Pushing the limits: exercise stress in the healthy human heart
Bibliographic record
Abstract
Humans have a remarkable capacity to adjust to acute physiological exercise stress. Increased cardiac contractility and cardiac output facilitate systemic increases in oxygen delivery and utilization to meet the increased metabolic demands of exercise. This process is made possible largely due to rapid increases in heart rate and stroke volume, both of which augment cardiac output and contribute to maximal cardiovascular capacity. Munch et al. (2014) recently implemented a novel right atrial pacing protocol during maximal exercise to explore the upper limits of cardiovascular function in highly trained males. This study evokes a few big questions in human exercise physiology: What cardiovascular factor limits our maximal exercise capacity – Is it heart rate or stroke volume? Is it possible to ‘push the limit’ in order to improve maximal exercise performance? These questions are hugely important to physiologists, clinicians, coaches and athletes alike. A recent prospective cross-sectional study by Munch et al. (2014) had 12 highly trained male cyclists perform maximal exercise protocols with and without an experimental condition involving right atrial pacing. The purpose of their study was to examine the effects of elevations in maximal heart rate on cardiopulmonary function and capacity. Direct catheterization-based measures of systemic and peripheral haemodynamics, as well as oxygen transport and consumption were obtained during two types of incremental exercise stress to exhaustion. These two exercise modalities are known to have divergent effects on circulation as a result of differences in muscle mass activation. Exercise stress was performed during an experimental condition (with atrial pacing increasing heart rate by 20 beats per minute at each workload) and a control condition (without atrial pacing) to examine whether either maximal heart rate or stroke volume limit cardiopulmonary capacity. More specifically, incremental cycling (i.e. large muscle mass) and knee extensor (i.e. small muscle mass) exercises were performed, with workload increased every 2 min to elicit 25, 49, 55, 70, 85 and 100% of the participants’ pre-determined maximal workload. Experimental and control data were compared at each workload. It was revealed that the myocardium can be paced to a higher heart rate than observed during maximal exercise without impairing cardiopulmonary performance. Their findings provide evidence that maximal heart rate and myocardial work capacity do not limit maximal exercise capacity in highly trained individuals. Instead, cardiac contractility and left ventricular filling (including transmural filling pressure) may reduce stroke volume and restrict cardiac output during maximal exercise with atrial pacing. Munch et al. (2014) utilized a novel approach to explore the effect of elevated heart rate on cardiovascular function during exercise. Their advanced protocol involved participants performing incremental cycling and knee extensor exercises while they had five different catheters inserted at various anatomical sites. This enabled the direct measurement and comparison of several systemic, upper body and lower body haemodynamic variables with and without right atrial pacing during exercise. These advanced study methods were unfortunately limited due to experimental complications and/or catheter displacement. Specifically, Munch et al. (2014) were unable to obtain cardiopulmonary (including cardiac output, right atrial pressure, pulmonary artery pressure, pulmonary capillary wedge pressure) and lower body haemodynamic measurements in one-third of participants during the cycling trials and one-half of participants during the knee extensor exercise trials. While these aforementioned limitations may decrease statistical power, their findings provide an interesting hypothesis to direct future studies. Several factors should be considered when interpreting the current study findings and designing future studies, including (i) exercise mode and (ii) cardiac structure and function. Exercise mode can evoke significant postural-induced haemodynamic changes. Munch et al. (2014) acquired direct measurements of acute exercise-induced haemodynamic changes in highly trained male cyclists on a cycle ergometer, to minimize upper body movements and improve haemodynamic monitoring. By contrast, whole-body treadmill exercise, for example, involves a more upright posture and is known to elicit a higher acute heart rate response relative to cycle ergometry. The Frank–Starling mechanism also predicts that a lower end-diastolic volume results in lower ventricular stretch and contractile force during atrial pacing. Taken together, these physiological responses to exercise demonstrate that exercise mode may influence cardiopulmonary capacity. Calbet et al. (2004) have demonstrated the feasibility of obtaining catheterization-based haemodynamic measurements during upright treadmill exercise to exhaustion. As a result, future studies are warranted to examine potential differences in the haemodynamic response to atrial pacing with changes in exercise modality and posture. While Munch et al. (2014) focused on highly trained male cyclists, they failed to characterize the training volume and underlying cardiac structure and function of participants. This is an important consideration because cardiac structure and function can influence the haemodynamic response to exercise and can be significantly modulated by exercise training history, sex and age. Cardiac remodelling, commonly known as the ‘Athletes Heart’, is associated with a higher resting stroke volume, as well as a greater increase in stroke volume with higher intensity exercise. Therefore, stroke volume plays a more active role in maintaining cardiac output during higher intensity exercise in endurance-trained athletes compared with sedentary individuals. Furthermore, exercise training mode can elicit profound differences within the ‘Athlete's Heart’, with cyclists exhibiting greater concentric remodelling and distance runners exhibiting greater eccentric remodelling (Fagard et al. 1984); this demonstrates that it is necessary to quantify cardiac structure and function. Sex differences in cardiovascular function and maximal exercise capacity have also been well documented. Male athletes typically exhibit greater atrial and ventricular remodelling, a higher blood pressure at rest and during exercise, a higher sympathetic tone, and a higher maximal exercise capacity relative to female athletes (Wilhelm et al. 2011). Furthermore, the interaction between endurance exercise training and age can also influence cardiac haemodynamics during exercise. Ageing is associated with a natural decline in cardiac and arterial compliance, and as a result the haemodynamic response to exercise may be attenuated with smaller changes in stroke volume for any given change in preload. Interestingly, Wainstein et al. (2012) have revealed sex differences in the dynamic response of the left ventricular chamber to changes in heart rate with a right atrial pacing model in aged humans. In particular, older women exhibited a greater inotropic response to incremental right atrial pacing, in conjunction with a greater decline in external stroke work. These findings predict limitations to left ventricular filling with increased heart rate in women relative to men. This limitation in left ventricular filling may be common to both female and aged myocardium, thereby altering cardiac output responses to maximal exercise. It has further been hypothesized that both structural and electrophysiological abnormalities may also develop in highly trained, middle-aged athletes as a result of adverse cardiac remodelling and haemodynamic changes (Wilhelm et al. 2011). As cardiac structure and function may be altered with both training history and ageing, it can be anticipated that left ventricular filling, filling pressures and cardiac output may be augmented; thus, sensitive measures of cardiac structure and function need to be considered. Notably, Shibata et al. (2008) were able to quantify the dynamics of the Frank–Starling mechanism. They captured the beat-to-beat modulation of stroke volume caused by beat-to-beat alterations in left ventricular filling using a novel method known as spectral transfer function. This method may have provided greater insight into the factors influencing maximal cardiovascular capacity in the current study, especially in determining the relationship between haemodynamics and cardiac stiffness. In conclusion, Munch et al. (2014) have provided a novel hypothesis that maximal exercise performance may be limited due to restrictions in left ventricular filling and not maximal heart rate. The direct measurement of key cardiopulmonary and haemodynamic variables is a definite strength; however, sample size was severely limited due to technical difficulties and precludes any significant conclusions. These novel findings need to be substantiated with larger studies in both highly trained and sedentary males and females across the lifespan. The characterization of cardiac structure and function, as well as training mode and volume, may be important factors modifying the haemodynamic response to exercise. None declared. None. None.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".