Insights on blood pressure response to graded exercise in males and females across the age and fitness spectrum: reply
Bibliographic record
Abstract
We would like to thank Dr Chavez-Guevara for his interest in our recent publication in the European Journal of Preventive Cardiology.1 We are pleased that our approach of incorporating external workload (W) in the assessment of exercise blood pressure (BP), through the systolic blood pressure/W (SBP/W) slope, has sparked interest and stimulated further discussion. We also appreciate Dr Chavez-Guevara’s insightful suggestions for future analysis2 and hereby hope to respond to those we could address with our dataset. Our analysis shows individual variation in the strength of association between SBP and W, as indicated by differing R2 values. Dr Chavez-Guevara suggests this may indicate that the BP response to graded exercise is non-linear and may be better explained by a regression model with a curvilinear or quadratic term.2 As compared with the strong linear associations based on multiple BP and W measurements (R2 =0.85 ± 0.18), incorporating a quadratic term to assess curvilinearity, resulted in a similarly strong association and variability between SBP and W (R2 = 0.90 ± 0.14). As shown in Figure 1, the curvilinear relationship is near-linear, illustrating a progressive increase in SBP with exercise. We believe that a linear model is robust and easier for clinical application. Furthermore, one cannot discount that at least part of the variation in the R2 values may also be due to the inherent variability of non-invasive BP measurement during exercise.3 Therefore, continuous, invasive BP monitoring during exercise would be necessary to comprehensively assess and confirm this pattern. Relationship between systolic blood pressure and workload during exercise in a cohort of 589 individuals. The scatter plot displays individual systolic blood pressure measurements recorded at different exercise workloads across a cohort of 589 individuals. The dotted line illustrates the curvilinear regression line, representing the overall systolic blood pressure response to increasing workload for the entire cohort. The curvilinear model demonstrates a near-linear increase in systolic blood pressure with increasing workload. Dr Chavez-Guevara suggests that incorporating additional predictors such as the heart rate (HR) inflection or deflection point, HR to W (HR/W) slope, oxygen pulse, and double product may provide additional insights into the determinants of the SBP/W slope in our cohort.2 These are valuable suggestions. However, we believe that multicollinearity would exclude the HR/W slope from the model, as HR correlates strongly with both W (r = 0.84) and SBP (r = 0.66) during exercise (P < 0.001 for both). We agree that the double product could provide further understanding into the haemodynamic load during exercise. Indeed, athletes in particular show a marked increase in SBP during exercise, leading to a 4-fold increase in the double product between rest and peak exercise, as compared with a 2.6-fold increase in non-athletes (P < 0.001 for comparison between groups). Whereas the double product is primarily applied to assesses the cardiac W of the left ventricle, research has shown that the right ventricle experiences even greater relative increases in pressure and ventricular work.4 Taken together, this highlights how integrating exercise BP responses (systemic or pulmonary) alongside other metrics (such as HR and W) can provide unique insights into the demands and cardiovascular adaptations stimulated by endurance exercise. Dr Chavez-Guevara also raises the question whether excessive activation of the metaboreflex may be associated with an increased SBP/W slope.2 The physiologic mechanisms driving differences in the SBP/W slope are unclear and unfortunately could not be determined from our analysis. However, based on Poiseuille’s law, it likely reflects either an exaggerated increase in cardiac output for a given W, or an inability to sufficiently decrease total peripheral resistance to accommodate the increased cardiac output during exercise.5 Incorporating variables like oxygen uptake efficiency and markers of metabolic acidosis (VE/VO2-slope)—as well as the determinants of BP responses (cardiac output, total peripheral resistance)—could indeed deepen our understanding of the physiological mechanisms underpinning variations in the SBP/W slope, and is a good suggestion for future research directions. We thank Dr Chavez-Guevara for their insights and suggestions for further analysis related to our study. We are confident that continued investigation in these areas will provide a more holistic understanding of the mechanisms, determinants, and clinical relevance of assessing cardiovascular responses during exercise. K.J. is supported through an Australian Government Research Training Program Scholarship. The data underlying this article will be shared on reasonable request to the corresponding author.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.014 | 0.025 |
| Insufficient payload (model declined to judge) | 0.004 | 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".