慢性冠状动脉综合征患者的社会心理负担与预后生物标志物之间的关系:STABILITY 子研究。

IF 8.4 2区 医学 Q1 CARDIAC & CARDIOVASCULAR SYSTEMS
Charlotte Wassberg, Gorav Batra, Nermin Hadziosmanovic, Emil Hagström, Harvey D White, Ralph A H Stewart, Agneta Siegbahn, Lars Wallentin, Claes Held
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引用次数: 0

摘要

目的:研究慢性冠状动脉综合征患者的社会心理负担与反映病理生理途径的生物标志物之间的关系:从自评问卷中收集社会心理(PS)因素,并收集代表炎症的生物标记物(高敏[hs]-C 反应蛋白[CRP]、白细胞介素-6 [IL-6]、脂蛋白相关磷脂酶 A2、胰岛素-1[CRP])、在 STABILITY 试验中,对 12,492 名慢性冠状动脉综合征患者进行了炎症(脂蛋白相关磷脂酶 A2 [Lp-PLA2])和心脏损伤/压力(hs-肌钙蛋白 T [hs-TnT]、N-末端前 B 型利钠肽 [NT-proBNP])生物标志物的测量。采用调整几何平均比(GMR)的线性模型评估了各社会心理因素水平(从不-很少(参考值)、有时、经常-总是)与生物标志物之间的关系。该模型由四个因素("情绪低落"、"失去兴趣"、"经济压力 "和 "独居")组成,这四个因素以前曾被证明与心血管(CV)结果有关。PS 评分与生物标志物之间的关系也得到了类似的评估:结果:更大的 PS 负担与炎症生物标志物的逐渐增加有明显关联(中度 vs 低 PS 负担和高度 vs 低 PS 负担的 GMR [95% CI]):hs-CRP(1.09 [1.04-1.14]; 1.12 [1.06-1.17])、IL-6(1.05 [1.02-1.07]; 1.08 [1.05-1.11])、LpPLA2(1.01 [1.00 - 1.02]; 1.02 [1.01-1.04])和心脏生物标志物 hs-TnT (1.结论:结论:在慢性冠状动脉综合征患者中,较大的社会心理负担与炎症和心脏生物标志物水平的升高有关。虽然这项观察性研究并未确定这些关联的因果关系,但研究结果表明,炎症和心脏损伤/应激是将心理社会负担与冠心病风险升高联系起来的合理途径,这需要进一步探讨。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Associations between psychosocial burden and prognostic biomarkers in patients with chronic coronary syndrome: a STABILITY substudy.

Aim: To investigate associations between psychosocial burden and biomarkers reflecting pathophysiological pathways in patients with chronic coronary syndrome.

Methods: Psychosocial (PS) factors were collected from self-assessed questionnaires and biomarkers representing inflammation (high-sensitivity [hs]-C-reactive protein [CRP], interleukin-6 [IL-6], lipoprotein-associated phospholipase A2 [Lp-PLA2]) and cardiac injury/stress (hs-troponin T [hs-TnT], N-terminal pro-B type natriuretic peptide [NT-proBNP]) were measured in 12,492 patients with chronic coronary syndrome in the STABILITY trial. Associations between level of each psychosocial factor (never-rarely (reference), sometimes, often-always) and biomarkers were evaluated using linear models with adjusted geometric mean ratios (GMR). A score comprising four factors ('feeling down', 'loss of interest', financial stress', 'living alone') that previously demonstrated association with cardiovascular (CV) outcome was created, and categorized into three levels: low, moderate and high PS burden. Associations between PS score and biomarkers were evaluated similarly.

Results: Greater PS burden was significantly associated with a gradual increase in inflammatory biomarkers (GMR [95% CI] for moderate vs low PS burden; and high vs low PS burden): hs-CRP (1.09 [1.04-1.14]; 1.12 [1.06-1.17]), IL-6 (1.05 [1.02-1.07]; 1.08 [1.05-1.11]), LpPLA2 (1.01 [1.00 - 1.02]; 1.02 [1.01-1.04]) and cardiac biomarkers hs-TnT (1.03 [1.01-1.06]; 1.06 [1.03-1.09]) and NT-proBNP (1.09 [1.04-1.13]; 1.21 [1.15-1.27]).

Conclusions: In patients with chronic coronary syndrome, greater psychosocial burden was associated with increased levels of inflammatory and cardiac biomarkers. While this observational study does not establish causal nature of these associations, the findings suggest inflammation and cardiac injury/stress as plausible pathways linking psychosocial burden to an elevated CV risk, that needs to be further explored.

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来源期刊
European journal of preventive cardiology
European journal of preventive cardiology CARDIAC & CARDIOVASCULAR SYSTEMS-
CiteScore
12.50
自引率
12.00%
发文量
601
审稿时长
3-8 weeks
期刊介绍: European Journal of Preventive Cardiology (EJPC) is an official journal of the European Society of Cardiology (ESC) and the European Association of Preventive Cardiology (EAPC). The journal covers a wide range of scientific, clinical, and public health disciplines related to cardiovascular disease prevention, risk factor management, cardiovascular rehabilitation, population science and public health, and exercise physiology. The categories covered by the journal include classical risk factors and treatment, lifestyle risk factors, non-modifiable cardiovascular risk factors, cardiovascular conditions, concomitant pathological conditions, sport cardiology, diagnostic tests, care settings, epidemiology, pharmacology and pharmacotherapy, machine learning, and artificial intelligence.
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