Clin Auton Res DOI 10.1007/s10286-015-0297-7
RESEARCH ARTICLE
Long-term continuous positive airway pressure therapy improves cardiac autonomic tone during sleep in patients with obstructive sleep apnea Jose-Alberto Palma1,2 • Jorge Iriarte1 • Secundino Fernandez3 • Manuel Alegre1,4 Miguel Valencia4 • Julio Artieda1,4 • Elena Urrestarazu1
•
Received: 29 March 2014 / Accepted: 25 April 2015 Ó Springer-Verlag Berlin Heidelberg 2015
Abstract Background Cardiac autonomic tone after long-term continuous positive airway pressure therapy in patients with obstructive sleep apnea remains unexplored. Methods Thirty patients with obstructive sleep apnea (14 with moderate and 16 with severe obstructive sleep apnea) were studied during a baseline polysomnographic study, after a full night of acute continuous positive airway pressure treatment, and after long-term (*2 years) chronic continuous positive airway pressure therapy. Twenty ageand gender-matched controls with baseline sleep study were selected for comparison purposes. Cross-spectral analysis and the low-frequency (LF) and high-frequency (HF) components of the heart rate variability were computed separately over 10-min ECG epochs during rapid eye movement sleep, non-rapid eye movement sleep, and wakefulness. Results During the baseline study, obstructive sleep apnea patients exhibited increased LF, decreased HF, and increased LF/HF ratio during sleep when compared to
controls. In a multiple regression model, the mean oxygen saturation explained the increased LF during rapid and nonrapid eye movement sleep in obstructive sleep apnea patients. Acute continuous positive airway pressure therapy decreased the LF modulations and the LF/HF ratio and increased the HF modulations during sleep in patients with severe obstructive sleep apnea. Long-term continuous positive airway pressure therapy decreased LF modulations and LF/HF ratio with increased HF modulations during sleep in patients with moderate and severe obstructive sleep apnea. Conclusions Long-term continuous positive airway pressure reduces the sympathovagal imbalance in patients with moderate and severe obstructive sleep apnea, both during rapid and non-rapid eye movement sleep. Continuous positive airway pressure seems to exert its changes in cardiac autonomic modulation by decreasing the burden of nocturnal hypoxia.
& Jose-Alberto Palma [email protected]
Introduction
& Jorge Iriarte [email protected]
Obstructive sleep apnea (OSA) is a sleep breathing disorder that affects over 5 % of men and 2 % of women [23]. OSA, which is considered an independent risk factor for cardiovascular disease, is characterized by repetitive episodes of partial or complete closure of the upper airway [11], changes in intrathoracic pressure, surges in sympathetic activity, and changes in heart rate regulation [3, 9]. These acute and repetitive respiratory events contribute to arrhythmia and sudden cardiac death [17]. One of the simplest noninvasive methods to monitor changes in autonomic cardiovascular modulation is heart
1
Sleep Unit, Clinical Neurophysiology Section, University Clinic of Navarra, Pı´o XII s/n 31008, Pamplona, Spain
2
Department of Neurology, Dysautonomia Center, New York University School of Medicine, 530 First Avenue, Suite 9Q, New York, NY 10016, USA
3
Department of Otolaryngology, University Clinic of Navarra, Pamplona, Spain
4
Neurophysiology Laboratory, CIMA, University of Navarra, Pamplona, Spain
Keywords Sleep-disordered breathing Heart rate variability OSA Hypoxia CPAP
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rate variability (HRV) analysis. HRV is measured by the variation in the R–R interval, and reflects the relationship between the parasympathetic and the sympathetic (SNS) nervous systems [27]. Cardiac autonomic modulations, as measured by HRV, are deeply altered in OSA. Patients with OSA exhibit increased low-frequency (LF) modulations, decreased highfrequency (HF) modulations, and increased LF/HF ratio during sleep [24, 28, 34]. Investigations have also showed that continuous positive air pressure (CPAP) therapy reverses these nocturnal HRV changes [4, 6, 20, 29], even in the first night of treatment [16]. However, only few studies have investigated the evolution of HRV across sleep stages in OSA [6, 15, 16, 28]. Besides, except for a recent report with a reduced sample size [12], the maximum duration of CPAP treatment was limited to 6–9 months, in spite of the fact that most of OSA patients use CPAP therapy for years. Finally, few studies have obtained nocturnal ECG recordings without excluding arousals and apneas, which can alter HRV data [13, 28, 30]. In this study we aimed to overcome the aforementioned limitations by prospectively evaluating the impact of shortterm (one night) and long-term CPAP therapy (*2 years) on HRV during sleep and wakefulness in a homogeneous sample of OSA patients with no comorbidities. We hypothesized that chronic CPAP treatment would reverse the abnormalities in cardiac autonomic tone during sleep found in OSA.
Methods Study population This prospective study included patients with moderate OSA (mOSA) or severe OSA (sOSA), and control subjects, all of them Caucasians. As patients with mild OSA are not usually treated with CPAP, they were not included. Patients were studied in three occasions: the night before CPAP treatment (i.e., sleep study performed with no CPAP), during the first night of CPAP treatment (i.e., sleep study performed with CPAP), and after a chronic CPAP therapy (i.e., sleep study performed with no CPAP). Patients were recruited consecutively from our Sleep Unit within patients undergoing diagnostic overnight polysomnography. Patient recruitment was performed from September 2009 to September 2011. The follow-up period ended in September 2013. Inclusion criteria for OSA were: (1) age range of 30–75 years old; (2) AHI C 15 events/h for mOSA, and C30 events/h for sOSA. Inclusion criteria for controls were: (1) age range of 30–75 years old (2) AHI \ 5 events/h.
