Review Reports

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The study by Baird et al. aims to address the gap in understanding complete cardiopulmonary function during exhaustive exercise, particularly in different age cohorts (6-11 and 11-16 years old) and the impact of caffeine on respiratory performance. Utilizing a comprehensive whole-body physiology model that integrates the nervous, cardiovascular, and respiratory systems, as well as oxygen transport, the researchers explore age-related differences in physiological responses to exercise. They report significant distinctions in respiratory, cardiovascular, and nervous system perturbations between the age groups. Additionally, caffeine is shown to have a statistically significant impact on respiratory function during simulated exercise. My comments are below.

If the major limitation in our understanding of cardiopulmonary function during moments of exhaustive exercise is truly due to invasive measurement techniques, this needs greater elaboration. Exercise physiologists routinely assess cardiopulmonary function, as maximal exercise is safe and encouraged in children. Moreover, if lack of access is the limiting factor, prediction equations, such as VO2max estimators, have been developed to assess cardiopulmonary function. It is this reviewer's belief that more is needed to convince the reader of the gap this model aims to fill. Please provide more details and justification regarding the stated limitations.

My major concern is that the relevance and benefit of this work are not clear. The ability to use the BioGears physiology engine with these age populations appears to be the clearest takeaway from the study, yet it is overshadowed by the finding that a model of caffeine intake affects physiology. To improve clarity, please emphasize the unique contributions of using the BioGears physiology engine and how it advances our understanding of cardiopulmonary function in children.

Additionally, 250 mg of caffeine is likely an unsafe level for children, especially those aged 6-11 years old. It is unclear why this dosage was used. Referencing studies with adults, where most show low to moderate amounts of caffeine improve physical performance, further drives the confusion as to why such a high dose of caffeine would be used in the model. I believe this paper would benefit from excluding the caffeine model and instead focusing on the application of the model and the potential relevance and practical benefit of a whole-body physiology model to investigate respiratory function. Please consider revising the study to exclude the caffeine model or justify the chosen dosage more clearly.

Author Response

comment 1: If the major limitation in our understanding of cardiopulmonary function during moments of exhaustive exercise is truly due to invasive measurement techniques, this needs greater elaboration. Exercise physiologists routinely assess cardiopulmonary function, as maximal exercise is safe and encouraged in children. Moreover, if lack of access is the limiting factor, prediction equations, such as VO2max estimators, have been developed to assess cardiopulmonary function. It is this reviewer's belief that more is needed to convince the reader of the gap this model aims to fill. Please provide more details and justification regarding the stated limitations.

Response: Added more details to the introduction to discuss the various reasons why this model might be important going forward. Although this study is small in scale, it is merely presenting the beginning framework for larger simulated studies using this model as a foundation.

 

Comment 2: My major concern is that the relevance and benefit of this work are not clear. The ability to use the BioGears physiology engine with these age populations appears to be the clearest takeaway from the study, yet it is overshadowed by the finding that a model of caffeine intake affects physiology. To improve clarity, please emphasize the unique contributions of using the BioGears physiology engine and how it advances our understanding of cardiopulmonary function in children.

Response: Updates to the discussion and introduction provide a more detailed perspective on the model and where it fits in the grander scheme of physiological modeling and needs in the community.

Comment 3: Additionally, 250 mg of caffeine is likely an unsafe level for children, especially those aged 6-11 years old. It is unclear why this dosage was used. Referencing studies with adults, where most show low to moderate amounts of caffeine improve physical performance, further drives the confusion as to why such a high dose of caffeine would be used in the model. I believe this paper would benefit from excluding the caffeine model and instead focusing on the application of the model and the potential relevance and practical benefit of a whole-body physiology model to investigate respiratory function. Please consider revising the study to exclude the caffeine model or justify the chosen dosage more clearly.

Response: The study has been revised to remove caffeine from the manuscript. Ultimately, this was an excellent discussion provided by the reviewer and (we agree) that the scope of the paper was better suited for just respiratory function over the age cohort.

Reviewer 2 Report

Comments and Suggestions for Authors

The mathematical model of the pulmonary function is important and interesting. As the authors state it is very important in education, in training and development of pharmacons, understanding pharmacokinetics.

