We thank the authors for their commentary on our recent article “De-Stiffening the Aged Aorta with Regular Aerobic Exercise in Humans: Fact or Fallacy?” [1]. The authors highlight and underscore several important concepts in our review that are worth re-emphasizing. Many short-term aerobic exercise studies that demonstrate small beneficial de-stiffening effects on the aorta that occur concomitantly with small reductions in blood pressure, suggest load-dependent changes rather than structural alterations in the aorta. Typically, significant improvements in aortic stiffness independent of blood pressure are associated with many years of aerobic exercise, at least 4–75 days/week or more. This underscores the concept that while short-term aerobic exercise interventions have a plethora of cardiometabolic, anti-inflammatory, and antioxidant benefits that likely contribute to lower overall cardiovascular disease (CVD) risk [2], structural changes in aortic stiffness that are clinically relevant are likely to take much longer to manifest. The authors also mention the studies on high-intensity interval training (HIIT) that resulted in no change in carotid-femoral pulse wave velocity (CFPWV) despite physiologically and clinically meaningful changes in cardiorespiratory fitness (CRF), assessed as maximal exercise oxygen uptake [3]. While these results are surprising, the improvement in maximal exercise oxygen uptake cannot be ignored, given its well-known robust inverse association with CVD morbidity and mortality [4, 5]. Thus, while short-term HIIT may not alter aortic stiffness, the long-term benefits of HIIT may still be clinically important, because of changes in CRF and other cardiometabolic risk factors.
The authors also reiterate some of the other non-exercise modalities that have shown promise in altering aortic stiffness in aged adults. Some of the most promising non-exercise strategies, such as inspiratory muscle training and hot water immersion therapy, provide exercise-mimetic-like benefits and, therefore, will be of great benefit to non-ambulatory individuals with orthopedic, balance, or mobility limitations. As mentioned in our review [1], a few novel compounds have shown promise in humans of alleviating aortic stiffness, such as mitochondrial antioxidants [6] and non-acetylated salicylates that block pro-inflammatory signaling [7] and nicotinamide adenine dinucleotide (NAD+) boosters [8], but many compounds such as inorganic nitrites [9], curcumin [10], and the auto-phagy-promoting trehalose [11] that have demonstrated beneficial effects in rodent preclinical models have not translated to similar de-stiffening effects on the aorta in aged humans. Thus, these compounds still require further exploration in translation to older humans, and in combination with aerobic exercise.
Lastly, the authors mention wearable technologies and artificial intelligence (AI) tools that could be used to characterize vascular biomarkers such as CFPWV to provide real-time feedback to personalize exercise regimens. However, given that aortic stiffness takes months and years to change in response to habitual aerobic exercise and that small changes in CFPWV will likely be masked by day-to-day and beat-to-beat changes in blood pressure, it is not clear how knowing day-to-day variance in CFPWV would be beneficial. However, the ability for long-term monitoring of aortic stiffness during exercise or other prolonged interventions could be valuable in large multi-year longitudinal studies, where remote assessments of pulse waveforms in combination with AI tools to generate vascular biomarkers might be useful. For example, a recent study from the Framingham Heart Study demonstrated the ability to train a convolutional neural network to extract unbiased waveform features and predict CFPWV on tonometry-acquired brachial, radial, and carotid waveforms from participants in the Age, Gene/Environment Susceptibility (AGES)-Reykjavik and Risk Evaluation For Infarct Estimates (REFINE)-Reykjavik cohort studies [12]. Then, predicted CFPWVs were validated in a Framingham Heart Study cohort, and an AI-Vascular Age model was derived from any of the three waveforms that were used to predict incident CVD events adjusted for standard risk factors. Taken together, this AI-Vascular Age model could make measuring CFPWV in the clinic or in response to exercise interventions more feasible because the acquisition of only one waveform would allow for ease of long-term monitoring and more personalized management of aortic stiffness.
Conflict of Interest Statement
G.L.P. was a member of the journal’s Editorial Board at the time of submission. The author has no other conflicts of interest to declare.
Funding Sources
This study was not supported by any sponsor or funder. G.L.P. is also supported by the Russell B. Day and Florence D. Day Endowed Chair in Liberal Arts and Sciences at the University of Iowa.
Author Contributions
G.L.P. was responsible for the conception and design of the first draft and the development of the figures; edited and revised the manuscript; and approved the final version of the manuscript.
References
