A breath-by-breath and oscillation-by-oscillation analysis study: Holistic integrative interpretation of the respiratory-related circulatory parameter changes in OSA patients

A breath-by-breath and oscillation-by-oscillation analysis study: Holistic integrative interpretation of the respiratory-related circulatory parameter changes in OSA patients | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article A breath-by-breath and oscillation-by-oscillation analysis study: Holistic integrative interpretation of the respiratory-related circulatory parameter changes in OSA patients Yu-Die Liu, Jia-Hao Chen, Xing-Guo Sun, Meng-Jun Xiang, Zeng-Fei Zhang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9002435/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Background: The disease OSA is common. The mechanism by which breathing pattern induces changes in circulatory parameters is still unclear. Based on the theory of Holistic Integrative Physiology, Medicine, this study aims to explain how respiration affects circulatory parameters during sleep. Methods: Our study included 18 normal subjects and 20 OSA patients (Apnea-Hypopnea Index ≥15) underwent standardized cardiopulmonary exercise testing (CPET) and polysomnography (PSG). We analyzed heart rate variability (HRV), systolic/diastolic blood pressure variability (SBPV/DBPV) and their percentage parameters using dual analytical approaches: Breath-by-Breath and Oscillation-by-Oscillation. Results: We found significant changes in respiratory and circulatory parameters during OSA sleep compared to those during normal respiratory sleep. HRV and HRV% were significantly higher in OSA patients (P = 0.004, P = 0.015). Systolic blood pressure and diastolic blood pressure were also significantly elevated. Anoscillation-by-oscillation study showed that abnormal OSA breathing patterns caused greater changes in the patient's circulatory parameters. Conclusions: Our study has shown that abnormal respiratorypatterns during sleep in OSA patients can significantly increase the variability of circulatory parameters, revealing that OSA may interfere with breathing patterns, affect changes in circulatory parameters. Health sciences/Cardiology Health sciences/Diseases Health sciences/Health care Health sciences/Medical research Biological sciences/Physiology Holistic Integrative Physiology Medicine and Health (HIPM) respiratory heart rate variability (HRV) obstructive sleep apnea (OSA) blood pressure variability (BPV) Polysomnography (PSG) Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Many factors cause obstructive sleep apnea (OSA), which causes repeated sleep-related apnea and hypopnea. These interruptions can cause sleep disorders, hypercapnia, and other physiological and clinical changes. Epidemiological studies have shown that OSA is a prevalent illness, affecting 936 million individuals aged 30–69 years worldwide. This age group has an alarming 23.6% prevalence rate in China, with over 175 million people suffering. Over 65 million people have a sleep apnea-hypopnea index (AHI) greater than 15 times per hour, or an 8.8% prevalence. China has the most OSA patients worldwide, surpassing Europe, America, and developing countries, including the Asia-Pacific region and Africa[ 1 ]. According to this epidemiological survey, the incidence and number of OSA cases in China are increasing, indicating that OSA is a major public health issue[ 2 ]. Heart rate (HR), blood pressure (BP), heart rate variability (HRV), and blood pressure variability (BPV) fluctuate during nighttime apnea and hypopnea in OSA patients and are linked to the onset and progression of cardiovascular and other systemic diseases[ 3 ]. Higher AHI readings indicate a greater risk of cardiovascular and cerebrovascular events[ 4 , 5 ]. Numerous studies[ 6 – 8 ] have investigated OSA-induced circulatory changes, although the underlying mechanisms are still unclear[ 9 ]. The theory of Holistic Integrative Physiology, Medicine, and Health (HIPM) highlights the importance of coordinated and integrated control among the respiratory, circulatory, and metabolic systems to sustain the stability of life functions[ 10 ]. The theory emphasizes that the organs and systems of the human body are interdependent and cannot function independently. When in good health, these systems maintain a dynamic balance so that any changes in one system inevitably affect the activity of others. Our discovery prompted the development of an innovative concept called 'integrated regulation of respiration, circulation, and metabolism' [ 10 ]. A great example of this interaction can be observed in the impact of changes in respiratory frequency (Rf) on circulatory parameters, specifically in the heart period (HP) and BPV [ 11 – 15 ]. During inspiration, there is a decrease in HP, and during expiration, there is an increase in HP. At the same time, BP also changes in a similar pattern as HP due to the baroreflex, which demonstrates the complex relationship between both systems. The synchronization of heart rate with breathing is not only a reflection of this coordinated regulation but also plays a vital role as a predictive tool for assessing cardiovascular and cerebrovascular diseases[ 16 , 17 ]. BPV refers to the dynamic changes in blood pressure over time. It is strongly associated with the onset and progression of cardiovascular and cerebrovascular diseases [ 18 – 20 ]. Our proposition is that HRV and BPV are involved in coordinated control between respiration, circulation, and metabolism. This concept is consistent with the principles of HIPM. To investigate this, we focused on the correlation between the respiratory cycle and HRV and BPV at two crucial time periods: immediately after falling asleep and just prior to waking up. Our methodology utilized advanced monitoring tools to precisely assess respiratory patterns and heart rate variability, effectively capturing the dynamics of HRV and BPV in relation to breathing cycles. The objective of our study was to analyze this relationship from a novel theoretical viewpoint, which sets it apart from conventional perspectives by taking into account the interaction of different physiological systems as a whole entity. By investigating how these systems interact to influence HRV and BPV, we sought to provide new insights into the underlying mechanisms of this phenomenon. Methods In this study, 18 normal subjects and 20 patients diagnosed with OSA were assessed. Prior to the study, all participants underwent thorough assessments, which included cardiopulmonary exercise testing (CPET) to evaluate their cardiopulmonary performance and polysomnography (PSG) to analyze their sleep patterns, at Fuwai Hospital. The research protocol received approval from the Ethics Committee of Fuwai Hospital (Approval No.: 2023–2236) and was conducted in complete compliance with the Helsinki Declaration and local ethical requirements. The inclusion criteria for normal subjects were as follows: (1) Age over 18 years. (2) A PSG evaluation confirmed the absence of sleep apnea hypopnea syndrome, with an AHI less than 5 events per hour. (3) Continuous PSG monitoring was performed for a minimum of 7 hours each night. The inclusion criteria for patients with OSA[ 21 , 22 ]: (1) Age over 18 years. (2) PSG evaluation showing an AHI ≥ 15 events/hour. (3) Continuous PSG monitoring was performed for a minimum of 7 hours each night. (4) The presence of wave breathing during sleep. Exclusion criteria for all participants: (1) Presence of severe respiratory diseases (e.g., advanced COPD, cystic fibrosis). (2) Patients who are currently in the acute stage of cardiovascular or cerebrovascular disease. (3) Pregnancy. (4) Secondary hypertension due to conditions such as renal artery stenosis or endocrine disorders. (5) Lower limb dysfunction impacting daily activities or sleep quality. 2.2 CPET evaluation 2.2.1 CPET scheme The CPET is a tool frequently used in clinical practice for cardiopulmonary rehabilitation. It is an objective, quantitative, and noninvasive technique. It is recognized as the most reliable method for assessing cardiopulmonary capacity, offering a thorough understanding of how the circulatory, pulmonary, and muscular systems respond to exercise[ 23 ]. The CPET was performed with the Quark PFT Ergo system produced by COSMEDS.R.L. in Italy. To ensure the precision of airflow exchange data, the system underwent rigorous daily calibration using a metabolic simulator prior to being used for patients. Before starting the exercise test, the subjects underwent comprehensive static pulmonary function testing in a seated position. Following this, an electromagnetically braked cycle ergometer was used. The power was incrementally increased according to the standards set by the Harbor-UCLA Medical Center[ 23 , 24 ]. 2.2.2 CPET data analysis method All data for the measured parameters were initially obtained from the software of the Quark PFT Ergo CPET system by COSMED S.R.L., Italy. The raw data were segmented second by second using a breath-by-breath method[ 24 ]. Analysis was then conducted following standard calculation principles[ 23 ]. All CPET reports were initially interpreted by two highly trained clinicians and subsequently reviewed by an experienced CPET specialist to ensure quality control. 2.3 PSG monitoring and evaluation 2.3.1 PSG monitoring The SOMNOscreenTM plus RC polysomnography device, produced by SOMNOmedics in Germany, was used to observe several parameters, such as nasal airflow, heart rate, arterial pressure, photoplethysmography (PPG) finger pulse wave, electroencephalography (EEG), and electromyography (EMG). To adapt to the surroundings and equipment, it was recommended that all participants wear the PSG device in the ward during the daytime. The duration of wearing was for a continuous period of 48 hours, covering two complete days and nights. The first night was used to collect basic data on natural sleep, while the second night was specifically dedicated to a trial of continuous positive airway pressure (CPAP). Participants were given warnings to abstain from taking any medications that had an impact on sleep for a period of one week prior to the monitoring. In addition, the use of sedatives, coffee, certain beverages, and any food or medication that could affect sleep was strictly forbidden during the monitoring phase. Body movements during sleep might result in the detachment of sensors, while the impact of ambient light may cause artifacts in PPG data[ 25 ]. Data points collected during periods of sensor detachment or artifacts were removed from the statistical analysis. The nasal catheter respiratory airflow data were sampled at a rate of 128 Hz, the ECG R-R interval was sampled at a rate of 256 Hz, and the indirect BP measurement based on pulse wave transit time (PTT) was also sampled at a rate of 256 Hz. There is a nonlinear correlation between PTT and BP[ 26 ]. By using a mathematical model, it is possible to obtain precise measurements of BP without the need for a cuff[ 26 ]. Criteria for diagnosing sleep apnea[ 21 , 27 ]: Sleep Apnea: Defined as a noticeable cessation or significant weakening (> 90% reduction from baseline amplitude) of respiratory airflow through the nose and mouth during sleep, lasting for ≥ 10 seconds. Hypopnea: This condition is characterized by a > 30% reduction in nasal and oral airflow compared to baseline, accompanied by a decrease in SpO2 of ≥ 4%, with a duration of ≥ 10 seconds. Alternatively, a > 50% reduction in airflow with a SpO 2 decrease of ≥ 3% and lasting ≥ 10 seconds also qualifies as hypopnea. 