与多变量回归模型中的 PWV 呈负相关。AIX 与包含炎症细胞因子的 PC2 显著相关。在 PC2 上具有最高因子负荷的蛋白质中，白细胞介素-6 在多变量模型中与 AIX 显著相关。cIMT 与包含趋化蛋白的 PC4 显著相关。在 PC4 上具有最高因子负荷的蛋白质中， fractalkine 与 cIMT 在多变量模型中显著相关。
在年轻健康的个体中，OLINK 炎症蛋白与血管状态的测量值相关。三个测量值 PWV、AIX 和 cIMT 分别针对血管的不同部位，与自身特定的蛋白质特征相关，这表明不同的炎症介质亚群影响血管的不同部位，并且在年轻健康的成年人中已经可以检测到。
