奥运运动员的心脏磁共振特征跟踪:心肌变形分析。

IF 7.5 2区 医学 Q1 CARDIAC & CARDIOVASCULAR SYSTEMS European journal of preventive cardiology Pub Date : 2025-02-06 DOI:10.1093/eurjpc/zwaf042
Sara Monosilio, Silvia Prosperi, Domenico Filomena, Erika Lemme, Giuseppe Di Gioia, Ruggiero Mango, Lucrezia Netti, Giovanni Tonti, Gianni Pedrizzetti, Gianfranco Gualdi, Maria Rosaria Squeo, Antonio Pelliccia, Viviana Maestrini
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引用次数: 0

摘要

背景:运动员心脏诱发极端的心血管重构,对早期心肌病的鉴别诊断提出了挑战。先进的心脏功能分析可能会有所帮助,但缺乏关于健康运动员和运动学科影响的数据。目的:用心脏磁共振(CMR)描述一组奥运会运动员的心肌变形,并评估基于运动学科和性别的可能差异。方法:一组心血管评估正常的奥林匹克运动员和一组年龄和性别匹配的久坐对照组进行CMR,不给药。通过专用的特征跟踪(FT)软件对电影图像进行后处理,以评估体积和功能,并评估双心室心肌变形参数,如左心室整体纵向和周向应变(LV-GLS和-GCS)和右心室GLS。运动员按ESC项目分类和性别进行分组。结果:300名奥林匹克运动员(13%技能,20%力量,25%混合,42%耐力,58%男性)和42名未经训练的对照组被招募。LV-GLS、-GCS和RV-GLS在不同运动类别的比较中无显著差异,除了耐力组的LV-GLS略低于技能组(p=0.045)。运动员的双心室射血分数略低(结论:我们的研究结果首次提供了一大批没有心血管异常的奥运会运动员的CMR-FT应变值,根据运动类型。耐力运动员的最低LV-GLS值与技能组和久坐组有显著差异。心肌变形参数在运动类别之间没有发现其他显著差异,但仅基于性别。
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Cardiac Magnetic Resonance Feature-Tracking in Olympic Athletes: a myocardial deformation analysis.

Background: Athlete's heart induces extreme cardiovascular remodelling, generating challenges for the differential diagnosis with early stages of cardiomyopathies. Advanced cardiac function analysis could be helpful, but data on healthy athletes and the impact of sports disciplines are lacking.

Aim: To describe myocardial deformation by cardiac magnetic resonance (CMR) in a cohort of Olympic athletes and to evaluate possible differences based on sports disciplines and sex.

Methods: A group of Olympic athletes with normal cardiovascular evaluation and a group of sedentary controls matched for age and sex underwent CMR without contrast administration. Cine-images were post-processed for volumes and function evaluation and to assess bi-ventricular myocardial deformation parameters, as left ventricular global longitudinal and circumferential strain (LV-GLS and -GCS) and right ventricular GLS, by a dedicated feature-tracking (FT) software. Athletes were divided according to ESC sports classification and sex.

Results: Three hundred Olympic athletes (13% skill, 20% power, 25% mixed, 42% endurance, 58% male) and 42 untrained controls were enrolled. No significant differences were found between LV-GLS, -GCS, and RV-GLS when comparing different sports categories, except for a slightly lower LV-GLS in the endurance group compared to the skill one (p=0.045). Athletes showed slightly lower biventricular ejection fraction (p<0.001) and LV-GCS (p<0.001) than sedentary controls, while only endurance athletes showed significant differences in LV- and RV-GLS versus the sedentary group (p=0.002 and p=0.001). Female athletes showed higher bi-ventricular GLS than males (p<0.001 for LV- and RV-GLS).

Conclusions: Our results provided for the first time CMR-FT strain values in a large cohort of Olympic athletes free of cardiovascular abnormalities, according to type of sports. Endurance athletes showed the lowest LV-GLS values being significantly different versus skill and sedentary controls. No other significant differences in myocardial deformation parameters between sports categories were found, but only based on sex.

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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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