2.3.2 PSG data calculation and analysis The sleep state of the subject was assessed by analyzing the EEG signals. During the REM sleep stage, EEG activity often displays rapid changes (20–50 Hz)[ 28 ]. DOMINO software (version 3.0), which is used together with PSG equipment, has the ability to automatically recognize the specific type of EEG activity, assess whether the subject is in a state of sleep, and identify sleep stages based on the standards outlined by the American Academy of Sleep Medicine (AASM)[ 29 ]. PSG monitoring data are segmented into sleep and awakening periods. All the raw data from the sleep periods are then exported from the software for the next step of analysis and processing. A nasal airflow velocity of 0 indicates a transition between the phases of respiration, while a value below 0 indicates inspiration, and a velocity above 0 indicates expiration. The nasal airflow velocity continuously decreases from 0 to a value below 0, followed by an increase to a value above 0 and then back to 0, thus ending the respiratory cycle. For more details, please refer to Fig. 1 . The HR is derived from the R-R interval on an ECG, which represents the period between consecutive R-peaks. Heart rate = 60000/R-R interval (ms). An HRV cycle refers to the fluctuation of heart rate as it increases from its minimum value to its maximum value and subsequently decreases back to its minimum value. The average amplitude of the HRV is determined by calculating the mean of the increasing and decreasing segments of the heart rate within one HRV cycle. HRV% is determined by dividing the HRV by the average HR during this HRV cycle. For detailed information, refer to Fig. 2 . Finger pulse wave data were used to calculate the number of BP variability cycles (BPV-n). The highest point of the main wave reflects SBP, while the lowest point reflects DBP. The pulse wave was sampled at a rate of 128 Hz. The values of the peak and trough of the main wave were extracted and sorted separately. SBP or DBP increases from its minimum value to its maximum value and then decreases from its maximum value back to the minimum value, creating a cycle of SBP variability (SBPV) or DBP variability (DBPV). SBPV-n is the total number of SBPV cycles that occur during sleep. The calculation method for the DBP variability cycle number (DBPV-n) is identical to that for SBPV-n. The mean amplitude of BP fluctuation (BPV-M) was calculated using pulse transit time (PTT). The SBPV amplitude (SBPV-M) and DBPV amplitude (DBPV-M) were determined by taking the average of the absolute values of the peak and lowest fluctuations during each SBPV and DBPV cycle, respectively. The percentage of SBPV-M (SBPV-M%) is the ratio of SBPV-M to the mean SBP in one cycle, calculated as SBPV-M/mean SBP \(\times\) 100%. Similarly, the percentage of DBPV-M (DBPV-M%) is the ratio of DBPV-M to the mean DBP in one cycle, calculated as DBPV-M/mean DBP \(\times\) 100%. Please refer to Fig. 3 A and 3 B for details. In addition, our research introduces the oscillation-by-oscillation calculation method for analyzing cycles during an OSA. This method is detailed as follows: An oscillatory cycle in respiration, also known as an OSA cycle, is characterized as follows: the nasal airflow velocity transitions from a shallow and slow pattern to a deep and fast pattern and then reverses back to a shallow and slow pattern. denoted as OSA-1, OSA-2, ..., up to OSA-n, while OSA-n denotes the total number of OSA cycles throughout an entire night's sleep. An HRV oscillation cycle, also known as an OSA-HRV cycle, is defined as follows. Similarly, for heart rate, an oscillation cycle comprises a gradual increase followed by a decrease in HR. These cycles are represented as OSA-HRV-1, OSA-HRV-2, ..., OSA-HRV-n. The term OSA-HRV-n indicates the total number of OSA-HRV cycles during the entire sleep period. A BPV oscillation cycle, also known as an OSA-BPV cycle, is characterized as follows: In the case of BP, each oscillation cycle involves a progressive rise followed by a subsequent decline, forming an OSA-BPV cycle. The cycles are denoted as OSA-BPV-1, OSA-BPV-2, ..., up to OSA-BPV-n, where OSA-BPV-n represents the total count of OSA-BPV cycles across the full sleep duration. For a detailed visual representation and understanding of the oscillation-by-oscillation method, please refer to Fig. 4 A- 4 D. 2.4 Statistical methods The data were processed and analyzed using SPSS software, version 26. The results are reported as the mean ± SD or number (%) for normally distributed and categorical variables or as the median (interquartile range) for nonnormally distributed variables unless otherwise indicated. Normally distributed continuous data were analyzed using unpaired or paired t tests. Nonparametric continuous data were tested with the Mann‒Whitney U test and Wilcoxon rank-sum test. Binomial data were analyzed using Fisher’s exact test. The significance level is set at 5%. Graphical representations were generated using Origin software. Results Analysis of participants’ demographics and CPET data This study included 18 normal subjects (NS) and 20 patients diagnosed with OSA. Table 1 presents comprehensive demographic data and essential CPET characteristics. The AHI of the OSA group was 25.55 (21.7-33.32), which was significantly greater than that of the NS group (3.36 ± 1.21), p < 0.001. Table 1 Demographics and CPET Data Age unit NS (n = 18) Patients (n = 20) P value yr 51.67 ± 13.53 57.65 ± 11.64 0.151 Male n (%) 10(56%) 11 (55%) 1.000 Height m 1.69 ± 0.06 1.69 ± 0.06 0.941 Weight kg 67.53 ± 11.61 74.50 ± 13.95 0.105 BMI kg/m 2 23.60 ± 3.56 25.92 ± 4.08 0.071 AHI events/hr 3.36 ± 1.21 25.55(21.7-33.32) <0.001 Peak \(\dot{\text{v}}\) O 2 %Pred 84.68 ± 16.64 80.17 ± 15.93 0.399 AT %Pred 77.65 ± 12.84 76.41(62.62–79.73) 0.516 work rate %Pred 90.77 ± 19.46 91.87 ± 19.92 0.865 OUEP %Pred 106.47 ± 9.96 112.48 ± 9.51 0.053 \(\text{L}\text{o}\text{w}\text{e}\text{s}\text{t}-\dot{\text{v}}\text{E}/\dot{\text{v}}\text{C}\text{O}\) 2 %Pred 106.76 ± 8.59 96.95 ± 6.24 <0.001 \(\dot{\text{v}}\) E/ \(\dot{\text{v}}\) CO 2 slope %Pred 106.83 ± 13.44 99.43 ± 11.06 0.071 The data are presented as the means ± SDs, medians (interquartile ranges), or numbers (frequencies). NS, normal subjects; BMI, body mass index; AHI, apnea-hypopnea index; AT, anaerobic threshold; lowest \(\dot{\text{v}}\) E/ \(\dot{\text{v}}\) CO 2 , lowest value of carbon dioxide ventilatory efficiency; OUEP, oxygen uptake efficiency plateau; peak \(\dot{\text{v}}\) O 2 , peak oxygen uptake; \(\dot{\text{v}}\) E/ \(\dot{\text{v}}\) CO 2 slope, slope of minute ventilation over carbon dioxide elimination; %Pred, percentage estimated value = measured value/predicted value×100%; W, watt. Analysis of participants’ respiration and circulatory parameters during normal breathing During regular breathing, the OSA group had a sleep duration of 5.07 ± 1.64, which was significantly shorter than that of the NS group (8.30 [7.29–8.63]), P< 0.001. The HRV % of the OSA group was 3.65 (2.69–5.18), which was significantly lower than that of the NS group, which was 4.91 (2.71–10.65), P = 0.044. See Table 2 for details. Table 2 Respiration and circulatory variability parameters during normal breathing Sleep duration unit NS (n = 18) Patients (n = 20) P value hr 8.30(7.29–8.63) 5.07 ± 1.64 <0.001 Br n/min 15.91 ± 2.36 14.65 ± 3.26 0.184 HR bpm 59.39 ± 6.89 62.13 ± 7.51 0.250 HRV bpm 2.90(1.85–5.38) 2.28(1.67–3.16) 0.194 HRV% % 4.91(2.71–10.65) 3.65(2.69–5.18) 0.044 B-n/HRV-n ratio 1.00(0.99–1.02) 0.99 ± 0.03 0.782 SBP mmHg 101.49 ± 14.81 101.65 ± 18.66 0.977 DBP mmHg 66.37 ± 10.20 71.10 ± 12.95 0.223 SBPV-M mmHg 2.10(1.69–3.30) 2.28(1.92–3.52) 0.174 DBPV-M mmHg 2.09(1.86–2.72) 2.32 ± 0.60 0.828 SBPV-M% % 2.00 ± 0.59 2.41 ± 0.82 0.107 DBPV-M% % 3.54 ± 0.92 3.37 ± 1.00 0.608 B-n/SBPV-n ratio 1.01 ± 0.04 0.98 ± 0.04 0.046 B-n/DBPV-n ratio 1.01 ± 0.04 0.98 ± 0.04 0.008 SBPV-n/DBPV-n ratio 1.00 ± 0.02 0.99 ± 0.04 0.522 The data are presented as the means ± SDs or medians (interquartile ranges). Br, respiratory rate; HR, heart rate; HRV, amplitude of respiratory heart rate variability; HRV%, percentage of HRV; B-n/HRV-n, number of respiratory cycle/number of respiratory heart rate variability cycles; SBP, systolic blood pressure; DBP, diastolic blood pressure; SBPV-M, mean value of systolic blood pressure variability amplitude; DBPV-M, mean value of diastolic blood pressure variability amplitude; SBPV-M%, percentage of SBPV-M; DBPV-M%, percentage of DBPV-M; B-n/SBPV-n, number of respiratory cycle/number of respiratory systolic blood pressure variability cycles; B-n/DBPV-n, number of respiratory cycle/number of respiratory diastolic blood pressure variability cycles; SBPV-n/DBPV-n, number of respiratory systolic blood pressure variability cycles/number of respiratory diastolic blood pressure variability cycles. Analysis of the respiration and circulatory parameters of the OSA group during the occurrence of OSA During OSA episodes, the respiratory and circulatory parameters of patients were significantly different from those during normal breathing. For more details, refer to Table 3 . Table 3 Respiration and circulatory variability parameters in patients with OSA during the period of OSA Sleep duration unit Patients (n = 20) P value hr 2.36(1.75–3.59) 0.005 Br n/min 17.01 ± 3.70 0.003 HR bpm 64.24 ± 7.09 0.070 HRV bpm 3.73 ± 1.73 0.004 HRV% % 5.85 ± 2.66 0.015 OSA-HRV bpm 12.91 ± 6.24 <0.001 OSA-HRV% % 20.46 ± 9.66 <0.001 B-n/HRV-n ratio 1.21 ± 0.14 <0.001 SBP mmHg 108.39 ± 20.99 0.010 DBP mmHg 73.66 ± 13.99 0.043 SBPV-M mmHg 3.83(2.97–4.95) <0.001 DBPV-M mmHg 3.76 ± 1.26 0.001 SBPV-M% % 14.60 ± 7.28 <0.001 DBPV-M% % 11.15 ± 4.17 <0.001 OSA-SBPV-M mmHg 17.99 ± 12.25 <0.001 OSA-DBPV-M mmHg 8.22 ± 3.32 <0.001 OSA- SBPV-M% % 11.15 ± 4.17 <0.001 OSA- DBPV-M% % 14.60 ± 7.28 <0.001 B-n/SBPV-n ratio 1.22 ± 0.17 <0.001 B-n/DBPV-n ratio 1.23 ± 0.17 <0.001 SBPV-n/DBPV-n ratio 1.01 ± 0.05 0.297 The data are presented as the means ± SDs or medians (interquartile ranges). Paired t tests or Wilcoxon rank-sum tests were used to compare the data collected during normal breathing between the OSA group and the control group. Br, respiratory rate; HR, heart rate; HRV, amplitude of respiratory heart rate variability; HRV%, percentage of HRV; B-n/HRV-n, number of respiratory cycle/number of respiratory heart rate variability cycles; OSA-HRV, amplitude of respiratory heart rate variability in one oscillation cycle; OSA-HRV%, percentage of OSA-HRV; SBP, systolic blood pressure; DBP, diastolic blood pressure; SBPV-M, mean value of systolic blood pressure variability amplitude; DBPV-M, mean value of diastolic blood pressure variability amplitude; SBPV-M%, percentage of SBPV-M; DBPV-M%, percentage of DBPV-M; OSA-DBPV-M, mean value of diastolic blood pressure variability amplitude in oscillation cycles; OSA-SBPV-M%, percentage of OSA-SBPV; OSA-DBPV-M%, percentage of OSA-DBPV-M; B-n/SBPV-n, number of respiratory cycle/number of respiratory systolic blood pressure variability cycles; B-n/DBPV-n, number of respiratory cycle/number of respiratory diastolic blood pressure variability cycles; SBPV-n/DBPV-n/DBPV-n, number of respiratory systolic blood pressure variability cycles Discussion In this study, respiratory parameters, HRV parameters and BPV-related parameters were analyzed. We found that all respiratory and circulatory parameters (including HRV and BPV-related parameters) increased significantly during OSA compared with those during normal respiration; in particular, oscillation-by-oscillation analysis revealed more significant changes in circulatory parameters. There are different respiratory and circulatory control mechanisms involved in normal respiration and OSA respiration in OSA patients. This differential regulation of OSA may contribute to the occurrence of cardiovascular and cerebrovascular events[ 30 , 31 ]. Respiratory sinus arrhythmia (RSA) was first identified by Professor Ludwig in 1847 and fully recorded[ 32 ]. Since its discovery, physicologists have made great efforts to investigate the mechanism of RSA and its physiological importance. Currently, it is suggested that RSA contributes to the enhancement of the ventilation-perfusion ratio, the reduction of physiological dead space volume, and the facilitation of lung gas exchange[ 33 , 34 ]. The process by which respiration affects circulatory system parameters, including changes in thoracic pressure, regulation by the vagus and sympathetic nerves, and reflexive control of the heart, is complicated. Typically, these changes allow the heart to sustain consistent performance under many physiological conditions. Respiration causes variations in thoracic pressure due to the action of the diaphragm and thoracic muscles, which in turn affects the pressure in the thoracic cavity. During the process of inspiration, the diaphragm undergoes a contraction in a downward direction, which leads to the expansion of the thoracic cavity and a decrease in pressure within the chest. During expiration, the diaphragm undergoes relaxation, causing contraction of the thoracic cavity and resulting in an increase in the intrathoracic pressure. Changes in thoracic pressure have a direct impact on the filling and ejection of the heart, which subsequently affects the body[ 35 , 36 ]. Respiration affects the activity of the vagus and sympathetic nerves by causing variations in thoracic pressure, which in turn leads to functional changes in the sympathetic and parasympathetic nervous systems. During the process of inspiration, the pressure within the chest cavity decreases, leading to a decrease in the excitability of the vagal nerve and an increase in the excitability of the sympathetic nerve. This eventually leads to an increase in heart rate. In contrast, during expiration, there is an increase in intrathoracic pressure, which results in opposite changes in vagal and sympathetic nerve activity, eventually leading to a decrease in HR[ 33 , 34 , 37 ]. Moreover, the heart has inherent reflex regulatory mechanisms. The heart receives changes in thoracic pressure via cardiac baroreceptors and pressure sensors. These receptors are responsible for sensing changes in blood pressure and volume. As a result, they control the heart rate and the strength of the heart's contractions to ensure the stability of the cardiovascular system[ 38 ]. The presence of RSA has nuanced and complicated impacts on BP. Typically, the sympathetic nervous system has a