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Exclusion criteria for patients and controls comprised: (1) Psychiatric, craniofacial, respiratory or neurologic disorders; (2) history of diabetes mellitus, coronary artery disease, arrhythmias, heart failure, or ST-T wave abnormalities; (3) presence of other sleep disorders; (4) smokers; (5) patients taking cardiovascular medications, or medications known to affect the ANS. Subjects were also advised to refrain from food and caffeine beverages for at least 4 h before the sleep study and not to consume alcohol or undergo intensive exercise within 24-h before the measurements. The Institutional Review Board of the University of Navarra approved this study, and all participants signed informed consent. Study design and polysomnographic study All subjects underwent a baseline polysomnographic study (PSG) to detect OSA. PSGs were performed using Lamont amplifiers, 20 bit, 32 channels and dedicated inputs for EEG, tibial and chin EMG, electrooculogram, oronasal flow (for respiratory rate), respiratory effort, oxymetry, heart rate and body position. ECG was recorded with two derivations (V3 and V5) was amplified, band-pass filtered (0.3–30 Hz), and digitized at 500 Hz. CPAP machines were Resmed (model S9 Elite, California, USA). Sleep stage classification was performed following the current American Academy of Sleep Medicine (AASM) criteria [31], and hypopneas, apneas, and arousals as well as wake time after sleep onset (WASO) were scored using the standard recommended AASM scoring criteria [1]. After the baseline study, patients underwent a second PSG study to treat the OSA using the theoretically appropriate CPAP pressure, which was calculated taking into consideration the neck circumference (NC), BMI, and AHI, according to a previously reported formula [10]. Only patients with total correction of apneas with CPAP were included in this study. CPAP treatment was considered successful if AHI during the night with CPAP was B10 and the lowest oxygen value was C85 %. After the second night, patients were submitted to domiciliary CPAP therapy and were educated about treatment compliance and the importance of CPAP therapy continuation. Control visits were conducted every 6–9 months after CPAP initiation. During control visits, data about CPAP use was downloaded from the CPAP device built-in memory and stored. After chronic CPAP therapy, a third PSG study was performed. Compliance information during the 2-year period was obtained by downloading the data from the CPAP device built-in memory. Only compliant patients (defined as CPAP use C4 h/night on 70 % of nights) were included in this study.
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HRV data handling and analysis Ten-minute epochs were repeatedly transformed and averaged throughout the sleep stages [REM, and non-REM (NREM)] and the wakefulness period right before sleep (WAKE). Sleep data in the form of digital files were collected using the Stellate Reviewer software program (Harmonie 6.0). The files were then analyzed using a HRV analysis custom program created with MatLab (Mathworks Inc., Nattick Massachusetts, USA). All consecutive 10-min ECG segments in each sleep stage were used in the analysis. Then, each ECG recording was manually inspected to exclude abnormal QRS wave morphology, ectopic cardiac beats, movement artifacts, and to ensure that R-waves were correctly marked by the HRV analysis program to allow an accurate detection of R–R intervals. Segments with arousals, apneas, hypopneas and respiratory event-related arousals (RERAs) were also excluded. Cross-spectral analysis between respiratory cycle (as measured by oronasal flow) signals and the LF and HF components of the HRV was calculated to determine the specific frequency ranges over which breathing influenced HRV during sleep. We only accepted cross-spectral results when coherence values were above 0.5 (range 0–1), which was considered to indicate a statistically significant linear correlation between the two signals [2]. R–R time series were interpolated to obtain equidistant values. Then, fast Fourier transform was applied to derive power spectral density across the LF (0.04–0.15 Hz) and HF (0.15–0.40 Hz). As vagal discharge is the principal source of HF power [33], HF power has been used to estimate parasympathetic heart rate modulations. While harmonic power within the LF range has been traditionally attributed to cardiac sympathetic outflow, a new interpretation suggests that LF power reflects modulation of cardiac autonomic outflows by baroreflexes [7]. We also considered the LF/HF ratio, a unitless measure which is thought to reflect the sympathovagal balance [33]. Statistical analyses HF and LF power bands were expressed in absolute units (ms2). Because cardiac autonomic measures are distributed non-normally, the Kruskal–Wallis test was used for comparisons among three or more groups using Bonferroni’s post hoc test for multiple comparisons. Wilcoxon test was used to compare two paired samples (such as changes in HRV in OSA before and after CPAP). Paired t test was used to compare parametric samples. Differences between controls, sOSA and mOSA during baseline were calculated with ANOVA using Bonferroni’s post hoc test for multiple comparisons. A linear regression model was used to examine the effects of BMI and AHI on the spectral
components of HRV. For each outcome variable, logarithm transformations were used to obtain normally distributed values when needed, and two models were constructed: simple linear model for estimating crude effect and multiple linear regression model for estimating adjusted effect by additionally including pre-specified confounding variables age and BMI. A 2-tailed p value \0.05 was considered statistically significant. Data are expressed as mean ± SD. Statistical analyses were performed using SPSS 18.0 (SPSS; Chicago, IL) and Graph Pad Prism 5.0 (MacKiev Software, Boston, MA).
