Left Ventricular Responses during Exercise in Highly Trained Youth Athletes: Echocardiographic Insights on Function and Adaptation.
Viswanath Balagopalan UnnithanAlexander J BeaumontThomas RowlandKeith GeorgeNicholas SculthorpeRachel N LordAndisheh BakhshiDavid Lee OxboroughPublished in: Journal of cardiovascular development and disease (2022)
There is an increase in the prevalence of elite youth sports academies, whose sole aim is to develop future elite athletes. This involves the exposure of the child and adolescent athlete to high-volume training during a period of volatile growth. The large amount of data in this area has been garnered from the resting echocardiographic left ventricular (LV) evaluation of the youth athlete; while this can provide some insight on the functional adaptations to training, it is unable to elucidate a comprehensive overview of the function of the youth athletes' LV during exercise. Consequently, there is a need to interrogate the LV responses in-exercise. This review outlines the feasibility and functional insight of capturing global indices of LV function (Stroke Index-SVIndex and Cardiac Index-QIndex), systolic and diastolic markers, and cardiac strain during submaximal and maximal exercise. Larger SVI and QI were noted in these highly trained young athletes compared to recreationally active peers during submaximal and maximal exercise. The mechanistic insights suggest that there are minimal functional systolic adaptions during exercise compared to their recreationally active peers. Diastolic function was superior during exercise in these young athletes, and this appears to be underpinned by enhanced determinants of pre-load.
Keyphrases
- left ventricular
- resistance training
- high intensity
- physical activity
- mental health
- hypertrophic cardiomyopathy
- heart failure
- body composition
- young adults
- blood pressure
- mitral valve
- acute myocardial infarction
- left atrial
- cardiac resynchronization therapy
- aortic stenosis
- heart rate
- pulmonary hypertension
- current status
- electronic health record
- coronary artery disease
- machine learning
- middle aged
- deep learning
- big data
- simultaneous determination