After an interesting introduction the description of the model is very well written and organized. Since the model is tested on virtual patients, the description of their data would be very informative.  Although we understand the importance of different age groups, it would be also useful to know if these individuals were simple controls/test of the model or the high deviation of the measured individual data are the consequences of some type of pulmonary problems. Agreeing with the fact that BioGears in finding appropriate virtual individuals is crucial in this respect, it is important to state the physiologic patterns of these virtual patients. It is also important in the understanding of huge individual differences in the analyzed parameters. It is indeed very important and shows the relevance/applicability of the method gaining the statistically significant data of the 2 different age groups. Since they are two very different populations, measuring physiologic responses known from research data and textbooks, also proves the quality of the model. The younger patients are mostly prepubertal individuals, while the older population is definitely postpubertal, consequently physiological data, like tidal volume etc. have to be indeed different.

In terms of cardiovascular function there are rather strange differences between the younger and older age groups. For example the mean arterial pressure seems to be higher in the younger population. What is the reason for that?

Since there are many significant differences between the age groups, how is it possible that the effects of caffeine are tested in one group Caffeine versus No Caffeine? The two groups should be tested separately because of the mentioned physiological and metabolic reasons. . Although earlier in this manuscript, authors have shown significant differences between physiological data of the two age groups in the effects of caffeine they lump acquired data. Why? I think the reason for that should be discussed.

Although authors refer to many exercise research data including the dosage, applicability of caffeine, they also state that many different responses were measured in different training types, structures, ages. Knowing these it would be appropriate describing more details of the training used, analyzing acquired data in age groups. There are many suggestions against caffeine usage in childhood and the prepubertal groups is partly in the “hazard” population, consequently collected data are very important already in even medical circumstances.

The description of Figures is weak, readers would not understand the relevance of them without reading the whole manuscript. Many of the Figures are not cited in the text (like Fig4, Fig 7). In Fig 5 the older age group’s box plot shows a very diverse population. Although the number of subjects in the group is small, was that group tested for normality or in this case it is not relevant? (Again the description of patients would be important.)

The discussion should contain more details of the used model and its applicability, or its usage already. Describing the need for these models seems to be important, but a discussion based only on that scientifically is a little weak.

In line 271-272 the sentence is not understandable.

In line 81 the word element is misspelled.

Author Response

Reviewer 2:

Comment 1: Since the model is tested on virtual patients, the description of their data would be very informative.  Although we understand the importance of different age groups, it would be also useful to know if these individuals were simple controls/test of the model or the high deviation of the measured individual data are the consequences of some type of pulmonary problems. Agreeing with the fact that BioGears in finding appropriate virtual individuals is crucial in this respect, it is important to state the physiologic patterns of these virtual patients.

Response: A section was added in the methods that includes discussions on patient construction. This (we feel) greatly enhances the manuscript.

Comment 2: It is also important in the understanding of huge individual differences in the analyzed parameters. It is indeed very important and shows the relevance/applicability of the method gaining the statistically significant data of the 2 different age groups. Since they are two very different populations, measuring physiologic responses known from research data and textbooks, also proves the quality of the model. The younger patients are mostly prepubertal individuals, while the older population is definitely postpubertal, consequently physiological data, like tidal volume etc. have to be indeed different.

Response: This is an excellent comment! Indeed we did expect there to be differences in the populations from the beginning of this manuscript, the purpose of which was to highlight the overall model and show something interpretable between age groups. In addition, we have added a large section to the results that describes the differences in patient configuration due to the age parameter. This, I think, helps to explain why the models may be different between cohorts.

Comment 3: In terms of cardiovascular function there are rather strange differences between the younger and older age groups. For example the mean arterial pressure seems to be higher in the younger population. What is the reason for that?

Response: This is explained in more detail in the results. We note that variance, in general, in the older cohort is much high and we try and discuss this in more detail in the results.

Comment 4: Since there are many significant differences between the age groups, how is it possible that the effects of caffeine are tested in one group Caffeine versus No Caffeine? The two groups should be tested separately because of the mentioned physiological and metabolic reasons. . Although earlier in this manuscript, authors have shown significant differences between physiological data of the two age groups in the effects of caffeine they lump acquired data. Why? I think the reason for that should be discussed.

Response: Like another reviewer, the caffeine section of the manuscript was much too obfuscated and unclear. This section was removed and the introduction/discussion  re-written to only consider the age study and the impact of the model in this context.

Comment 5: Although authors refer to many exercise research data including the dosage, applicability of caffeine, they also state that many different responses were measured in different training types, structures, ages. Knowing these it would be appropriate describing more details of the training used, analyzing acquired data in age groups. There are many suggestions against caffeine usage in childhood and the prepubertal groups is partly in the “hazard” population, consequently collected data are very important already in even medical circumstances.