Results Subjects’ characteristics Sixteen patients with mOSA and 14 with sOSA with complete data after 2 years of follow-up met the inclusion criteria. In addition, 20 age- and gender-matched controls were selected for comparison purposes. With the exception of the AHI (higher in sOSA and lower in controls) and the mean and minimum SatO2 (lower in sOSA and higher in controls) there were no significant differences in any of the demographic, sleep, or respiratory characteristics between OSA and controls (Table 1). Patients with mOSA and sOSA also underwent PSG studies with acute CPAP and after chronic CPAP use. Patients’ clinical and sleep features after acute and chronic CPAP therapy are depicted in Table 2. Spectral analysis and coherence analysis Figure 1 shows the results in the HF and LF spectral components and the LF/HF ratio during different stages (WAKE, REM and NREM) at baseline (no CPAP), during acute CPAP therapy, and after chronic CPAP therapy. Compared to controls, sOSA exhibited higher LF power during NREM (Fig. 1b). Compared to controls, both mOSA and sOSA exhibited higher LF modulations during REM (Fig. 1c), lower HF modulations during NREM (Fig. 1e) and REM (Fig. 1f), and higher LF/HF ratio during NREM (Fig. 1h) and REM (Fig. 1i), in all cases p \ 0.001. No differences were observed during wakefulness. Patients with OSA were then submitted to acute CPAP therapy. In patients with mOSA, chronic CPAP use reduced the LF/HF ratio during NREM (Fig. 1h). During REM the use of chronic CPAP (but not acute CPAP) led to a significant decrease in LF power (Fig. 1c) and the LF/HF ratio (Fig. 1i) and an increase in HF power (Fig. 1f). In patients with sOSA, both acute and chronic CPAP decreased the LF power during NREM (Fig. 1b) and REM
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Clin Auton Res Table 1 Baseline demographic and sleep characteristics of patients included in this study Controls (n = 20)
Moderate OSA (n = 16)
Severe OSA (n = 14)
ANOVA p value (Bonferroni post hoc significant comparisons)
Age (years), mean ± SD
51.2 ± 11.8
51.8 ± 13.4
52.3 ± 11.9
0.96
Male/female
15/5
13/3
11/3
0.98
Body mass index (kg/m2) Systolic blood pressure (mmHg)
32.1 ± 9 122.2 ± 14.1
31.3 ± 9.5 123.2 ± 12.1
35.1 ± 8.3 131.6 ± 16.7
0.48 0.10
Diastolic blood pressure (mmHg)
77.3 ± 7.7
81.2 ± 8.3
80.1 ± 11.4
0.35
Total sleep time (min)
465.3 ± 77
476.7 ± 76.8
507.3 ± 70.3
0.27
Apnea hypopnea index (n8/hr)
3.5 ± 1.1
26.6 ± 1.8
55.8 ± 16.5
\0.0001 (controls vs mOSA; controls vs sOSA; mOSA vs sOSA)
REM stage (%)
21.7 ± 6.1
19.6 ± 5.5
19.5 ± 8.1
0.32
NREM stage (%)
78.3 ± 11.8
80.4 ± 8.1
80.5 ± 10.2
0.45
REM latency (min) WASO (%)
117.6 ± 46.7 7.4 ± 5.3
121.3 ± 90.1 9.1 ± 7.3
133.7 ± 104 10.3 ± 8.3
0.84 0.47
Awakenings (n8/h)
4.04 ± 3.7
7.1 ± 4.3
5.5 ± 3.7
0.07
Minimum (nadir) SatO2 (%)
91.8 ± 2
84.9 ± 6.4
71.5 ± 11.2
\0.0001 (controls vs mOSA; controls vs sOSA; mOSA vs sOSA)
Mean SatO2 (%)
96.3 ± 1.1
95.2 ± 1.5
92.8 ± 3.1
\0.0001 (controls vs sOSA; mOSA vs sOSA)
OSA obstructive sleep apnea, SD standard deviation, CPAP continuous positive air pressure, WASO wake time after sleep onset, REM rapid eye movement, NREM non-REM
Table 2 Clinical and sleep parameters after acute and chronic CPAP in patients with sleep apnea Moderate OSA (n = 16)
Severe OSA (n = 14)
During acute CPAP
After chronic CPAP
During acute CPAP
After chronic CPAP
Length of CPAP use (years)
–#
2.1 ± 0.75
–#
1.9 ± 0.71
Age (years), mean ± SD
51.8 ± 13.4
52.3 ± 7.9
52.3 ± 11.9
53.1 ± 8.7
Male/female (n)
13/3
13/3
11/3
11/3
Body mass index (kg/m2)
31.3 ± 9.5
33.1 ± 10.51
35.1 ± 8.3
34.38 ± 8.5
Systolic blood pressure (mmHg)
123.3 ± 11.2
125.14 ± 13.1
130.2 ± 12.7
121.7 ± 4.4*
Diastolic blood pressure (mmHg)
82.2 ± 9.3
79.8 ± 9.2
79.2 ± 11.4
77.8 ± 7.6
Total sleep time (min) Apnea hypopnea index (n8/h)
456.1 ± 90.1 4.2 ± 6.4
499 ± 61.8 7.6 ± 3.9
432.5 ± 66.8 3.9 ± 8.1
470.8 ± 67.1 6.1 ± 3.4
REM stage (%)
19.6 ± 9.7
18.3 ± 5.1
21.8 ± 9.3
24.5 ± 9.1
NREM stage (%)
80.4 ± 8.8
81.7 ± 9.5
78.2 ± 10.7
75.5 ± 15.1
REM latency (min)
112.4 ± 77.3
112.5 ± 46.9
91.3 ± 102.1
116.6 ± 104
WASO (%)
9.1 ± 8
7.2 ± 5.7
10.6 ± 7.8
7.1 ± 7.5
Awakenings (n8/h)
3.1 ± 3.1
4.8 ± 1.7
6.9 ± 2.6
5.1 ± 4.3
Minimal SatO2 (%)
91.2 ± 2.8
91.1 ± 4.1
89.1 ± 4.6
90.7 ± 6.2
Mean SatO2 (%)
96.2 ± 0.8
95.4 ± 0.5
95.3 ± 1.2
95.2 ± 0.9
OSA obstructive sleep apnea, SD standard deviation, CPAP continuous positive air pressure, WASO wake time after sleep onset, REM rapid eye movement, NREM non-REM #
Patients were using CPAP for the first time
* Denotes statistically significant differences (p \ 0.05) between chronic CPAP and acute CPAP
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Fig. 1 High-frequency (HF) and low-frequency (LF) bands and LF/ HF ratio during different stages (WAKE, REM and NREM) at baseline (no CPAP), during acute CPAP therapy, and after chronic CPAP therapy. In patients with OSA, treatment with CPAP induced a
decrease in LF modulations and the LF/HF ratio and an increase in HF modulations during REM and NREM. No significant changes were observed during wakefulness (WAKE). * p \ 0.05, ** p \ 0.01
(Fig. 1c), increased the HF power during NREM (Fig. 1e) and REM (Fig. 1f) and decreased the LF/HF ratio during REM (Fig. 1i). The latter effect was also observed during NREM but only after chronic CPAP use (Fig. 1h). No differences were observed during wakefulness. As expected, coherence analysis disclosed a significant coherence between the respiratory cycle and the HF band, in all stages, particularly during NREM. There was no significant coherence between the respiratory cycle and the LF band. The coherence in the HF band increased significantly after CPAP in patients with mOSA and sOSA (Table 3).
