Big heart, no longer a big problem: using catecholamine‐based quantifications may be an effective means to prescribe exercise to non‐obstructive hypertrophic cardiomyopathy patients
Bibliographic record
Abstract
Exercise has been shown to have a beneficial impact on cardiovascular health and the prevention of chronic diseases. Although its benefits have been widely quantified, the current methods used to assess its role in patients with genetic cardiovascular diseases such as hypertrophic cardiomyopathy (HCM) have not been fully explored (Gersh et al. 2011). This can be attributed to HCM patients being susceptible to ventricular arrhythmias during high intensity exercise, due to increases in plasma catecholamine levels (Maron et al. 2015). Consequently, clinicians face challenges in prescribing an active lifestyle to such patients, who have conditions that may restrict their exercise capabilities. As such, HCM patients are recommended to adopt a relatively sedentary lifestyle, which is problematic because this is a risk factor for developing unrelated chronic diseases such as coronary artery disease, hyperlipidaemia and hypertension. Therefore, establishing a safe and effective means for HCM patients to receive the benefits of exercise while minimizing its risks remains a challenge. In a recent article in The Journal of Physiology, Shah et al. (2018) developed an exercise protocol to measure biochemical parameters in response to various intensities of exercise between sedentary male adults aged 18–45 in non-obstructive HCM patients and matched healthy controls. In their model, parameters such as ventilatory threshold (VT), catecholamine threshold (CT) and lactate threshold (LT) were used to quantify the physiological and metabolic changes seen with increasing levels of exercise intensity. Since both catecholamine and lactate plasma levels are known to increase in an exponential manner with incremental exercise (McMorris et al. 2000), measuring these parameters was important in determining the levels of exercise that maintain low plasma catecholamines in these patients. By identifying these thresholds during exercise, reported as a function of heart rate (HR), the aim is to eventually develop an exercise protocol that can be beneficial to HCM patients. The protocol used in the study consisted of two visits to quantify plasma catecholamine and lactate levels at various intensities of exercise. By measuring peak exercise and VT parameters such as oxygen consumption () using maximal effort cardiopulmonary exercise testing (MECPET) during visit 1, an individualized submaximal, incremental intensity test was created for visit 2. In this visit, participants would surpass their VT (i.e. time point during exercise when ventilation increases faster than ) while simultaneously plasma catecholamine and lactate levels were measured using intravenous blood sampling and capillary earlobe measurements, respectively. The authors were able to show that non-obstructive HCM patients resembled age-matched controls in CT, LT and VT. In both groups, catecholamines remained low during low to moderate intensity exercise and increased exponentially at higher intensities. Furthermore, it was shown that CT (i.e. exercise intensity at which catecholamines begin to spike in the blood) was reached at higher exercise intensities than were LT and VT. This result led the authors to suggest a potential role for measuring VT or LT as a surrogate for establishing maximal loads of exercise for HCM patients. This has important clinical implications in terms of combining the use of biochemical parameters and exercise testing to demonstrate appropriate bouts of exercise for HCM patients. This study evokes the need to re-evaluate clinical recommendations of a relatively sedentary lifestyle in HCM patients by demonstrating that certain exercise intensities (low to moderate intensity) maintain low plasma catecholamine levels. By measuring exercise parameters such as VT, LT, CT and HR, Shah et al. (2018) provide a framework for the form of testing that should be used to determine safe exercise tests. This is particularly relevant since previous studies have suggested endurance exercise has a protective role against ventricular arrhythmias (Billman, 2002). The present study builds on previous work that showed improved exercise capacity after 16 weeks of moderate exercise in HCM patients with no major adverse effects (Saberi et al. 2017). Although this previous study linked exercise intensity with HCM, the only parameter used to quantify its benefits was peak oxygen consumption (). Accordingly, it was necessary to measure the biochemical and physiological changes that exercise gives rise to and the impact of exercise dose (i.e. exercise intensity) on plasma catecholamine levels in these patients. By combining the use of MECPET and submaximal exercise testing, a comprehensive assessment of the biochemical kinetics was demonstrated in non-obstructive HCM patients. This allowed the authors to establish a heart rate zone during which the VT, LT and CT were reached to provide insight into optimum levels of exercise intensity that are beneficial for HCM patients. The protocol established by the authors can potentially be applied to other cardiovascular