vasoconstrictive influence on blood vessels, resulting in an increase in BP. When changes in BP resulting from respiration combined with the regulation of the sympathetic nervous system might result in an elevation in the force of heart contractions and the amount of blood pumped by the heart, leading to an increase in BP. The impact is particularly noticeable during the inspiration phase of respiration, when there is an increase in sympathetic activity. Conversely, the vagus nerve system has a vasodilatory influence on blood vessels, resulting in a reduction in BP. When changes in HR due to breathing combined with the regulation of the vagus nerve system can result in a reduction in the ability of the heart to contract and pump blood, thereby leading to a decrease in BP. The impact is particularly pronounced during the expiration phase of respiration [ 39 ]. In this study, it was shown that during normal respiratory sleep, the B-n/HRV-n, B-n/SBPV-n, and B-n/DBPV-n ratios were approximately equal to 1. This suggests a strong relationship between the features of the respiratory and circulatory systems. These findings indicate that breathing patterns have a primary influence on changes in circulatory parameters during sleep. However, during OSA episodes, these levels exceeded 1. This difference suggests that some respiratory cycles during OSA fail to sufficiently induce changes in circulatory parameters. We noticed a considerable increase in the Bf value during episodes of OSA compared to normal respiration. Normal respiration was frequently interrupted and replaced by smaller and shorter respiratory events, as shown in Fig. 4 A. This occurrence can be explained by the approach mentioned above. During sleep, humans experience a decrease in electrical activity in the muscles of the upper airways. This, together with the backward tilt of the tongue and soft palate, can result in different levels of obstruction in the upper airway, leading to the development of OSA[ 4 ]. During OSA episodes, an obstruction of the airway causes an interruption of breathing, resulting in a reduction in oxygen levels in the blood and an increase in carbon dioxide levels. The peripheral and central chemoreceptors, specifically those located in the aortic body and medulla oblongata, detect these chemical changes and send signals to the brainstem. As a result, there is heightened sensitivity of the respiratory center, which triggers the activation of the sympathetic nervous system, resulting in deeper and faster respiration. However, as a result of the relaxation of the upper respiratory muscles and either partial or complete obstruction of the airway, the act of breathing creates greater negative pressure within the chest cavity. This leads to an increase in the resistance that the left ventricle must overcome to pump blood, which in turn increases the amount of oxygen needed by the heart muscle and reduces the amount of blood it can pump with each beat. As a result, this has an impact on several aspects of blood circulation, as indicated by circulatory parameters. Additionally, it causes minor variations in airflow, as illustrated in Fig. 4 A. The respiratory muscles located in the upper part of the body counteract airway obstruction by enhancing strength. This helps to maintain an open airway, enabling the entry of air into the lungs for the purpose of ventilation. Consequently, respiratory and circulatory parameters tend to return to normal. However, as the muscles become tired and then relax, the upper airway becomes blocked, resulting in repeated episodes of OSA and considerable changes in respiratory and circulatory parameters. Studies have shown that the resting HRV decreases as individuals age and when they suffer from disease[ 16 , 40 , 41 ], reflecting a diminished ability to cope with environmental stress. On the other hand, a larger resting HRV usually suggests a better level of physiological adaptability and health benefits[ 42 , 43 ]. Consequently, these patients have a reduced HRV during sleep, even if they do not have OSA, while maintaining a normal breathing rhythm. However, during periods of OSA, measures related to HRV increase. Although this may seem advantageous at first, it is crucial to acknowledge that the control of breathing in patients with OSA is fundamentally different from that in individuals without OSA during regular sleep. As a result, directly comparing HRV indications between the two groups can be problematic. The main reason for the increased HRV during OSA is the recurrent activation of the sympathetic nerves[ 44 ], which leads to frequent and significant changes in circulatory parameters, especially HR and BP. The variability in heart rate increases the risk of cardiovascular events, suggesting that the higher HRV reported during OSA episodes is indeed harmful to the body. Our research revealed that BPV in patients with OSA is notably greater during episodes of OSA than during normal respiratory sleep during the same night. This indicates a direct relationship between abnormal respiratory episodes that are typical of OSA and increased levels of BPV. Previous studies support the clinical importance of this finding[ 45 ]. For example, Gutteridge et al.[ 20 ] have suggested that increased BPV, specifically during nighttime SBPV, could cause a decrease in brain volume and shrinkage of the hippocampus. This, in turn, may result in cognitive deterioration during later stages of life. Parati et al.[ 46 ] This study highlights the close relationship between BPV and the risk, progression, and severity of organ damage in the heart and kidneys, linking BPV to an increased risk of cardiovascular events. Considering these findings, efficiently managing abnormal breathing patterns in patients with OSA is crucial for enhancing the quality of sleep and reducing the negative effects of increased nocturnal BPV on cardiovascular and cerebrovascular health. The HIPM theory strongly emphasizes the interconnections of human physiology, with a particular focus on the integrated connection between the respiratory and circulatory systems[ 10 ]. The levels of PaO 2 , PaCO 2 , and [H + ]a play a crucial role in regulating respiration and show periodic changes. The variations in arterial blood are more noticeable, suggesting ongoing gas exchange, and decrease to nearly zero in venous blood[ 47 , 48 ]. Furthermore, the wave signal increased during hyperventilation and decreased during hypopnea or apnea [ 49 ]. An increased or reduced wave signal has an impact on the autonomic nervous system's tension by activating peripheral chemoreceptors, which in turn affects the functioning of the circulatory system. Venous blood, including low-frequency signals, enters the right side of the heart, receives oxygenation in the pulmonary circulation, and is then transformed into oxygenated blood with high-frequency signals, which is then expelled by the left side of the heart[ 10 ]. The peripheral chemoreceptors located in the aortic arch and carotid body quickly sense changes in the breathing pattern and transmit this information to central structures to make instantaneous adjustments to the respiratory system. The concept of 'strong-strong' and 'weak-weak' regulation implies that high-amplitude wave signals elicit greater breath, while low-amplitude signals lead to lower breath. Nevertheless, the medulla oblongata's sluggish chemoreceptors are essential for maintaining respiratory stability because they detect alterations in blood signals and respond within approximately 30 seconds. The sensitivity of peripheral fast chemoreceptors is modulated based on these delayed signals, increasing sensitivity when the wave amplitude is low and decreasing it when it is high. The coordination between rapid and gradual responses guarantees the stability of the respiratory system by balancing immediate reactions with longer-term regulatory adjustments[ 10 ]. During sleep impacted by OSA, the narrowing or obstruction of the upper airway causes a decrease in the amount of oxygen reaching the small air sacs in the lungs called alveoli, resulting in a reduction in the amplitude of the wave-like signals. This decrease involves the control of fast-response chemoreceptors with low sensitivity, resulting in a reduction in the amplitude of respiration. After a delay of approximately 30 seconds, the chemoreceptors in the respiratory control center of the medulla oblongata detect a weakened signal and increase the sensitivity of fast-response chemoreceptors via neural pathways. As a consequence, there is a rise in the amplitude of subsequent breathing in patients with OSA, which leads to the observed respiratory pattern characterized by alternating strong and weak breaths. This pattern repeats every 40–60 seconds, as shown in Fig. 4 A. Moreover, medical disorders such as hyperlipidemia and hyperglycemia, which elevate blood viscosity and impede blood circulation, might lengthen the duration it takes for wave signals to reach both peripheral and central chemoreceptors. Changes in blood viscosity and flow rate can impact the transmission of signals to receptors, hence affecting the regulation of the respiratory cycle. Likewise, the influence of these wave signals on the tension of the autonomic nervous system reflects their impact on respiration. Signals with a small amplitude decrease tension in the autonomic nervous system, causing variations in HR and BP. Once the signal amplitude falls below a specific threshold, it ceases to induce substantial oscillations during high-pass and low-pass processing. Thus, in the context of OSA, the greater frequency of respiratory cycles relative to the variability in circulatory parameters leads to an elevated ratio of B-n to circulatory parameter cycles. Limitations This study had a small sample size, making interpretation difficult. A continuous 48-hour PSG monitoring period presented logistical and participation issues, restricting the number of participants. Thus, the small sample size may cause research findings to deviate, impacting the statistical robustness and generalizability of the findings. Conclusions When OSA patients have typical breathing patterns during sleep, respiratory and circulatory parameters are correlated, showing that respiratory activities affect circulatory function. When respiratory patterns vary, as they do during OSA episodes, circulatory parameters change, indicating that these changes are respiratory-related. Due to increased sympathetic nerve activity, the instantaneous heart rate and blood pressure increase during OSA onset. Declarations Funding declaration This work was supported by the National Key Research and Development Program of China (2022YFC2010003), National Key Research and Development Program of China (2022YFC2010000), National Key Research and Development Program of China (2022YFC3601000), National Key Research and Development Program of China (2020YFC2009006), and National Key Research and Development Program of China (2020YFC2009002). Author Contribution All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Yu-Die Liu, Jia-Hao Chen, Meng-Jun Xiang and Zeng-Fei Zhang. The first draft of the manuscript was written by Yu-Die Liu and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Data availability The datasets used during the present study are available from the corresponding author upon reasonable request. Additional Information Conflicts of Interest: All authors have no conflicts of interest to declare. Ethical Statement: The authors are 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. The Ethics Committee of Fuwai Hospital approved the research protocol (Approval No.: 2023-2236), which was carried out in accordance with the Helsinki Declaration. Informed consent was obtained from all participants. References Benjafield Av, A. et al. Estimation of the global prevalence and burden of obstructive sleep apnoea: a literature-based analysis. Lancet Respiratory Med. https://doi.org/10.1016/S2213-2600(19)30198-5 (2019). 7:. Hou, D. Q., Wang, X. F. & Yang, H. F. Clinical epidemiological investigation and analysis of related factors of obstructive sleep apnea-hypopnea syndrome. Med. Clin. Res. 23 , 297–299 (2006). Baranwal, N., Yu, P. K. & Siegel, N. S. 