(r2 = -0.47; p = 0.004).
Linear regression model Unadjusted regression model using baseline data from OSA patients disclosed significant relationships (p \ 0.05) between BMI, AHI, mean SatO2 and the LF component during NREM and REM, but not during WAKE. However, a multiple linear regression model adjusting for age, BMI and AHI disclosed significant relationships only between mean SatO2 and the LF component during REM
p \ 0.001)
and
NREM
(r2 = -0.41;
Discussion This study confirms that patients with OSA exhibit a different HRV profile during sleep when compared to control subjects and that these differences tend to decrease with acute CPAP therapy [16]. In patients with OSA, LF modulations were higher during sleep and decreased after CPAP therapy, while HF modulations were lower during sleep and increased after CPAP therapy. The major finding of this study is that changes induced by acute CPAP were sustained and even magnified after chronic (*2 years) CPAP. This was even in spite of the fact that patients’ body mass index did not change between the basal and the long-term studies. The changes after chronic CPAP tended to be higher than those seen after acute CPAP, confirming the notion that long-term compliance of CPAP treatment is essential for the normalization of the cardiac autonomic activity. This seemed to be
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Clin Auton Res Table 3 Average coherence results from cross-spectral analysis between respiratory cycle and spectral components of heart rate variability Controls (n = 20)
Moderate OSA (n = 16)
Baseline
Baseline
Severe OSA (n = 14)
Acute CPAP
Chronic CPAP
Baseline
Acute CPAP
Chronic CPAP
WAKE Coherence in LF band
0.24 ± 0.22
0.21 ± 0.13
0.28 ± 0.11
0.24 ± 0.17
0.19 ± 0.18
0.20 ± 0.13
0.13 ± 0.12
Coherence in HF band
0.77 ± 0.30
0.80 ± 0.34
0.84 ± 0.31
0.85 ± 0.78
0.85 ± 0.44
0.86 ± 0.43
0.89 ± 0.55
Coherence in LF band
0.25 ± 0.20
0.30 ± 0.15
0.32 ± 0.23
0.35 ± 0.24
0.39 ± 0.09
0.27 ± 0.16
0.19 ± 0.06
Coherence in HF band
0.81 ± 0.14
0.69 ± 0.20
0.84 ± 0.13*
0.85 ± 0.45*
0.70 ± 025
0.81 ± 0.34*
0.89 ± 0.27*
Coherence in LF band
0.17 ± 0.06
0.21 ± 0.12
0.23 ± 0.22
0.16 ± 0.13
0.20 ± 0.07
0.22 ± 0.14
0.19 ± 0.07
Coherence in HF band
0.80 ± 0.39
0.69 ± 0.21
0.76 ± 0.23
0.79 ± 0.28*
0.73 ± 0.23
0.82 ± 0.39
0.89 ± 0.25*
NREM
REM
* Denotes statistically significant differences (p \ 0.05) compared to baseline
more relevant in patients with moderate, rather than severe OSA, as significant changes in this subgroup were observed only after chronic, rather than acute CPAP therapy. Spectral analysis of HRV during sleep in patients with untreated OSA has been applied in previous studies. These found abnormally low levels of overall HRV and HF power, and high levels of LF during sleep [15, 19, 34]. Other works have examined the effect of long-term CPAP therapy (limited to 6–9 months) on HRV and its spectral components during sleep [8, 14, 29]. In a recent paper, 9 patients with OSA before and after *4 years of nasal CPAP were studied [12]. No statistical differences in terms of spectral components of HRV during sleep were found, although the lack of significance might be related to the small sample size. This is the first study to analyze HRV measures across sleep stages in a noteworthy sample of OSA patients (n = 30) after long-term CPAP therapy. Our study also used cross-spectral analysis to study the relationship between the respiratory cycle and the HRV. We found that the HF modulations are in intimate coherence with the respiration, and that CPAP treatment increases this coherence, as previously described in obese OSA subjects [25]. This study has several strengths. Only patients that were fully compliant with the CPAP therapy were selected. CPAP compliance was measured objectively by means of a built-in memory that stored the time at which the CPAP unit was applied at the prescribed pressure, in contrast to previous studies which used self-reported estimated use [18] or did not specify compliance [29]. Also, HRV analyses during sleep were performed excluding arousals, hypopneas, RERAs, and apneas (which can alter the HRV analysis), and across sleep stages, while previous studies did not differentiate among sleep stages. And fourth, we
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used a regression analysis to study which variable had influence on the observed HRV differences. Another appealing finding of the present study is that changes in the LF spectral component during REM and non-REM sleep were correlated with the BMI, the AHI, the minimal SatO2 and the mean SatO2; however, when all these variables were introduced in a regression model, only the mean SatO2 (and not the differences in BMI, hypertension or AHI) explained the increased LF modulations during sleep in OSA. These results are consistent with fact that muscle sympathetic nerve activity is chronically elevated in patients with OSA [18], probably as a direct consequence of nocturnal hypoxemia [32] independent of the obesity status or the AHI. Our findings which show that changes in HRV during sleep after long-term CPAP are not associated with BMI is in accordance with a recent report [12]. Conversely, previous studies have observed that impairment of HRV in OSA is more severe if subjects also have obesity [28]. This discrepancy may be explained by the fact that the previous studies that found differences of HRV depending on the BMI did not perform regression analysis, so that their results may be cofounded by other variables, such as the AHI and SatO2, as obese subjects generally exhibit increased AHI and decreased nocturnal SatO2. Some pieces of research showed beneficial effects of CPAP therapy in cardiovascular autonomic tone during daytime, rather than during sleep [18, 19], whereas we found this improvement only during sleep. Our results would support the notion that CPAP treatment contributes to the resetting of cardiovascular reflexes and cardiorespiratory physiology during sleep, rather than during wakefulness. This is in keeping with the fact that OSA patients have a peak in sudden cardiac death during sleeping hours
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[5]. Long-term CPAP therapy may, therefore, have the potential to prevent sudden death in OSA, although more studies are needed to confirm this. It is known that HRV during sleep can be altered due to fluctuations mediated by respiratory changes in intrathoracic pressure. This was the main reason to exclude periods with apneas in our analysis. Our results support the importance of hypoxia per se, separately from respiratory phenomena in the abnormalities of cardiac autonomic modulation during sleep as seems to be the case in patients with sleep-related hypoxia with no apneas (i.e., sleep-related hypoventilation) [22]. These findings suggest that, from a clinical point of view, when OSA patients are submitted to CPAP treatment, an appropriate increase in nocturnal SatO2 concurrently with a decrease in AHI are necessary to revert the changes in cardiac autonomic modulation seen before CPAP. Patients with OSA frequently have other comorbidities. The main limitation of this work is that our sample was very homogeneous, free from conditions such as obesity, hypertension, or diabetes. Even mild hyperglycemia has been shown to influence HRV in OSA [26]. Further, very long-term prospective studies must study the effect of OSA comorbidities in cardiac autonomic modulation, taking into account the different phenotypes of OSA [21]. In conclusion, long-term treatment with CPAP in OSA patients reduced the sympathovagal imbalance and increased parasympathetic modulations to the heart, both during REM and non-REM sleep. Long-term CPAP treatment might therefore have potential to reduce the risk for death in this population, although more studies are needed to confirm this. From a therapeutic point of view, our results suggest that CPAP treatment exerts its changes in cardiac autonomic modulation by decreasing the burden of nocturnal hypoxia. Therefore, when assessing the efficacy of CPAP treatment, not only the apnea and hypopnea index but also the nocturnal oxygen saturation should be closely evaluated. Acknowledgments This work was funded in part by Asociacion de Amigos de la Universidad de Navarra. Conflict of interest The authors declare that they have no conflicts of interests.