diseases in which exercise is restricted. Many of these potential diseases are genetic, such as catecholaminergic polymorphic ventricular tachycardia and arrhythmogenic right ventricular dysplasia. Like HCM, these diseases are associated with exercise-induced ventricular tachycardias triggered by an increase of catecholamine release (Maron et al. 2004). As a result, the use of various biochemical tests to establish physiological parameters such as CT and VT can be a useful aid for these patients, not only to monitor their catecholamine levels but also to develop an exercise routine. This pilot study pioneers an excellent method to characterize patients with cardiovascular diseases who are recommended a sedentary life. By measuring a subject's CT using submaximal exercise testing, an individualized range can be established for how much exercise is useful to the patient. The protocol used in this study has the potential for implementation in a wide spectrum of cardiovascular disease therapies to determine suitable levels of exercise that will enhance wellbeing and promote longevity. The pilot study of Shah et al. (2018) is not without its limitations, and there is a need to further explore the ideas presented in the article. This study strictly looked at patients with non-obstructive HCM, which is the milder form of HCM. Alternatively, obstructive HCM is a condition where blood is blocked from flowing out of the ventricles, thereby decreasing cardiac output. A previous study has shown that relative to non-obstructive HCM patients, patients with obstructive HCM had a worse exercise capacity and lower increment in peak exercise HR (Lu et al. 2018). It would be interesting to see if these patients’ CT arises earlier, which would mean that less exercise is needed to trigger a catecholamine spike. This would indicate that for a given work rate, elevated catecholamine levels can augment cardiac output due to these patients having a lower exercise tolerance. Although this is a pilot study, it is necessary to mention that the thresholds were only measured once in sedentary non-obstructive HCM patients. This is acceptable as a pilot study, but since aerobic exercise is known to cause the heart to remodel over time, the next step would be to repeat these biochemical tests throughout a longer duration exercise routine. For example, endurance athletes undergo eccentric heart remodelling characterized by larger ventricular volumes, modest wall thickening and reductions in resting heart rates (Shave et al. 2017). Consequently, consistent exercise alters biochemical parameters such as LT, VT and CT. To establish a clinically driven long-term exercise routine for HCM patients, it is important to determine how often these parameters should be measured and monitored. Additionally, the study states that there is a strong association between HR defining LT and VT for which the authors report thresholds as a function of HR. However, as mentioned above, exercise causes heart and autonomic remodelling such that there would be progressive changes in baseline HR and exercise threshold. This indicates that using HR to quantify and represent thresholds may not be a sensible means to report such values. Thus, there is a need to explore other non-invasive measures for quantifying the relevant biochemical and physiological parameters involved. Lastly, while the authors looked specifically at exercise intensity, further studies need to incorporate duration and frequency into this protocol. Experiments using submaximal testing designed to have subjects exercise for different durations at various intensity stages would be informative as they can help explain the relationship between duration and plasma catecholamine levels. In addition, there is a need to test if duration is a confounding factor during their step-wise, submaximal exercise training protocol. This can be accomplished by measuring O2 and blood plasma levels at various time points of the exercise testing to demonstrate that the onset of lactate and catecholamine spikes reported in their findings are strictly intensity dependent. Thus, further studies are required to determine the use of an individualized exercise plan in a clinical setting. In conclusion, Shah et al. (2018) have developed a novel model to assess the biochemical and physiological parameters that arise during exercise. This pilot study demonstrates that although high-intensity exercise triggers exponential catecholamine release, non-obstructive HCM patients can still gain the benefits of exercise at a low to moderate intensity. This is the first study to link these biochemical thresholds to HCM patients with the goal of deriving patients’ exercise capabilities, which allows them to attain the benefits of exercise while minimizing its risks. Therefore, this study is the first step in establishing an exercise model in HCM patients who have been previously restricted to a sedentary lifestyle. None declared. All authors have read and approved the final version of this manuscript and agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. All persons designated as authors qualify for authorship, and all those who qualify for authorship are listed. None declared.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".