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Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 04 Apr, 2026 Editor invited by journal 09 Mar, 2026 Editor assigned by journal 02 Mar, 2026 Submission checks completed at journal 02 Mar, 2026 First submitted to journal 01 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9002435","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":619178372,"identity":"e6b46055-8c51-4269-a1f5-cde4404352d1","order_by":0,"name":"Yu-Die Liu","email":"","orcid":"","institution":"The Affiliated Rehabilitation Hospital of Chongqing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yu-Die","middleName":"","lastName":"Liu","suffix":""},{"id":619178373,"identity":"1c2bb1c5-12d9-45d4-9814-a897af7d355f","order_by":1,"name":"Jia-Hao Chen","email":"","orcid":"","institution":"The Affiliated Rehabilitation Hospital of Chongqing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jia-Hao","middleName":"","lastName":"Chen","suffix":""},{"id":619178374,"identity":"e994b7e0-8628-4159-a31a-41d466bd2f6a","order_by":2,"name":"Xing-Guo Sun","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAz0lEQVRIiWNgGAWjYNCCCgsGA2YQg41oLWckSNXC2AbUwkCsFoPzy59JF86TkDdnZz664UMZg5x5/wL8WiRnvDGTnrlNwnBnM1vazRnnGIxlbjzAr4Vf4gybNO82CcYNh3nMbvO2MSTOkDiAXwubxPFn0rxzJOzBWv4So4Wfv8FMmrdBIhGshRGkhb+BkF94jK15jkkkg/3Sc07CWEICvw5giB1/eJunxsZ2O//hYzd+lNnISfATcBiDRAIqF0MEE2CaSdCWUTAKRsEoGGkAAC3qPuznqKs1AAAAAElFTkSuQmCC","orcid":"","institution":"Fuwai Hospital","correspondingAuthor":true,"prefix":"","firstName":"Xing-Guo","middleName":"","lastName":"Sun","suffix":""},{"id":619178375,"identity":"fdec4af2-e367-4d59-8757-1e4b4a727465","order_by":3,"name":"Meng-Jun Xiang","email":"","orcid":"","institution":"The Affiliated Rehabilitation Hospital of Chongqing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Meng-Jun","middleName":"","lastName":"Xiang","suffix":""},{"id":619178376,"identity":"f10c7d2e-b6ef-4842-afd8-f775507246c9","order_by":4,"name":"Zeng-Fei Zhang","email":"","orcid":"","institution":"The Affiliated Rehabilitation Hospital of Chongqing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zeng-Fei","middleName":"","lastName":"Zhang","suffix":""},{"id":619178377,"identity":"b30c103c-2cfb-4985-83e5-4cbc3265b30d","order_by":5,"name":"You-Hong Xie","email":"","orcid":"","institution":"The Affiliated Rehabilitation Hospital of Chongqing Medical University","correspondingAuthor":false,"prefix":"","firstName":"You-Hong","middleName":"","lastName":"Xie","suffix":""}],"badges":[],"createdAt":"2026-03-01 15:23:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9002435/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9002435/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106835709,"identity":"0142e0a8-b1c9-4b4a-8c21-0359875cd750","added_by":"auto","created_at":"2026-04-14 02:02:17","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":152324,"visible":true,"origin":"","legend":"\u003cp\u003eMethod for calculatingrespiratory cycles\u003c/p\u003e\n\u003cp\u003eIn respiratory physiology, each respiratory cycle comprises two key processes: inspiration and expiration. The cycle is measured in terms of airflow rate, which indicates the velocity of air moving in and out of the lungs. A pivotal point in this cycle is when the airflow rate is 0, marking the transition or 'breathing switch' between inspiration and expiration. During inspiration, the airflow velocity begins at 0,increases as air is drawn into the lungs, and then gradually slows to 0, signifying the end of the inspiration phase. Conversely, during expiration, the airflow velocity again starts at0 and increases as the lungs contract to expel air, reaching a maximum rate before decelerating back to 0, thus completing the expiration phase and the entire breathing cycle. In the context of sleep studies, we quantify these respiratory cycles using the notation B-1, B-2, … B-n, where each notation represents an individual respiratory cycle. The term B-n denotes the total number of respiratory cycles recorded during a sleep period.\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9002435/v1/4e6be872ea55e30ca5d402ab.jpg"},{"id":106835710,"identity":"61bdf907-ac0a-4b55-ba8c-210152364c22","added_by":"auto","created_at":"2026-04-14 02:02:17","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":79817,"visible":true,"origin":"","legend":"\u003cp\u003eCalculation methods for the HRV and HRV%\u003c/p\u003e\n\u003cp\u003eIn our study, we defined two key metrics for analyzing HRV: HRV-a and HRV-d. HRV-a is the absolute value of the difference between the minimum and maximum heart rates observed during the heart rate's increasing phase in the respiratory cycle. Conversely, HRV-d refers to the absolute value of the difference between the maximum and minimum heart rates during the decreasing heart rate phase.\u003c/p\u003e\n\u003cp\u003eOne complete HRV cycle, denoted as HRV-n, involves the heart rate rising from its lowest point (captured in HRV-a) to its highest point and then descending back from this peak to the lowest point (captured in HRV-d). Throughout the sleep period, these HRV cycles are tracked and quantified as HRV-1, HRV-2, …, HRV-n, with HRV-n representing the total number of HRV cycles during sleep. To calculate the value of a single HRV cycle, we used the following formula: one HRV= (HRV-a + HRV-d)/2.\u003c/p\u003e\n\u003cp\u003eFurthermore, we also calculated the HRV% for each cycle. HRV% is the ratio of the HRV value for a specific cycle to the average heart rate of that same cycle. For example, for the first HRV cycle, HRV%-1 was calculated as the HRV value of HRV-1 divided by the average heart rate during HRV-1.\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9002435/v1/85dfbc673380f89cedd082af.jpg"},{"id":106960824,"identity":"a37e6f86-3504-40dd-a467-997d845e5087","added_by":"auto","created_at":"2026-04-15 09:23:17","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":365431,"visible":true,"origin":"","legend":"\u003cp\u003eCalculation method forSBPV\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 3A \u003c/strong\u003eillustrates the methodology for calculating the mean amplitude of systolic blood pressure variability (SBPV-M). A cycle of SBPV-M is defined as systolic blood pressure (SBP) increasing from its lowest to highest point and then declining back to the baseline, denoted as SBPV-M1 to SBPV-Mn, where n represents the total number of SBPV cycles overnight. Each cycle includes two key parameters: the ascending phase (SBPV-M-a) and the descending phase (SBPV-M-d). The SBPV-M for one cycle is calculated as one SBPV-M = (SBPV-M-a + SBPV-M-d)/2. The mean SBPV-M for the entire night is obtained by averaging values across all cycles. The percentage change in SBPV-M (SBPV-M%) is computed by dividing the cycle-specific SBPV-M by the corresponding mean SBP and multiplying by 100%, representing the relative fluctuation of SBPV-M during the same period. Analysis of these cycles enables evaluation of systolic blood pressure fluctuations in OSA patients during sleep and offers valuable information about their nocturnal circulatory patterns.\u003c/p\u003e\n\u003cp\u003eThe methodology for calculating mean diastolic blood pressure variability (DBPV-M) and its percentage change (DBPV-M%) follows the same principles as SBPV-M.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 3B \u003c/strong\u003eillustrates the method for counting blood pressure variability cycles (BPV-n) using photovolumetric pulse wave data. The peak of the main pulse wave corresponds to SBP, and the trough represents DBP. Peak and trough values are extracted and sequenced for analysis. A complete SBPV cycle is defined as SBP rising from a minimum to a maximum and then returning to a minimum. DBPV cycles follow the same pattern for DBP. These cycles are labeled SBPV-1 to SBPV-n, where SBPV-n indicates the total number of systolic blood pressure variability cycles during sleep. The method for counting diastolic blood pressure variability cycles (DBPV-n) is identical to that for SBPV-n, tracking the rise and fall of DBP throughout sleep.\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9002435/v1/2885bee88c515e5dee88fd53.jpg"},{"id":106835712,"identity":"6c666444-83b2-4bce-934d-265a40d7f457","added_by":"auto","created_at":"2026-04-14 02:02:17","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":475802,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelations between respiration and circulatory parameter variability in patients with OSA\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 4A \u003c/strong\u003eshows a schematic representation of nasal airflow velocity during OSA episodes in a patient. It depicts four complete OSA cycles, each characterized by initially rapid airflow that gradually slows down. Across these cycles, a total of 56 respiratory cycles are presented, illustrating the dynamic airflow velocity changes typical of OSA episodes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 4B\u003c/strong\u003e shows HRV during OSA. It highlights significant heart rate fluctuations that closely correlate with nasal airflow velocity changes in OSA patients. Four major heart rate fluctuations corresponding to OSA cycles are displayed, covering 48 complete HRV cycles. Each OSA‑HRV cycle shows a gradual heart rate increase followed by a decrease, forming a fluctuation cycle (OSA‑HRV‑n). Within each cycle, heart rate rises from a minimum to a maximum (HRV‑a) and then declines to a minimum (HRV‑d). The mean HRV amplitude in one OSA cycle was calculated as OSA‑HRV = (HRV‑a + HRV‑d)/2; for instance, OSA‑HRV‑1 = (HRV‑a1 + HRV‑d1)/2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 4C\u003c/strong\u003e shows blood pressure variability during OSA episodes. Consistent with nasal airflow and HRV patterns, blood pressure displays marked fluctuations in response to OSA. The figure illustrates four OSA cycles leading to four prominent blood pressure changes. Similar to OSA‑HRV, each OSA‑BPV cycle consists of a blood pressure increase followed by a decrease. The mean BPV amplitude in an OSA cycle is calculated as OSA‑BPV = (BPV‑a + BPV‑d)/2, such as OSA‑SBPV‑1 = (SBPV‑a1 + SBPV‑d1)/2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 4D \u003c/strong\u003eillustrates photovolumetric pulse wave variability during OSA episodes. These pulse wave fluctuations show a clear correlation with the respiratory cycle. The figure presents three complete pulse wave fluctuation cycles, including 51 SBPV cycles and 51 DBPV cycles. A key finding is an approximately 30‑second delay between pulse wave changes and nasal airflow variations. This time lag may arise mainly from two factors: a relatively low sampling rate and the application of a moving average algorithm during data analysis.\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9002435/v1/4a1b4bc6e1c0e44fafd095d2.jpg"},{"id":107704896,"identity":"54f04948-d971-4295-9d2c-88a7ce13af3f","added_by":"auto","created_at":"2026-04-24 09:03:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1560720,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9002435/v1/ce18c718-8ac8-4753-917c-4d10c23cacd3.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A breath-by-breath and oscillation-by-oscillation analysis study: Holistic integrative interpretation of the respiratory-related circulatory parameter changes in OSA patients","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMany factors cause obstructive sleep apnea (OSA), which causes repeated sleep-related apnea and hypopnea. These interruptions can cause sleep disorders, hypercapnia, and other physiological and clinical changes. Epidemiological studies have shown that OSA is a prevalent illness, affecting 936\u0026nbsp;million individuals aged 30\u0026ndash;69 years worldwide. This age group has an alarming 23.6% prevalence rate in China, with over 175\u0026nbsp;million people suffering. Over 65\u0026nbsp;million people have a sleep apnea-hypopnea index (AHI) greater than 15 times per hour, or an 8.8% prevalence. China has the most OSA patients worldwide, surpassing Europe, America, and developing countries, including the Asia-Pacific region and Africa[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. According to this epidemiological survey, the incidence and number of OSA cases in China are increasing, indicating that OSA is a major public health issue[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Heart rate (HR), blood pressure (BP), heart rate variability (HRV), and blood pressure variability (BPV) fluctuate during nighttime apnea and hypopnea in OSA patients and are linked to the onset and progression of cardiovascular and other systemic diseases[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Higher AHI readings indicate a greater risk of cardiovascular and cerebrovascular events[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Numerous studies[\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] have investigated OSA-induced circulatory changes, although the underlying mechanisms are still unclear[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe theory of Holistic Integrative Physiology, Medicine, and Health (HIPM) highlights the importance of coordinated and integrated control among the respiratory, circulatory, and metabolic systems to sustain the stability of life functions[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The theory emphasizes that the organs and systems of the human body are interdependent and cannot function independently. When in good health, these systems maintain a dynamic balance so that any changes in one system inevitably affect the activity of others. Our discovery prompted the development of an innovative concept called 'integrated regulation of respiration, circulation, and metabolism' [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. A