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RESEARCH ARTICLE
Long-term continuous positive airway pressure therapy improves cardiac autonomic tone during sleep in patients with obstructive sleep apnea Jose-Alberto Palma1,2 • Jorge Iriarte1 • Secundino Fernandez3 • Manuel Alegre1,4 Miguel Valencia4 • Julio Artieda1,4 • Elena Urrestarazu1
•
Received: 29 March 2014 / Accepted: 25 April 2015 Ó Springer-Verlag Berlin Heidelberg 2015
Abstract Background Cardiac autonomic tone after long-term continuous positive airway pressure therapy in patients with obstructive sleep apnea remains unexplored. Methods Thirty patients with obstructive sleep apnea (14 with moderate and 16 with severe obstructive sleep apnea) were studied during a baseline polysomnographic study, after a full night of acute continuous positive airway pressure treatment, and after long-term (*2 years) chronic continuous positive airway pressure therapy. Twenty ageand gender-matched controls with baseline sleep study were selected for comparison purposes. Cross-spectral analysis and the low-frequency (LF) and high-frequency (HF) components of the heart rate variability were computed separately over 10-min ECG epochs during rapid eye movement sleep, non-rapid eye movement sleep, and wakefulness. Results During the baseline study, obstructive sleep apnea patients exhibited increased LF, decreased HF, and increased LF/HF ratio during sleep when compared to
controls. In a multiple regression model, the mean oxygen saturation explained the increased LF during rapid and nonrapid eye movement sleep in obstructive sleep apnea patients. Acute continuous positive airway pressure therapy decreased the LF modulations and the LF/HF ratio and increased the HF modulations during sleep in patients with severe obstructive sleep apnea. Long-term continuous positive airway pressure therapy decreased LF modulations and LF/HF ratio with increased HF modulations during sleep in patients with moderate and severe obstructive sleep apnea. Conclusions Long-term continuous positive airway pressure reduces the sympathovagal imbalance in patients with moderate and severe obstructive sleep apnea, both during rapid and non-rapid eye movement sleep. Continuous positive airway pressure seems to exert its changes in cardiac autonomic modulation by decreasing the burden of nocturnal hypoxia.
& Jose-Alberto Palma [email protected]
Introduction
& Jorge Iriarte [email protected]
Obstructive sleep apnea (OSA) is a sleep breathing disorder that affects over 5 % of men and 2 % of women [23]. OSA, which is considered an independent risk factor for cardiovascular disease, is characterized by repetitive episodes of partial or complete closure of the upper airway [11], changes in intrathoracic pressure, surges in sympathetic activity, and changes in heart rate regulation [3, 9]. These acute and repetitive respiratory events contribute to arrhythmia and sudden cardiac death [17]. One of the simplest noninvasive methods to monitor changes in autonomic cardiovascular modulation is heart
1
Sleep Unit, Clinical Neurophysiology Section, University Clinic of Navarra, Pı´o XII s/n 31008, Pamplona, Spain
2
Department of Neurology, Dysautonomia Center, New York University School of Medicine, 530 First Avenue, Suite 9Q, New York, NY 10016, USA
3
Department of Otolaryngology, University Clinic of Navarra, Pamplona, Spain
4
Neurophysiology Laboratory, CIMA, University of Navarra, Pamplona, Spain
Keywords Sleep-disordered breathing Heart rate variability OSA Hypoxia CPAP
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rate variability (HRV) analysis. HRV is measured by the variation in the R–R interval, and reflects the relationship between the parasympathetic and the sympathetic (SNS) nervous systems [27]. Cardiac autonomic modulations, as measured by HRV, are deeply altered in OSA. Patients with OSA exhibit increased low-frequency (LF) modulations, decreased highfrequency (HF) modulations, and increased LF/HF ratio during sleep [24, 28, 34]. Investigations have also showed that continuous positive air pressure (CPAP) therapy reverses these nocturnal HRV changes [4, 6, 20, 29], even in the first night of treatment [16]. However, only few studies have investigated the evolution of HRV across sleep stages in OSA [6, 15, 16, 28]. Besides, except for a recent report with a reduced sample size [12], the maximum duration of CPAP treatment was limited to 6–9 months, in spite of the fact that most of OSA patients use CPAP therapy for years. Finally, few studies have obtained nocturnal ECG recordings without excluding arousals and apneas, which can alter HRV data [13, 28, 30]. In this study we aimed to overcome the aforementioned limitations by prospectively evaluating the impact of shortterm (one night) and long-term CPAP therapy (*2 years) on HRV during sleep and wakefulness in a homogeneous sample of OSA patients with no comorbidities. We hypothesized that chronic CPAP treatment would reverse the abnormalities in cardiac autonomic tone during sleep found in OSA.