Response: This is an excellent comment and is a contributor towards why we decided to remove the caffeine section of the manuscript. We believe (like you mention here) that it deserves it’s own study and will be something we look into in future work

Comment 6: The description of Figures is weak, readers would not understand the relevance of them without reading the whole manuscript. Many of the Figures are not cited in the text (like Fig4, Fig 7). In Fig 5 the older age group’s box plot shows a very diverse population. Although the number of subjects in the group is small, was that group tested for normality or in this case it is not relevant? (Again the description of patients would be important.)

Response: Updates have been made to figure captions and more added to the results section to include references to all figures and provide a discussion surrounding the variance seen in the older age cohorts.

Comment 7: The discussion should contain more details of the used model and its applicability, or its usage already. Describing the need for these models seems to be important, but a discussion based only on that scientifically is a little weak.

Response: Added a much larger introduction to include some more discussion as to the applicability and importance of models like this, as well as a broader discussion as to what is being done. We also added some comments to the discussions

Comment 8: In line 271-272 the sentence is not understandable.

response: Updated

Comment 9: In line 81 the word element is misspelled.

Response: updated

 

Reviewer 3 Report

Comments and Suggestions for Authors

 

The manuscript presents a comprehensive modeling investigation of cardiopulmonary function and its perturbation during exercise comparing simulations for two age groups in children. Further a computational model is used to assess how caffeine impacts respiratory performance during exercise events. This research provides insights into how caffeine affects exercise and respiratory health, offering guidance for its use to enhance performance and well-being among youth.

The presented modeling approach is innovative and the presented results are novel and interesting. I recommend acceptance of the manuscript for publication after the authors address several minor points.

Point 1: It is unclear which significant test was performed to obtain the significant levels; the authors should introduce the statistical tests they performed in the Method section and should mention the specific statistical tests based on which the significance of the results is estimated. Notably, considering the older age group (11-16 years) has a larger variance (see Fig 5 and Fig 7). The authors should demonstrate that the used statistical test is robust in the presence of heteroscedasticity. If a t-test is used in Fig 5, Fig 7 and Fig 8, the authors should provide evidence for the Gaussian distribution of the measurements in each age group.

 

Point 2: Since the manuscript examines cardiopulmonary function during exercise and explores differences across two age groups, comparing the model’s output with actual physiological measurements in the discussion section would enhance the model’s reliability. Other researches have investigated the effects of aging on cardiac and respiratory dynamics individually as well as on cardio-respiratory coupling and phase synchronization as a function of age. . Although these earlier studies of the cardiac and respiratory response to modulation in sympathovagal balance were conducted during resting wake and sleep, the presented empirical findings—such as a decrease of nonlinearity in cardiac and respiratory dynamics as well as reduced cardiorespiratory coupling with aging—should be considered in the age-related computational model presented in the manuscript. A discussion should be provided to what extent the output of the presented computational model accounts for the empirical observations.

Point 3: The manuscript developed a mathematical model of the whole-body physiological response to exercise that included age-associated adjustments. The authors should note that there is a term in the literature, Network Physiology, to designate the field of research focusing on investigations of whole-body physiology and how integration across physiological systems as a network facilitates the emergence of basic physiological states such as wake/sleep, rest/exercise, etc. A reference and discussion should be provided in relation to the first works where Network Physiology was introduced In recent years there has been a growing body of work demonstrating a strong association between the hierarchical structure and dynamics of physiological networks and distinct physiological states, leading to the discovery of new laws of physiological regulation . It would be instructive for the authors to discuss the relation of their modeling approach to Network Physiology— a multidisciplinary field with a focus on whole-body research to understand the laws of cross-communication between physiological systems, and how systems and sub-systems in the human body integrate as a network to synchronize their dynamics and generate various states and functions at the organism level. This could provide valuable perspectives for future enhancements to the presented computational BioGears model and would strengthen the manuscript.

Point 4: The manuscript demonstrates age-related differences in physiological responses to exercise, affecting the respiratory, cardiovascular, and nervous systems by using computational simulations. Meanwhile, in the discussion section, the authors highlight “It is essential that future work highlight quantitative features that are validated on real human physiological data”. It is worth noting that within the field of Network Physiology.  For example, a new area of research has emerged, Network Physiology of Exercise, with focus on exercise, training and fatigue.   In fact, the Network Physiology framework has already been used to investigate cardio-muscular interactions in exercise and fatigue, cortico-muscular and inter-muscular interactions and their modulation with aging.  (See Garcia-Retortillo S et. al., Age-related breakdown in networks of inter-muscular coordination, GeroScience, 2024: 1-25).

 It appears from the current list of references in the manuscript that the authors are locked in their own work (a half dozen of cited articles relate to different variations of their modeling approach). The authors should do a proper literature search and place new findings within the background and context of prior advances, which I felt were missing in this manuscript. So, related earlier work and an already exciting framework on whole-body research by other scholars should be referred to integrate their work within current advances in the literature.