great example of this interaction can be observed in the impact of changes in respiratory frequency (Rf) on circulatory parameters, specifically in the heart period (HP) and BPV [\u003cspan additionalcitationids=\"CR12 CR13 CR14\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. During inspiration, there is a decrease in HP, and during expiration, there is an increase in HP. At the same time, BP also changes in a similar pattern as HP due to the baroreflex, which demonstrates the complex relationship between both systems. The synchronization of heart rate with breathing is not only a reflection of this coordinated regulation but also plays a vital role as a predictive tool for assessing cardiovascular and cerebrovascular diseases[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. BPV refers to the dynamic changes in blood pressure over time. It is strongly associated with the onset and progression of cardiovascular and cerebrovascular diseases [\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur proposition is that HRV and BPV are involved in coordinated control between respiration, circulation, and metabolism. This concept is consistent with the principles of HIPM. To investigate this, we focused on the correlation between the respiratory cycle and HRV and BPV at two crucial time periods: immediately after falling asleep and just prior to waking up. Our methodology utilized advanced monitoring tools to precisely assess respiratory patterns and heart rate variability, effectively capturing the dynamics of HRV and BPV in relation to breathing cycles. The objective of our study was to analyze this relationship from a novel theoretical viewpoint, which sets it apart from conventional perspectives by taking into account the interaction of different physiological systems as a whole entity. By investigating how these systems interact to influence HRV and BPV, we sought to provide new insights into the underlying mechanisms of this phenomenon.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eIn this study, 18 normal subjects and 20 patients diagnosed with OSA were assessed. Prior to the study, all participants underwent thorough assessments, which included cardiopulmonary exercise testing (CPET) to evaluate their cardiopulmonary performance and polysomnography (PSG) to analyze their sleep patterns, at Fuwai Hospital. The research protocol received approval from the Ethics Committee of Fuwai Hospital (Approval No.: 2023\u0026ndash;2236) and was conducted in complete compliance with the Helsinki Declaration and local ethical requirements.\u003c/p\u003e \u003cp\u003eThe inclusion criteria for normal subjects were as follows:\u003c/p\u003e \u003cp\u003e(1) Age over 18 years.\u003c/p\u003e \u003cp\u003e(2) A PSG evaluation confirmed the absence of sleep apnea hypopnea syndrome, with an AHI less than 5 events per hour.\u003c/p\u003e \u003cp\u003e(3) Continuous PSG monitoring was performed for a minimum of 7 hours each night.\u003c/p\u003e \u003cp\u003eThe inclusion criteria for patients with OSA[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]:\u003c/p\u003e \u003cp\u003e(1) Age over 18 years.\u003c/p\u003e \u003cp\u003e(2) PSG evaluation showing an AHI\u0026thinsp;\u0026ge;\u0026thinsp;15 events/hour.\u003c/p\u003e \u003cp\u003e(3) Continuous PSG monitoring was performed for a minimum of 7 hours each night.\u003c/p\u003e \u003cp\u003e(4) The presence of wave breathing during sleep.\u003c/p\u003e \u003cp\u003eExclusion criteria for all participants:\u003c/p\u003e \u003cp\u003e(1) Presence of severe respiratory diseases (e.g., advanced COPD, cystic fibrosis).\u003c/p\u003e \u003cp\u003e(2) Patients who are currently in the acute stage of cardiovascular or cerebrovascular disease.\u003c/p\u003e \u003cp\u003e(3) Pregnancy.\u003c/p\u003e \u003cp\u003e(4) Secondary hypertension due to conditions such as renal artery stenosis or endocrine disorders.\u003c/p\u003e \u003cp\u003e(5) Lower limb dysfunction impacting daily activities or sleep quality.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.2 CPET evaluation\u003c/h2\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1 CPET scheme\u003c/h2\u003e \u003cp\u003eThe CPET is a tool frequently used in clinical practice for cardiopulmonary rehabilitation. It is an objective, quantitative, and noninvasive technique. It is recognized as the most reliable method for assessing cardiopulmonary capacity, offering a thorough understanding of how the circulatory, pulmonary, and muscular systems respond to exercise[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The CPET was performed with the Quark PFT Ergo system produced by COSMEDS.R.L. in Italy. To ensure the precision of airflow exchange data, the system underwent rigorous daily calibration using a metabolic simulator prior to being used for patients. Before starting the exercise test, the subjects underwent comprehensive static pulmonary function testing in a seated position. Following this, an electromagnetically braked cycle ergometer was used. The power was incrementally increased according to the standards set by the Harbor-UCLA Medical Center[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2 CPET data analysis method\u003c/h2\u003e \u003cp\u003eAll data for the measured parameters were initially obtained from the software of the Quark PFT Ergo CPET system by COSMED S.R.L., Italy. The raw data were segmented second by second using a breath-by-breath method[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Analysis was then conducted following standard calculation principles[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. All CPET reports were initially interpreted by two highly trained clinicians and subsequently reviewed by an experienced CPET specialist to ensure quality control.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.3 PSG monitoring and evaluation\u003c/h2\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.3.1 PSG monitoring\u003c/h2\u003e \u003cp\u003eThe SOMNOscreenTM plus RC polysomnography device, produced by SOMNOmedics in Germany, was used to observe several parameters, such as nasal airflow, heart rate, arterial pressure, photoplethysmography (PPG) finger pulse wave, electroencephalography (EEG), and electromyography (EMG). To adapt to the surroundings and equipment, it was recommended that all participants wear the PSG device in the ward during the daytime. The duration of wearing was for a continuous period of 48 hours, covering two complete days and nights. The first night was used to collect basic data on natural sleep, while the second night was specifically dedicated to a trial of continuous positive airway pressure (CPAP). Participants were given warnings to abstain from taking any medications that had an impact on sleep for a period of one week prior to the monitoring. In addition, the use of sedatives, coffee, certain beverages, and any food or medication that could affect sleep was strictly forbidden during the monitoring phase. Body movements during sleep might result in the detachment of sensors, while the impact of ambient light may cause artifacts in PPG data[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Data points collected during periods of sensor detachment or artifacts were removed from the statistical analysis.\u003c/p\u003e \u003cp\u003eThe nasal catheter respiratory airflow data were sampled at a rate of 128 Hz, the ECG R-R interval was sampled at a rate of 256 Hz, and the indirect BP measurement based on pulse wave transit time (PTT) was also sampled at a rate of 256 Hz. There is a nonlinear correlation between PTT and BP[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. By using a mathematical model, it is possible to obtain precise measurements of BP without the need for a cuff[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCriteria for diagnosing sleep apnea[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]:\u003c/p\u003e \u003cp\u003eSleep Apnea: Defined as a noticeable cessation or significant weakening (\u0026gt;\u0026thinsp;90% reduction from baseline amplitude) of respiratory airflow through the nose and mouth during sleep, lasting for \u0026ge;\u0026thinsp;10 seconds.\u003c/p\u003e \u003cp\u003eHypopnea: This condition is characterized by a\u0026thinsp;\u0026gt;\u0026thinsp;30% reduction in nasal and oral airflow compared to baseline, accompanied by a decrease in SpO2 of \u0026ge;\u0026thinsp;4%, with a duration of \u0026ge;\u0026thinsp;10 seconds. Alternatively, a\u0026thinsp;\u0026gt;\u0026thinsp;50% reduction in airflow with a SpO\u003csub\u003e2\u003c/sub\u003e decrease of \u0026ge;\u0026thinsp;3% and lasting\u0026thinsp;\u0026ge;\u0026thinsp;10 seconds also qualifies as hypopnea.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.3.2 PSG data calculation and analysis\u003c/h2\u003e \u003cp\u003eThe sleep state of the subject was assessed by analyzing the EEG signals. During the REM sleep stage, EEG activity often displays rapid changes (20\u0026ndash;50 Hz)[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. DOMINO software (version 3.0), which is used together with PSG equipment, has the ability to automatically recognize the specific type of EEG activity, assess whether the subject is in a state of sleep, and identify sleep stages based on the standards outlined by the American Academy of Sleep Medicine (AASM)[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. PSG monitoring data are segmented into sleep and awakening periods. All the raw data from the sleep periods are then exported from the software for the next step of analysis and processing.\u003c/p\u003e \u003cp\u003eA nasal airflow velocity of 0 indicates a transition between the phases of respiration, while a value below 0 indicates inspiration, and a velocity above 0 indicates expiration. The nasal airflow velocity continuously decreases from 0 to a value below 0, followed by an increase to a value above 0 and then back to 0, thus ending the respiratory cycle. For more details, please refer to Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eThe HR is derived from the R-R interval on an ECG, which represents the period between consecutive R-peaks. Heart rate\u0026thinsp;=\u0026thinsp;60000/R-R interval (ms).\u003c/p\u003e \u003cp\u003eAn HRV cycle refers to the fluctuation of heart rate as it increases from its minimum value to its maximum value and subsequently decreases back to its minimum value. The average amplitude of the HRV is determined by calculating the mean of the increasing and decreasing segments of the heart rate within one HRV cycle. HRV% is determined by dividing the HRV by the average HR during this HRV cycle. For detailed information, refer to Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eFinger pulse wave data were used to calculate the number of BP variability cycles (BPV-n). The highest point of the main wave reflects SBP, while the lowest point reflects DBP. The pulse wave was sampled at a rate of 128 Hz. The values of the peak and trough of the main wave were extracted and sorted separately. SBP or DBP increases from its minimum value to its maximum value and then decreases from its maximum value back to the minimum value, creating a cycle of SBP variability (SBPV) or DBP variability (DBPV). SBPV-n is the total number of SBPV cycles that occur during sleep. The calculation method for the DBP variability cycle number (DBPV-n) is identical to that for SBPV-n. The mean amplitude of BP fluctuation (BPV-M) was calculated using pulse transit time (PTT). The SBPV amplitude (SBPV-M) and DBPV amplitude (DBPV-M) were determined by taking the average of the absolute values of the peak and lowest fluctuations during each SBPV and DBPV cycle, respectively. The percentage of SBPV-M (SBPV-M%) is the ratio of SBPV-M to the mean SBP in one cycle, calculated as SBPV-M/mean SBP\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\times\\)\u003c/span\u003e\u003c/span\u003e100%. Similarly, the percentage of DBPV-M (DBPV-M%) is the ratio of DBPV-M to the mean DBP in one cycle, calculated as DBPV-M/mean DBP\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\times\\)\u003c/span\u003e\u003c/span\u003e100%. Please refer to Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003eA and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003eB for details.\u003c/p\u003e \u003cp\u003eIn addition, our research introduces the oscillation-by-oscillation calculation method for analyzing cycles during an OSA. This method is detailed as follows:\u003c/p\u003e \u003cp\u003eAn oscillatory cycle in respiration, also known as an OSA cycle, is characterized as follows: the nasal airflow velocity transitions from a shallow and slow pattern to a deep and fast pattern and then reverses back to a shallow and slow pattern. denoted as OSA-1, OSA-2, ..., up to OSA-n, while OSA-n denotes the total number of OSA cycles throughout an entire night's sleep.