Methods Study population This prospective study included patients with moderate OSA (mOSA) or severe OSA (sOSA), and control subjects, all of them Caucasians. As patients with mild OSA are not usually treated with CPAP, they were not included. Patients were studied in three occasions: the night before CPAP treatment (i.e., sleep study performed with no CPAP), during the first night of CPAP treatment (i.e., sleep study performed with CPAP), and after a chronic CPAP therapy (i.e., sleep study performed with no CPAP). Patients were recruited consecutively from our Sleep Unit within patients undergoing diagnostic overnight polysomnography. Patient recruitment was performed from September 2009 to September 2011. The follow-up period ended in September 2013. Inclusion criteria for OSA were: (1) age range of 30–75 years old; (2) AHI C 15 events/h for mOSA, and C30 events/h for sOSA. Inclusion criteria for controls were: (1) age range of 30–75 years old (2) AHI \ 5 events/h.
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Exclusion criteria for patients and controls comprised: (1) Psychiatric, craniofacial, respiratory or neurologic disorders; (2) history of diabetes mellitus, coronary artery disease, arrhythmias, heart failure, or ST-T wave abnormalities; (3) presence of other sleep disorders; (4) smokers; (5) patients taking cardiovascular medications, or medications known to affect the ANS. Subjects were also advised to refrain from food and caffeine beverages for at least 4 h before the sleep study and not to consume alcohol or undergo intensive exercise within 24-h before the measurements. The Institutional Review Board of the University of Navarra approved this study, and all participants signed informed consent. Study design and polysomnographic study All subjects underwent a baseline polysomnographic study (PSG) to detect OSA. PSGs were performed using Lamont amplifiers, 20 bit, 32 channels and dedicated inputs for EEG, tibial and chin EMG, electrooculogram, oronasal flow (for respiratory rate), respiratory effort, oxymetry, heart rate and body position. ECG was recorded with two derivations (V3 and V5) was amplified, band-pass filtered (0.3–30 Hz), and digitized at 500 Hz. CPAP machines were Resmed (model S9 Elite, California, USA). Sleep stage classification was performed following the current American Academy of Sleep Medicine (AASM) criteria [31], and hypopneas, apneas, and arousals as well as wake time after sleep onset (WASO) were scored using the standard recommended AASM scoring criteria [1]. After the baseline study, patients underwent a second PSG study to treat the OSA using the theoretically appropriate CPAP pressure, which was calculated taking into consideration the neck circumference (NC), BMI, and AHI, according to a previously reported formula [10]. Only patients with total correction of apneas with CPAP were included in this study. CPAP treatment was considered successful if AHI during the night with CPAP was B10 and the lowest oxygen value was C85 %. After the second night, patients were submitted to domiciliary CPAP therapy and were educated about treatment compliance and the importance of CPAP therapy continuation. Control visits were conducted every 6–9 months after CPAP initiation. During control visits, data about CPAP use was downloaded from the CPAP device built-in memory and stored. After chronic CPAP therapy, a third PSG study was performed. Compliance information during the 2-year period was obtained by downloading the data from the CPAP device built-in memory. Only compliant patients (defined as CPAP use C4 h/night on 70 % of nights) were included in this study.
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HRV data handling and analysis Ten-minute epochs were repeatedly transformed and averaged throughout the sleep stages [REM, and non-REM (NREM)] and the wakefulness period right before sleep (WAKE). Sleep data in the form of digital files were collected using the Stellate Reviewer software program (Harmonie 6.0). The files were then analyzed using a HRV analysis custom program created with MatLab (Mathworks Inc., Nattick Massachusetts, USA). All consecutive 10-min ECG segments in each sleep stage were used in the analysis. Then, each ECG recording was manually inspected to exclude abnormal QRS wave morphology, ectopic cardiac beats, movement artifacts, and to ensure that R-waves were correctly marked by the HRV analysis program to allow an accurate detection of R–R intervals. Segments with arousals, apneas, hypopneas and respiratory event-related arousals (RERAs) were also excluded. Cross-spectral analysis between respiratory cycle (as measured by oronasal flow) signals and the LF and HF components of the HRV was calculated to determine the specific frequency ranges over which breathing influenced HRV during sleep. We only accepted cross-spectral results when coherence values were above 0.5 (range 0–1), which was considered to indicate a statistically significant linear correlation between the two signals [2]. R–R time series were interpolated to obtain equidistant values. Then, fast Fourier transform was applied to derive power spectral density across the LF (0.04–0.15 Hz) and HF (0.15–0.40 Hz). As vagal discharge is the principal source of HF power [33], HF power has been used to estimate parasympathetic heart rate modulations. While harmonic power within the LF range has been traditionally attributed to cardiac sympathetic outflow, a new interpretation suggests that LF power reflects modulation of cardiac autonomic outflows by baroreflexes [7]. We also considered the LF/HF ratio, a unitless measure which is thought to reflect the sympathovagal balance [33]. Statistical analyses HF and LF power bands were expressed in absolute units (ms2). Because cardiac autonomic measures are distributed non-normally, the Kruskal–Wallis test was used for comparisons among three or more groups using Bonferroni’s post hoc test for multiple comparisons. Wilcoxon test was used to compare two paired samples (such as changes in HRV in OSA before and after CPAP). Paired t test was used to compare parametric samples. Differences between controls, sOSA and mOSA during baseline were calculated with ANOVA using Bonferroni’s post hoc test for multiple comparisons. A linear regression model was used to examine the effects of BMI and AHI on the spectral
components of HRV. For each outcome variable, logarithm transformations were used to obtain normally distributed values when needed, and two models were constructed: simple linear model for estimating crude effect and multiple linear regression model for estimating adjusted effect by additionally including pre-specified confounding variables age and BMI. A 2-tailed p value \0.05 was considered statistically significant. Data are expressed as mean ± SD. Statistical analyses were performed using SPSS 18.0 (SPSS; Chicago, IL) and Graph Pad Prism 5.0 (MacKiev Software, Boston, MA).