Given the manuscript's focus on whole-body modeling, 'Network Physiology' should be discussed and possibly listed as a keyword.

Author Response

Reviewer 3:

**Point 1**: It is unclear which significant test was performed to obtain the significant levels; the authors should introduce the statistical tests they performed in the Method section and should mention the specific statistical tests based on which the significance of the results is estimated. Notably, considering the older age group (11-16 years) has a larger variance (see Fig 5 and Fig 7). The authors should demonstrate that the used statistical test is robust in the presence of heteroscedasticity. If a t-test is used in Fig 5, Fig 7 and Fig 8, the authors should provide evidence for the Gaussian distribution of the measurements in each age group.

Response: An excellent catch! Indeed the variance in the older cohort is much larger than the younger cohort. We adjusted the t-statistic algorithm to use Welch’s t-test for two datasets with non-equal variance in order to avoid heteroscedasticity. This was not being done before and the graphs have been updated in response.

 

**Point 2**: Since the manuscript examines cardiopulmonary function during exercise and explores differences across two age groups, comparing the model’s output with actual physiological measurements in the discussion section would enhance the model’s reliability. Other researches have investigated the effects of aging on cardiac and respiratory dynamics individually as well as on cardio-respiratory coupling and phase synchronization as a function of age. . Although these earlier studies of the cardiac and respiratory response to modulation in sympathovagal balance were conducted during resting wake and sleep, the presented empirical findings—such as a decrease of nonlinearity in cardiac and respiratory dynamics as well as reduced cardiorespiratory coupling with aging—should be considered in the age-related computational model presented in the manuscript. A discussion should be provided to what extent the output of the presented computational model accounts for the empirical observations.

Response: The reviewer is correct there is no direct coupling in the cardiopulmonary systems as a function of age (directly) in the model. We have added an additional section in the methods that defines what, exactly, we are changing as a function of age in the models and believe the further coupling is outside the scope of this specific manuscript but will certainly be considered in a future work. Of particular note is the suggestion to look at sympathovagal balance and how modulation changes with age. We’ve also added an additional paragraph to the discussions as to how the model qualitatively compares to experimental work.

 

**Point 3**: The manuscript developed a mathematical model of the whole-body physiological response to exercise that included age-associated adjustments. The authors should note that there is a term in the literature, Network Physiology, to designate the field of research focusing on investigations of whole-body physiology and how integration across physiological systems as a network facilitates the emergence of basic physiological states such as wake/sleep, rest/exercise, etc. A reference and discussion should be provided in relation to the first works where Network Physiology was introduced In recent years there has been a growing body of work demonstrating a strong association between the hierarchical structure and dynamics of physiological networks and distinct physiological states, leading to the discovery of new laws of physiological regulation . It would be instructive for the authors to discuss the relation of their modeling approach to Network Physiology— a multidisciplinary field with a focus on whole-body research to understand the laws of cross-communication between physiological systems, and how systems and sub-systems in the human body integrate as a network to synchronize their dynamics and generate various states and functions at the organism level. This could provide valuable perspectives for future enhancements to the presented computational BioGears model and would strengthen the manuscript.

Response: A detailed description (with references) was added to the introduction to try and describe this new exciting field and how the model presented here may have relation to this area of research. A very exciting coupling between the two will certainly be an area of future work and we are very excited that the reviewer turned us onto this field of research!

**Point 4:** The manuscript demonstrates age-related differences in physiological responses to exercise, affecting the respiratory, cardiovascular, and nervous systems by using computational simulations. Meanwhile, in the discussion section, the authors highlight “_It is essential that future work highlight quantitative features that are validated on real human physiological data_”. It is worth noting that within the field of Network Physiology.  For example, a new area of research has emerged, Network Physiology of Exercise, with focus on exercise, training and fatigue.   In fact, the Network Physiology framework has already been used to investigate cardio-muscular interactions in exercise and fatigue, cortico-muscular and inter-muscular interactions and their modulation with aging.  (See Garcia-Retortillo S et. al., _Age-related breakdown in networks of inter-muscular coordination_, GeroScience, 2024: 1-25).

Response: We have added a detailed new introduction segment that highlights some of this work and how the current model relates to this exciting field. I think there is something exciting that could be done between the modeling framework of biogears and network physiology models and will be the focus of new research.

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

Authors sufficiently addressed my original comments and concerns. I have no additional notes. The manuscript contributes to the (limited) existing literature on the topic.