循环蛋白生物标志物及其与易损斑块特征的关联——PROSPECT II亚研究

IF 2.1 Q3 PERIPHERAL VASCULAR DISEASE
Tania Sharma , Akiko Maehara , Michael Maeng , Lars Kjøller-Hansen , Thomas Engstrøm , Ori Ben-Yehuda , Mitsuaki Matsumura , Ole Fröbert , Jonas Persson , Rune Wiseth , Alf Inge Larsen , Sasha Koul , Rebecca Rylance , Gary S. Mintz , Ziad A. Ali , Stefan K. James , Gregg W. Stone , David Erlinge
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引用次数: 0

摘要

在PROSPECT-II研究中,近红外光谱(NIRS)和血管内超声(IVUS)被用于表征冠状动脉粥样硬化斑块。nirs衍生的脂质核心负担指数(LCBI)和ivus衍生的斑块负担(PB)能够识别与不良心血管事件密切相关的斑块。我们的目的是确定与冠状动脉LCBI或PB相关的生物标志物。方法对898例近期心肌梗死患者行经皮冠状动脉介入治疗。采集血液样本分析179种与心血管疾病相关的血浆蛋白水平,并使用NIRS-IVUS联合导管分析冠状动脉。计算生物标志物与相关结果之间调整后的线性回归模型,并对多重检验进行调整。用log-rank检验评估生物标志物Kaplan-Meier生存曲线的中位数。计算主要不良心血管事件的校正Cox比例模型。结果24种蛋白与PB相关,28种蛋白与LCBI相关。其中8项生物标志物与泛冠状动脉LCBI和PB升高相关;IL-18R1, CSF-1, VEGFA, EN-RAGE,组织蛋白酶D, PCSK9,转铁蛋白受体蛋白1和OPN。在调整了多样性后,血管生成素样3 (ANGPTL3)与LCBI的相关性保持不变,IL-18R1和CSF-1与PB的相关性保持不变。结论我们能够识别出与PB和LCBI相关的不同生物标志物模式。IL-18R1和CSF-1与PB有较强的相关性。ANGPTL3与富脂斑块相关,但与PB无关,支持其在易损斑块的脂质积累和发展中的作用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Circulating protein biomarkers and their association with vulnerable plaque characteristics – a PROSPECT II substudy

Background

In the PROSPECT-II study, near infrared spectroscopy (NIRS) and intravascular ultrasound (IVUS) was used to characterize atherosclerotic plaques in the coronary arteries. NIRS-derived lipid core burden index (LCBI) and IVUS-derived plaque burden (PB) were able to identify plaques strongly associated with adverse cardiovascular events.

Aim

Our aim was to identify biomarkers associated with LCBI or PB in the coronary arteries.

Methods

898 patients with recent myocardial infarction underwent percutaneous coronary intervention. Blood samples to analyze plasma levels of 179 proteins associated with cardiovascular disease were procured and a combined NIRS-IVUS catheter was used to analyze the coronary arteries. Adjusted linear regression models were calculated between the biomarkers and the outcomes of interest, adjusted for multiplicity testing. Kaplan-Meier survival curves of biomarkers divided by median were assessed with the log-rank test. Adjusted Cox proportional models were calculated for major adverse cardiovascular events.

Results

A total of 24 proteins were associated with PB and 28 proteins with LCBI. Eight of these biomarkers were associated with both increased pan-coronary LCBI and PB; IL-18R1, CSF-1, VEGFA, EN-RAGE, cathepsin D, PCSK9, transferrin receptor protein 1 and OPN. After adjusting for multiplicity, angiopoietin like 3 (ANGPTL3) retained its association with LCBI, and IL-18R1 and CSF-1 retained their association with PB.

Conclusion

We were able to identify distinct biomarker patterns associated with PB and LCBI. IL-18R1 and CSF-1 had a strong relationship with PB. ANGPTL3 was associated with lipid rich plaques but not with PB, supporting its role in lipid accumulation and development of vulnerable plaques.
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