\u003c/p\u003e \u003cp\u003eAn HRV oscillation cycle, also known as an OSA-HRV cycle, is defined as follows. Similarly, for heart rate, an oscillation cycle comprises a gradual increase followed by a decrease in HR. These cycles are represented as OSA-HRV-1, OSA-HRV-2, ..., OSA-HRV-n. The term OSA-HRV-n indicates the total number of OSA-HRV cycles during the entire sleep period.\u003c/p\u003e \u003cp\u003eA BPV oscillation cycle, also known as an OSA-BPV cycle, is characterized as follows: In the case of BP, each oscillation cycle involves a progressive rise followed by a subsequent decline, forming an OSA-BPV cycle. The cycles are denoted as OSA-BPV-1, OSA-BPV-2, ..., up to OSA-BPV-n, where OSA-BPV-n represents the total count of OSA-BPV cycles across the full sleep duration.\u003c/p\u003e \u003cp\u003eFor a detailed visual representation and understanding of the oscillation-by-oscillation method, please refer to Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e4\u003c/span\u003eA-\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e4\u003c/span\u003eD.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Statistical methods\u003c/h2\u003e \u003cp\u003eThe data were processed and analyzed using SPSS software, version 26. The results are reported as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD or number (%) for normally distributed and categorical variables or as the median (interquartile range) for nonnormally distributed variables unless otherwise indicated. Normally distributed continuous data were analyzed using unpaired or paired t tests. Nonparametric continuous data were tested with the Mann‒Whitney U test and Wilcoxon rank-sum test. Binomial data were analyzed using Fisher\u0026rsquo;s exact test. The significance level is set at 5%. Graphical representations were generated using Origin software.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cb\u003eAnalysis of participants\u0026rsquo; demographics and CPET data\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThis study included 18 normal subjects (NS) and 20 patients diagnosed with OSA. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents comprehensive demographic data and essential CPET characteristics. The AHI of the OSA group was 25.55 (21.7-33.32), which was significantly greater than that of the NS group (3.36\u0026thinsp;\u0026plusmn;\u0026thinsp;1.21), p \u0026lt; 0.001.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographics and CPET Data\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eunit\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;18)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePatients\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;20)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eyr\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51.67\u0026thinsp;\u0026plusmn;\u0026thinsp;13.53\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57.65\u0026thinsp;\u0026plusmn;\u0026thinsp;11.64\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.151\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10(56%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11 (55%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003em\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.69\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.69\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.941\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ekg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67.53\u0026thinsp;\u0026plusmn;\u0026thinsp;11.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e74.50\u0026thinsp;\u0026plusmn;\u0026thinsp;13.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.105\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ekg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.60\u0026thinsp;\u0026plusmn;\u0026thinsp;3.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.92\u0026thinsp;\u0026plusmn;\u0026thinsp;4.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.071\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAHI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eevents/hr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.36\u0026thinsp;\u0026plusmn;\u0026thinsp;1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.55(21.7-33.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeak \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\dot{\\text{v}}\\)\u003c/span\u003e\u003c/span\u003eO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e%Pred\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84.68\u0026thinsp;\u0026plusmn;\u0026thinsp;16.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e80.17\u0026thinsp;\u0026plusmn;\u0026thinsp;15.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.399\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e%Pred\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77.65\u0026thinsp;\u0026plusmn;\u0026thinsp;12.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e76.41(62.62\u0026ndash;79.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.516\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ework rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e%Pred\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90.77\u0026thinsp;\u0026plusmn;\u0026thinsp;19.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e91.87\u0026thinsp;\u0026plusmn;\u0026thinsp;19.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.865\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOUEP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e%Pred\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e106.47\u0026thinsp;\u0026plusmn;\u0026thinsp;9.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e112.48\u0026thinsp;\u0026plusmn;\u0026thinsp;9.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\text{L}\\text{o}\\text{w}\\text{e}\\text{s}\\text{t}-\\dot{\\text{v}}\\text{E}/\\dot{\\text{v}}\\text{C}\\text{O}\\)\u003c/span\u003e\u003c/span\u003e\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e%Pred\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e106.76\u0026thinsp;\u0026plusmn;\u0026thinsp;8.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e96.95\u0026thinsp;\u0026plusmn;\u0026thinsp;6.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\dot{\\text{v}}\\)\u003c/span\u003e\u003c/span\u003eE/\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\dot{\\text{v}}\\)\u003c/span\u003e\u003c/span\u003eCO\u003csub\u003e2\u003c/sub\u003e slope\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e%Pred\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e106.83\u0026thinsp;\u0026plusmn;\u0026thinsp;13.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e99.43\u0026thinsp;\u0026plusmn;\u0026thinsp;11.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.071\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eThe data are presented as the means\u0026thinsp;\u0026plusmn;\u0026thinsp;SDs, medians (interquartile ranges), or numbers (frequencies). NS, normal subjects; BMI, body mass index; AHI, apnea-hypopnea index; AT, anaerobic threshold; lowest \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\dot{\\text{v}}\\)\u003c/span\u003e\u003c/span\u003eE/\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\dot{\\text{v}}\\)\u003c/span\u003e\u003c/span\u003eCO\u003csub\u003e2\u003c/sub\u003e, lowest value of carbon dioxide ventilatory efficiency; OUEP, oxygen uptake efficiency plateau; peak \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\dot{\\text{v}}\\)\u003c/span\u003e\u003c/span\u003eO\u003csub\u003e2\u003c/sub\u003e, peak oxygen uptake; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\dot{\\text{v}}\\)\u003c/span\u003e\u003c/span\u003eE/\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\dot{\\text{v}}\\)\u003c/span\u003e\u003c/span\u003eCO\u003csub\u003e2\u003c/sub\u003e slope, slope of minute ventilation over carbon dioxide elimination; %Pred, percentage estimated value\u0026thinsp;=\u0026thinsp;measured value/predicted value\u0026times;100%; W, watt.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eAnalysis of participants’ respiration and circulatory parameters during normal breathing\u003c/h3\u003e\n\u003cp\u003eDuring regular breathing, the OSA group had a sleep duration of 5.07\u0026thinsp;\u0026plusmn;\u0026thinsp;1.64, which was significantly shorter than that of the NS group (8.30 [7.29\u0026ndash;8.63]), P\u0026lt; 0.001. The HRV % of the OSA group was 3.65 (2.69\u0026ndash;5.18), which was significantly lower than that of the NS group, which was 4.91 (2.71\u0026ndash;10.65), P\u0026thinsp;=\u0026thinsp;0.044. See Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e for details.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRespiration and circulatory variability parameters during normal breathing\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSleep duration\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eunit\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;18)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePatients\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;20)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehr\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.30(7.29\u0026ndash;8.63)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.07\u0026thinsp;\u0026plusmn;\u0026thinsp;1.64\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003en/min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.91\u0026thinsp;\u0026plusmn;\u0026thinsp;2.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.65\u0026thinsp;\u0026plusmn;\u0026thinsp;3.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.184\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ebpm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59.39\u0026thinsp;\u0026plusmn;\u0026thinsp;6.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e62.13\u0026thinsp;\u0026plusmn;\u0026thinsp;7.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.250\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHRV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ebpm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.90(1.85\u0026ndash;5.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.28(1.67\u0026ndash;3.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.194\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHRV%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.91(2.71\u0026ndash;10.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.65(2.69\u0026ndash;5.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB-n/HRV-n\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00(0.99\u0026ndash;1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.99\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.782\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e101.49\u0026thinsp;\u0026plusmn;\u0026thinsp;14.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e101.65\u0026thinsp;\u0026plusmn;\u0026thinsp;18.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.977\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66.37\u0026thinsp;\u0026plusmn;\u0026thinsp;10.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e71.10\u0026thinsp;\u0026plusmn;\u0026thinsp;12.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.223\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBPV-M\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.10(1.69\u0026ndash;3.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.28(1.92\u0026ndash;3.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.174\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBPV-M\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.09(1.86\u0026ndash;2.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.828\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBPV-M%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.41\u0026thinsp;\u0026plusmn;\u0026thinsp;0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.107\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBPV-M%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.54\u0026thinsp;\u0026plusmn;\u0026thinsp;0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.37\u0026thinsp;\u0026plusmn;\u0026thinsp;1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.608\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB-n/SBPV-n\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.98\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB-n/DBPV-n\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.98\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBPV-n/DBPV-n\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.99\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.522\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eThe data are presented as the means\u0026thinsp;\u0026plusmn;\u0026thinsp;SDs or medians (interquartile ranges). Br, respiratory rate; HR, heart rate; HRV, amplitude of respiratory heart rate variability; HRV%, percentage of HRV; B-n/HRV-n, number of respiratory cycle/number of respiratory heart rate variability cycles; SBP, systolic blood pressure; DBP, diastolic blood pressure; SBPV-M, mean value of systolic blood pressure variability amplitude; DBPV-M, mean value of diastolic blood pressure variability amplitude; SBPV-M%, percentage of SBPV-M; DBPV-M%, percentage of DBPV-M; B-n/SBPV-n, number of respiratory cycle/number of respiratory systolic blood pressure variability cycles; B-n/DBPV-n, number of respiratory cycle/number of respiratory diastolic blood pressure variability cycles; SBPV-n/DBPV-n, number of respiratory systolic blood pressure variability cycles/number of respiratory diastolic blood pressure variability cycles.