Lipoprotein (a) and Incident Coronary Heart Disease in the Community: Impact of Traditional Cardiovascular Risk Factors.

IF 7.5 2区 医学 Q1 CARDIAC & CARDIOVASCULAR SYSTEMS
Natalie Arnold, Alina Goßling, Benjamin Bay, Jessica Weimann, Christopher Blaum, Fabian J Brunner, Marco M Ferrario, Paolo Brambilla, Giancarlo Cesana, Valerio Leoni, Luigi Palmieri, Chiara Donfrancesco, Teresa Padró, Jonas Andersson, Pekka Jousilahti, Francisco Ojeda, Tanja Zeller, Allan Linneberg, Stefan Söderberg, Licia Iacoviello, Francesco Gianfagna, Susana Sans, Giovanni Veronesi, Barbara Thorand, Annette Peters, Hugh Tunstall-Pedoe, Frank Kee, Veikko Salomaa, Renate B Schnabel, Kari Kuulasmaa, Stefan Blankenberg, Christoph Waldeyer, Wolfgang Koenig
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引用次数: 0

Abstract

Aims: Deleterious effects Lipoprotein (a) (Lp(a)) might be mitigated by overall cardiovascular (CV) risk reduction. However, data on the relationship between increased Lp(a) and incident coronary heart disease (CHD) according to the distribution of modifiable CV risk factors (CVRF) at baseline are still scarce. We investigated the association between high Lp(a) and incident CHD in the general population, depending on the presence/absence of four major CVRFs (hypertension, diabetes, hypercholesterolemia, smoking) at baseline.

Methods: Overall 66,495 CHD-free individuals from eight European prospective population-based cohorts were included. The cohort was stratified according to CVRF burden at baseline in "0/1 CVRF" (low risk; n= 41,770) and"≥2 CVRFs" (increased risk; n=24,725). Fine and Gray competing risk-adjusted models were calculated for the association between Lp(a) mass (<90th versus ≥90th percentile (pctl.); cut-off 43.2 mg/dL) and future CHD events.

Results: During a median follow-up of 9.7 years, 3,467 incident CHD events occurred. Despite being at very low absolute risk based on traditional CVRF, individuals with 0/1CVRF demonstrated a strong association between increased Lp(a) mass (≥90th pctl.) and future CHD events, which was comparable to the association observed among individuals with ≥2 CVRFs. The fully-adjusted sub-distribution Hazard Ratios [sHRs] for elevated Lp(a) were 1.38 (95% CI, 1.12-1.71) versus 1.27 (95% CI, 1.10-1.46) in those having 0/1 versus ≥2 CVRFs at baseline (Pinteraction0.50).

Conclusion: Among CHD-free subjects, high Lp(a) was related to adverse outcome even in individuals with no or only one CVRF at baseline, thereby generating substantial challenges in mitigating Lp(a)-associated CHD risk in very low risk populations.

脂蛋白(a)和社区冠心病事件:传统心血管危险因素的影响
目的:脂蛋白(a) (Lp(a))的有害作用可能通过降低整体心血管(CV)风险而减轻。然而,根据可改变的心血管危险因素(CVRF)的基线分布,关于Lp(a)升高与冠心病(CHD)发生率之间关系的数据仍然很少。我们根据基线时四种主要cvrf(高血压、糖尿病、高胆固醇血症、吸烟)的存在与否,调查了普通人群中高脂蛋白(a)与冠心病发生率之间的关系。方法:共纳入来自8个欧洲前瞻性人群队列的66,495名无冠心病患者。根据基线时CVRF负担分为“0/1 CVRF”(低风险;n= 41,770)和“≥2个cvrf”(风险增加;n = 24725)。Fine和Gray竞争风险调整模型计算Lp(a)质量之间的关联(结果:在中位9.7年的随访期间,发生了3,467例冠心病事件。尽管基于传统CVRF的绝对风险非常低,但0/1CVRF个体表现出Lp(a)质量增加(≥90 pctl)与未来冠心病事件之间的强烈关联,这与≥2 CVRF个体观察到的关联相当。基线时cvrf为0/1和≥2的患者,Lp(a)升高的完全调整后亚分布风险比为1.38 (95% CI, 1.12-1.71)和1.27 (95% CI, 1.10-1.46) (Pinteraction0.50)。结论:在无冠心病受试者中,即使在基线时没有或只有一个CVRF的个体中,高Lp(a)也与不良结局相关,因此在极低风险人群中,降低Lp(a)相关的冠心病风险存在重大挑战。
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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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