Results Subjects’ characteristics Sixteen patients with mOSA and 14 with sOSA with complete data after 2 years of follow-up met the inclusion criteria. In addition, 20 age- and gender-matched controls were selected for comparison purposes. With the exception of the AHI (higher in sOSA and lower in controls) and the mean and minimum SatO2 (lower in sOSA and higher in controls) there were no significant differences in any of the demographic, sleep, or respiratory characteristics between OSA and controls (Table 1). Patients with mOSA and sOSA also underwent PSG studies with acute CPAP and after chronic CPAP use. Patients’ clinical and sleep features after acute and chronic CPAP therapy are depicted in Table 2. Spectral analysis and coherence analysis Figure 1 shows the results in the HF and LF spectral components and the LF/HF ratio during different stages (WAKE, REM and NREM) at baseline (no CPAP), during acute CPAP therapy, and after chronic CPAP therapy. Compared to controls, sOSA exhibited higher LF power during NREM (Fig. 1b). Compared to controls, both mOSA and sOSA exhibited higher LF modulations during REM (Fig. 1c), lower HF modulations during NREM (Fig. 1e) and REM (Fig. 1f), and higher LF/HF ratio during NREM (Fig. 1h) and REM (Fig. 1i), in all cases p \ 0.001. No differences were observed during wakefulness. Patients with OSA were then submitted to acute CPAP therapy. In patients with mOSA, chronic CPAP use reduced the LF/HF ratio during NREM (Fig. 1h). During REM the use of chronic CPAP (but not acute CPAP) led to a significant decrease in LF power (Fig. 1c) and the LF/HF ratio (Fig. 1i) and an increase in HF power (Fig. 1f). In patients with sOSA, both acute and chronic CPAP decreased the LF power during NREM (Fig. 1b) and REM
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Clin Auton Res Table 1 Baseline demographic and sleep characteristics of patients included in this study Controls (n = 20)
Moderate OSA (n = 16)
Severe OSA (n = 14)
ANOVA p value (Bonferroni post hoc significant comparisons)
Age (years), mean ± SD
51.2 ± 11.8
51.8 ± 13.4
52.3 ± 11.9
0.96
Male/female
15/5
13/3
11/3
0.98
Body mass index (kg/m2) Systolic blood pressure (mmHg)
32.1 ± 9 122.2 ± 14.1
31.3 ± 9.5 123.2 ± 12.1
35.1 ± 8.3 131.6 ± 16.7
0.48 0.10
Diastolic blood pressure (mmHg)
77.3 ± 7.7
81.2 ± 8.3
80.1 ± 11.4
0.35
Total sleep time (min)
465.3 ± 77
476.7 ± 76.8
507.3 ± 70.3
0.27
Apnea hypopnea index (n8/hr)
3.5 ± 1.1
26.6 ± 1.8
55.8 ± 16.5
\0.0001 (controls vs mOSA; controls vs sOSA; mOSA vs sOSA)
REM stage (%)
21.7 ± 6.1
19.6 ± 5.5
19.5 ± 8.1
0.32
NREM stage (%)
78.3 ± 11.8
80.4 ± 8.1
80.5 ± 10.2
0.45
REM latency (min) WASO (%)
117.6 ± 46.7 7.4 ± 5.3
121.3 ± 90.1 9.1 ± 7.3
133.7 ± 104 10.3 ± 8.3
0.84 0.47
Awakenings (n8/h)
4.04 ± 3.7
7.1 ± 4.3
5.5 ± 3.7
0.07
Minimum (nadir) SatO2 (%)
91.8 ± 2
84.9 ± 6.4
71.5 ± 11.2
\0.0001 (controls vs mOSA; controls vs sOSA; mOSA vs sOSA)
Mean SatO2 (%)
96.3 ± 1.1
95.2 ± 1.5
92.8 ± 3.1
\0.0001 (controls vs sOSA; mOSA vs sOSA)
OSA obstructive sleep apnea, SD standard deviation, CPAP continuous positive air pressure, WASO wake time after sleep onset, REM rapid eye movement, NREM non-REM
Table 2 Clinical and sleep parameters after acute and chronic CPAP in patients with sleep apnea Moderate OSA (n = 16)
Severe OSA (n = 14)
During acute CPAP
After chronic CPAP
During acute CPAP
After chronic CPAP
Length of CPAP use (years)
–#
2.1 ± 0.75
–#
1.9 ± 0.71
Age (years), mean ± SD
51.8 ± 13.4
52.3 ± 7.9
52.3 ± 11.9
53.1 ± 8.7
Male/female (n)
13/3
13/3
11/3
11/3
Body mass index (kg/m2)
31.3 ± 9.5
33.1 ± 10.51
35.1 ± 8.3
34.38 ± 8.5
Systolic blood pressure (mmHg)
123.3 ± 11.2
125.14 ± 13.1
130.2 ± 12.7
121.7 ± 4.4*
Diastolic blood pressure (mmHg)
82.2 ± 9.3
79.8 ± 9.2
79.2 ± 11.4
77.8 ± 7.6
Total sleep time (min) Apnea hypopnea index (n8/h)
456.1 ± 90.1 4.2 ± 6.4
499 ± 61.8 7.6 ± 3.9
432.5 ± 66.8 3.9 ± 8.1
470.8 ± 67.1 6.1 ± 3.4
REM stage (%)
19.6 ± 9.7
18.3 ± 5.1
21.8 ± 9.3
24.5 ± 9.1
NREM stage (%)
80.4 ± 8.8
81.7 ± 9.5
78.2 ± 10.7
75.5 ± 15.1
REM latency (min)
112.4 ± 77.3
112.5 ± 46.9
91.3 ± 102.1
116.6 ± 104
WASO (%)
9.1 ± 8
7.2 ± 5.7
10.6 ± 7.8
7.1 ± 7.5
Awakenings (n8/h)
3.1 ± 3.1
4.8 ± 1.7
6.9 ± 2.6
5.1 ± 4.3
Minimal SatO2 (%)
91.2 ± 2.8
91.1 ± 4.1
89.1 ± 4.6
90.7 ± 6.2
Mean SatO2 (%)
96.2 ± 0.8
95.4 ± 0.5
95.3 ± 1.2
95.2 ± 0.9
OSA obstructive sleep apnea, SD standard deviation, CPAP continuous positive air pressure, WASO wake time after sleep onset, REM rapid eye movement, NREM non-REM #
Patients were using CPAP for the first time
* Denotes statistically significant differences (p \ 0.05) between chronic CPAP and acute CPAP
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Fig. 1 High-frequency (HF) and low-frequency (LF) bands and LF/ HF ratio during different stages (WAKE, REM and NREM) at baseline (no CPAP), during acute CPAP therapy, and after chronic CPAP therapy. In patients with OSA, treatment with CPAP induced a
decrease in LF modulations and the LF/HF ratio and an increase in HF modulations during REM and NREM. No significant changes were observed during wakefulness (WAKE). * p \ 0.05, ** p \ 0.01
(Fig. 1c), increased the HF power during NREM (Fig. 1e) and REM (Fig. 1f) and decreased the LF/HF ratio during REM (Fig. 1i). The latter effect was also observed during NREM but only after chronic CPAP use (Fig. 1h). No differences were observed during wakefulness. As expected, coherence analysis disclosed a significant coherence between the respiratory cycle and the HF band, in all stages, particularly during NREM. There was no significant coherence between the respiratory cycle and the LF band. The coherence in the HF band increased significantly after CPAP in patients with mOSA and sOSA (Table 3).