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eAnalysis of the respiration and circulatory parameters of the OSA group during the occurrence of OSA\u003c/b\u003e \u003c/p\u003e \u003cp\u003eDuring OSA episodes, the respiratory and circulatory parameters of patients were significantly different from those during normal breathing. For more details, refer to Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRespiration and circulatory variability parameters in patients with OSA during the period of OSA\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSleep duration\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eunit\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePatients\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;20)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehr\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.36(1.75\u0026ndash;3.59)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003en/min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.01\u0026thinsp;\u0026plusmn;\u0026thinsp;3.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ebpm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64.24\u0026thinsp;\u0026plusmn;\u0026thinsp;7.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.070\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHRV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ebpm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.73\u0026thinsp;\u0026plusmn;\u0026thinsp;1.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHRV%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.85\u0026thinsp;\u0026plusmn;\u0026thinsp;2.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOSA-HRV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ebpm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.91\u0026thinsp;\u0026plusmn;\u0026thinsp;6.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOSA-HRV%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.46\u0026thinsp;\u0026plusmn;\u0026thinsp;9.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB-n/HRV-n\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e108.39\u0026thinsp;\u0026plusmn;\u0026thinsp;20.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73.66\u0026thinsp;\u0026plusmn;\u0026thinsp;13.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBPV-M\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.83(2.97\u0026ndash;4.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBPV-M\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.76\u0026thinsp;\u0026plusmn;\u0026thinsp;1.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBPV-M%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.60\u0026thinsp;\u0026plusmn;\u0026thinsp;7.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBPV-M%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.15\u0026thinsp;\u0026plusmn;\u0026thinsp;4.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOSA-SBPV-M\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.99\u0026thinsp;\u0026plusmn;\u0026thinsp;12.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOSA-DBPV-M\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.22\u0026thinsp;\u0026plusmn;\u0026thinsp;3.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOSA- SBPV-M%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.15\u0026thinsp;\u0026plusmn;\u0026thinsp;4.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOSA- DBPV-M%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.60\u0026thinsp;\u0026plusmn;\u0026thinsp;7.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB-n/SBPV-n\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB-n/DBPV-n\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBPV-n/DBPV-n\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.297\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eThe data are presented as the means\u0026thinsp;\u0026plusmn;\u0026thinsp;SDs or medians (interquartile ranges). Paired t tests or Wilcoxon rank-sum tests were used to compare the data collected during normal breathing between the OSA group and the control group. Br, respiratory rate; HR, heart rate; HRV, amplitude of respiratory heart rate variability; HRV%, percentage of HRV; B-n/HRV-n, number of respiratory cycle/number of respiratory heart rate variability cycles; OSA-HRV, amplitude of respiratory heart rate variability in one oscillation cycle; OSA-HRV%, percentage of OSA-HRV; SBP, systolic blood pressure; DBP, diastolic blood pressure; SBPV-M, mean value of systolic blood pressure variability amplitude; DBPV-M, mean value of diastolic blood pressure variability amplitude; SBPV-M%, percentage of SBPV-M; DBPV-M%, percentage of DBPV-M; OSA-DBPV-M, mean value of diastolic blood pressure variability amplitude in oscillation cycles; OSA-SBPV-M%, percentage of OSA-SBPV; OSA-DBPV-M%, percentage of OSA-DBPV-M; B-n/SBPV-n, number of respiratory cycle/number of respiratory systolic blood pressure variability cycles; B-n/DBPV-n, number of respiratory cycle/number of respiratory diastolic blood pressure variability cycles; SBPV-n/DBPV-n/DBPV-n, number of respiratory systolic blood pressure variability cycles\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, respiratory parameters, HRV parameters and BPV-related parameters were analyzed. We found that all respiratory and circulatory parameters (including HRV and BPV-related parameters) increased significantly during OSA compared with those during normal respiration; in particular, oscillation-by-oscillation analysis revealed more significant changes in circulatory parameters. There are different respiratory and circulatory control mechanisms involved in normal respiration and OSA respiration in OSA patients. This differential regulation of OSA may contribute to the occurrence of cardiovascular and cerebrovascular events[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRespiratory sinus arrhythmia (RSA) was first identified by Professor Ludwig in 1847 and fully recorded[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Since its discovery, physicologists have made great efforts to investigate the mechanism of RSA and its physiological importance. Currently, it is suggested that RSA contributes to the enhancement of the ventilation-perfusion ratio, the reduction of physiological dead space volume, and the facilitation of lung gas exchange[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. The process by which respiration affects circulatory system parameters, including changes in thoracic pressure, regulation by the vagus and sympathetic nerves, and reflexive control of the heart, is complicated. Typically, these changes allow the heart to sustain consistent performance under many physiological conditions. Respiration causes variations in thoracic pressure due to the action of the diaphragm and thoracic muscles, which in turn affects the pressure in the thoracic cavity. During the process of inspiration, the diaphragm undergoes a contraction in a downward direction, which leads to the expansion of the thoracic cavity and a decrease in pressure within the chest. During expiration, the diaphragm undergoes relaxation, causing contraction of the thoracic cavity and resulting in an increase in the intrathoracic pressure. Changes in thoracic pressure have a direct impact on the filling and ejection of the heart, which subsequently affects the body[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Respiration affects the activity of the vagus and sympathetic nerves by causing variations in thoracic pressure, which in turn leads to functional changes in the sympathetic and parasympathetic nervous systems. During the process of inspiration, the pressure within the chest cavity decreases, leading to a decrease in the excitability of the vagal nerve and an increase in the excitability of the sympathetic nerve. This eventually leads to an increase in heart rate. In contrast, during expiration, there is an increase in intrathoracic pressure, which results in opposite changes in vagal and sympathetic nerve activity, eventually leading to a decrease in HR[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Moreover, the heart has inherent reflex regulatory mechanisms. The heart receives changes in thoracic pressure via cardiac baroreceptors and pressure sensors. These receptors are responsible for sensing changes in blood pressure and volume. As a result, they control the heart rate and the strength of the heart's contractions to ensure the stability of the cardiovascular system[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. The presence of RSA has nuanced and complicated impacts on BP. Typically, the sympathetic nervous system has a vasoconstrictive influence on blood vessels, resulting in an increase in BP. When changes in BP resulting from respiration combined with the regulation of the sympathetic nervous system might result in an elevation in the force of heart contractions and the amount of blood pumped by the heart, leading to an increase in BP. The impact is particularly noticeable during the inspiration phase of respiration, when there is an increase in sympathetic activity. Conversely, the vagus nerve system has a vasodilatory influence on blood vessels, resulting in a reduction in BP. When changes in HR due to breathing combined with the regulation of the vagus nerve system can result in a reduction in the ability of the heart to contract and pump blood, thereby leading to a decrease in BP. The impact is particularly pronounced during the expiration phase of respiration [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this study, it was shown that during normal respiratory sleep, the B-n/HRV-n, B-n/SBPV-n, and B-n/DBPV-n ratios were approximately equal to 1. This suggests a strong relationship between the features of the respiratory and circulatory systems. These findings indicate that breathing patterns have a primary influence on changes in circulatory parameters during sleep. However, during OSA episodes, these levels exceeded 1. This difference suggests that some respiratory cycles during OSA fail to sufficiently induce changes in circulatory parameters. We noticed a considerable increase in the Bf value during episodes of OSA compared to normal respiration. Normal respiration was frequently interrupted and replaced by smaller and shorter respiratory events, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e4\u003c/span\u003eA. This occurrence can be explained by the approach mentioned above. During sleep, humans experience a decrease in electrical activity in the muscles of the upper airways. This, together with the backward tilt of the tongue and soft palate, can result in different levels of obstruction in the upper airway, leading to the development of OSA[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. During OSA episodes, an obstruction of the airway causes an interruption of breathing, resulting in a reduction in oxygen levels in the blood and an increase in carbon dioxide levels. The peripheral and central chemoreceptors, specifically those located in the aortic body and medulla oblongata, detect these chemical changes and send signals to the brainstem. As a result, there is heightened sensitivity of the respiratory center, which triggers the activation of the sympathetic nervous system, resulting in deeper and faster respiration. However, as a result of the relaxation of the upper respiratory muscles and either partial or complete obstruction of the airway, the act of breathing creates greater negative pressure within the chest cavity. This leads to an increase in the resistance that the left ventricle must overcome to pump blood, which in turn increases the amount of oxygen needed by the heart muscle and reduces the amount of blood it can pump with each beat. As a result, this has an impact on several aspects of blood circulation, as indicated by circulatory parameters. Additionally, it causes minor variations in airflow, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e4\u003c/span\u003eA. The respiratory muscles located in the upper part of the body counteract airway obstruction by enhancing strength. This helps to maintain an open airway, enabling the entry of air into the lungs for the purpose of ventilation. Consequently, respiratory and circulatory parameters tend to return to normal. However, as the muscles become tired and then relax, the upper airway becomes blocked, resulting in repeated episodes of OSA and considerable changes in respiratory and circulatory parameters.\u003c/p\u003e \u003cp\u003eStudies have shown that the resting HRV decreases as individuals age and when they suffer from disease[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], reflecting a diminished ability to cope with environmental stress. On the other hand, a larger resting HRV usually suggests a better level of physiological adaptability and health benefits[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Consequently, these patients have a reduced HRV during sleep, even if they do not have OSA, while maintaining a normal breathing rhythm. However, during periods of OSA, measures related to HRV increase. Although this may seem advantageous at first, it is crucial to acknowledge that the control of breathing in patients with OSA is fundamentally different from that in individuals without OSA during regular sleep. As a result, directly comparing HRV indications between the two groups can be problematic. The main reason for the increased HRV during OSA is the recurrent activation of the sympathetic nerves[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], which leads to frequent and significant changes in circulatory parameters, especially HR and BP. The variability in heart rate increases the risk of cardiovascular events, suggesting that the higher HRV reported during OSA episodes is indeed harmful to the body.\u003c/p\u003e \u003cp\u003eOur research revealed that BPV in patients with OSA is notably greater during episodes of OSA than during normal respiratory sleep during the same night. This indicates a direct relationship between abnormal respiratory episodes that are typical of OSA and increased levels of BPV. Previous studies support the clinical importance of this finding[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. For example, Gutteridge et al.[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] have suggested that increased BPV, specifically during nighttime SBPV, could cause a decrease in brain volume and shrinkage of the hippocampus. This, in turn, may result in cognitive deterioration during later stages of life. Parati et al.[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e] This study highlights the close relationship between BPV and the risk, progression, and severity of organ damage in the heart and kidneys, linking BPV to an increased risk of cardiovascular events. Considering these findings, efficiently managing abnormal breathing patterns in patients with OSA is crucial for enhancing the quality of sleep and reducing the negative effects of increased nocturnal BPV on cardiovascular and cerebrovascular health.\u003c/p\u003e \u003cp\u003eThe HIPM theory strongly emphasizes the interconnections of human physiology, with a particular focus on the integrated connection between the respiratory and circulatory systems[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The levels of PaO\u003csub\u003e2\u003c/sub\u003e, PaCO\u003csub\u003e2\u003c/sub\u003e, and [H\u003csup\u003e+\u003c/sup\u003e]a play a crucial role in regulating respiration and show periodic changes. The variations in arterial blood are more noticeable, suggesting ongoing gas exchange, and decrease to nearly zero in venous blood[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Furthermore, the wave signal increased during hyperventilation and decreased during hypopnea or apnea [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. An increased or reduced wave signal has an impact on the autonomic nervous system's tension by activating peripheral chemoreceptors, which in turn affects the functioning of the circulatory system. Venous blood, including low-frequency signals, enters the right side of the heart, receives oxygenation in the pulmonary circulation, and is then transformed into oxygenated blood with high-frequency signals, which is then expelled by the left side of the heart[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The peripheral chemoreceptors located in the aortic arch and carotid body quickly sense changes in the breathing pattern and transmit this information to central structures to make instantaneous adjustments to the respiratory system. The concept of 'strong-strong' and 'weak-weak' regulation implies that high-amplitude wave signals elicit greater breath, while low-amplitude signals lead to lower breath. Nevertheless, the medulla oblongata's sluggish chemoreceptors are essential for maintaining respiratory stability because they detect alterations in blood signals and respond within approximately 30 seconds. The sensitivity of peripheral fast chemoreceptors is modulated based on these delayed signals, increasing sensitivity when the wave amplitude is low and decreasing it when it is high. The coordination between rapid and gradual responses guarantees the stability of the respiratory system by balancing immediate reactions with longer-term regulatory adjustments[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDuring sleep impacted by OSA, the narrowing or obstruction of the upper airway causes a decrease in the amount of oxygen reaching the small air sacs in the lungs called alveoli, resulting in a reduction in the amplitude of the wave-like signals. This decrease involves the control of fast-response chemoreceptors with low sensitivity, resulting in a reduction in the amplitude of respiration. After a delay of approximately 30 seconds, the chemoreceptors in the respiratory control center of the medulla oblongata detect a weakened signal and increase the sensitivity of fast-response chemoreceptors via neural pathways. As a consequence, there is a rise in the amplitude of subsequent breathing in patients with OSA, which leads to the observed respiratory pattern characterized by alternating strong and weak breaths. This pattern repeats every 40\u0026ndash;60 seconds, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e4\u003c/span\u003eA. Moreover, medical disorders such as hyperlipidemia and hyperglycemia, which elevate blood viscosity and impede blood circulation, might lengthen the duration it takes for wave signals to reach both peripheral and central chemoreceptors. Changes in blood viscosity and flow rate can impact the transmission of signals to receptors, hence affecting the regulation of the respiratory cycle. Likewise, the influence of these wave signals on the tension of the autonomic nervous system reflects their impact on respiration. Signals with a small amplitude decrease tension in the autonomic nervous system, causing variations in HR and BP. Once the signal amplitude falls below a specific threshold, it ceases to induce substantial oscillations during high-pass and low-pass processing. Thus, in the context of OSA, the greater frequency of respiratory cycles relative to the variability in circulatory parameters leads to an elevated ratio of B-n to circulatory parameter cycles.\u003c/p\u003e"},{"header":"Limitations","content":"\u003cp\u003eThis study had a small sample size, making interpretation difficult. A continuous 48-hour PSG monitoring period presented logistical and participation issues, restricting the number of participants. Thus, the small sample size may cause research findings to deviate, impacting the statistical robustness and generalizability of the findings.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eWhen OSA patients have typical breathing patterns during sleep, respiratory and circulatory parameters are correlated, showing that respiratory activities affect circulatory function. When respiratory patterns vary, as they do during OSA episodes, circulatory parameters change, indicating that these changes are respiratory-related. Due to increased sympathetic nerve activity, the instantaneous heart rate and blood pressure increase during OSA onset.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by\u0026nbsp;the\u0026nbsp;National Key Research and Development Program of China (2022YFC2010003), National Key Research and Development Program of China (2022YFC2010000), National Key Research and Development Program of China (2022YFC3601000), National Key Research and Development Program of China (2020YFC2009006), and National Key Research and Development Program of China (2020YFC2009002).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Yu-Die Liu, Jia-Hao Chen, Meng-Jun Xiang and Zeng-Fei Zhang. The first draft of the manuscript was written by\u0026nbsp;Yu-Die Liu and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used during the present study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional Information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConflicts of Interest: All authors have no conflicts of interest to declare.\u003c/p\u003e\n\u003cp\u003eEthical Statement:\u003c/p\u003e\n\u003cp\u003eThe authors are 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. The Ethics Committee of Fuwai Hospital approved the research protocol (Approval No.: 2023-2236), which was carried out in accordance with the Helsinki Declaration. Informed consent was obtained from all participants.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBenjafield Av, A. et al. Estimation of the global prevalence and burden of obstructive sleep apnoea: a literature-based analysis. \u003cem\u003eLancet Respiratory Med.\u003c/em\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S2213-2600(19)30198-5\u003c/span\u003e\u003cspan address=\"10.1016/S2213-2600(19)30198-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2019). 7:.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHou, D. Q., Wang, X. F. \u0026amp; Yang, H. F. 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Physiol.\u003c/em\u003e \u003cb\u003e37\u003c/b\u003e, 40\u0026ndash;44. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://10.12047/j.cjap.0078.2021.103\u003c/span\u003e\u003cspan address=\"https://10.12047/j.cjap.0078.2021.103\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Holistic Integrative Physiology, Medicine, and Health (HIPM), respiratory heart rate variability (HRV), obstructive sleep apnea (OSA), blood pressure variability (BPV), Polysomnography (PSG)","lastPublishedDoi":"10.21203/rs.3.rs-9002435/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9002435/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe disease OSA is common. The mechanism by which breathing pattern induces changes in circulatory parameters is still unclear. Based on the theory of Holistic Integrative Physiology, Medicine, this study aims to explain how respiration affects circulatory parameters during sleep.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur study included 18 normal subjects and 20 OSA patients \u0026nbsp;(Apnea-Hypopnea Index ≥15) underwent standardized cardiopulmonary exercise testing (CPET) and polysomnography (PSG). We analyzed heart rate variability (HRV), systolic/diastolic blood pressure variability (SBPV/DBPV) and their percentage parameters using dual analytical approaches: Breath-by-Breath and Oscillation-by-Oscillation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe found significant changes in respiratory and circulatory parameters during OSA sleep compared to those during normal respiratory sleep. HRV and HRV% were significantly higher in OSA patients (P = 0.004, P = 0.015). Systolic blood pressure and diastolic blood pressure were also significantly elevated. Anoscillation-by-oscillation study showed that abnormal OSA breathing patterns caused greater changes in the patient's circulatory parameters.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur study has shown that abnormal respiratorypatterns during sleep in OSA patients can significantly increase the variability of circulatory parameters, revealing that OSA may interfere with breathing patterns, affect changes in circulatory parameters.\u003c/p\u003e","manuscriptTitle":"A breath-by-breath and oscillation-by-oscillation analysis study: Holistic integrative interpretation of the respiratory-related circulatory parameter changes in OSA patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-14 02:02:13","doi":"10.21203/rs.3.rs-9002435/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-04-04T20:49:00+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-09T05:03:13+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-03T04:08:35+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-03T04:07:11+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-03-01T15:07:27+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2cb4dff6-1cd9-45c1-827f-6c972c0310f6","owner":[],"postedDate":"April 14th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":65888630,"name":"Health sciences/Cardiology"},{"id":65888631,"name":"Health sciences/Diseases"},{"id":65888632,"name":"Health sciences/Health care"},{"id":65888633,"name":"Health sciences/Medical research"},{"id":65888634,"name":"Biological sciences/Physiology"}],"tags":[],"updatedAt":"2026-04-14T02:02:13+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-14 02:02:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9002435","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9002435","identity":"rs-9002435","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}