(r2 = -0.47; p = 0.004).
Linear regression model Unadjusted regression model using baseline data from OSA patients disclosed significant relationships (p \ 0.05) between BMI, AHI, mean SatO2 and the LF component during NREM and REM, but not during WAKE. However, a multiple linear regression model adjusting for age, BMI and AHI disclosed significant relationships only between mean SatO2 and the LF component during REM
p \ 0.001)
and
NREM
(r2 = -0.41;
Discussion This study confirms that patients with OSA exhibit a different HRV profile during sleep when compared to control subjects and that these differences tend to decrease with acute CPAP therapy [16]. In patients with OSA, LF modulations were higher during sleep and decreased after CPAP therapy, while HF modulations were lower during sleep and increased after CPAP therapy. The major finding of this study is that changes induced by acute CPAP were sustained and even magnified after chronic (*2 years) CPAP. This was even in spite of the fact that patients’ body mass index did not change between the basal and the long-term studies. The changes after chronic CPAP tended to be higher than those seen after acute CPAP, confirming the notion that long-term compliance of CPAP treatment is essential for the normalization of the cardiac autonomic activity. This seemed to be
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Clin Auton Res Table 3 Average coherence results from cross-spectral analysis between respiratory cycle and spectral components of heart rate variability Controls (n = 20)
Moderate OSA (n = 16)
Baseline
Baseline
Severe OSA (n = 14)
Acute CPAP
Chronic CPAP
Baseline
Acute CPAP
Chronic CPAP
WAKE Coherence in LF band
0.24 ± 0.22
0.21 ± 0.13
0.28 ± 0.11
0.24 ± 0.17
0.19 ± 0.18
0.20 ± 0.13
0.13 ± 0.12
Coherence in HF band
0.77 ± 0.30
0.80 ± 0.34
0.84 ± 0.31
0.85 ± 0.78
0.85 ± 0.44
0.86 ± 0.43
0.89 ± 0.55
Coherence in LF band
0.25 ± 0.20
0.30 ± 0.15
0.32 ± 0.23
0.35 ± 0.24
0.39 ± 0.09
0.27 ± 0.16
0.19 ± 0.06
Coherence in HF band
0.81 ± 0.14
0.69 ± 0.20
0.84 ± 0.13*
0.85 ± 0.45*
0.70 ± 025
0.81 ± 0.34*
0.89 ± 0.27*
Coherence in LF band
0.17 ± 0.06
0.21 ± 0.12
0.23 ± 0.22
0.16 ± 0.13
0.20 ± 0.07
0.22 ± 0.14
0.19 ± 0.07
Coherence in HF band
0.80 ± 0.39
0.69 ± 0.21
0.76 ± 0.23
0.79 ± 0.28*
0.73 ± 0.23
0.82 ± 0.39
0.89 ± 0.25*
NREM
REM
* Denotes statistically significant differences (p \ 0.05) compared to baseline
more relevant in patients with moderate, rather than severe OSA, as significant changes in this subgroup were observed only after chronic, rather than acute CPAP therapy. Spectral analysis of HRV during sleep in patients with untreated OSA has been applied in previous studies. These found abnormally low levels of overall HRV and HF power, and high levels of LF during sleep [15, 19, 34]. Other works have examined the effect of long-term CPAP therapy (limited to 6–9 months) on HRV and its spectral components during sleep [8, 14, 29]. In a recent paper, 9 patients with OSA before and after *4 years of nasal CPAP were studied [12]. No statistical differences in terms of spectral components of HRV during sleep were found, although the lack of significance might be related to the small sample size. This is the first study to analyze HRV measures across sleep stages in a noteworthy sample of OSA patients (n = 30) after long-term CPAP therapy. Our study also used cross-spectral analysis to study the relationship between the respiratory cycle and the HRV. We found that the HF modulations are in intimate coherence with the respiration, and that CPAP treatment increases this coherence, as previously described in obese OSA subjects [25]. This study has several strengths. Only patients that were fully compliant with the CPAP therapy were selected. CPAP compliance was measured objectively by means of a built-in memory that stored the time at which the CPAP unit was applied at the prescribed pressure, in contrast to previous studies which used self-reported estimated use [18] or did not specify compliance [29]. Also, HRV analyses during sleep were performed excluding arousals, hypopneas, RERAs, and apneas (which can alter the HRV analysis), and across sleep stages, while previous studies did not differentiate among sleep stages. And fourth, we
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used a regression analysis to study which variable had influence on the observed HRV differences. Another appealing finding of the present study is that changes in the LF spectral component during REM and non-REM sleep were correlated with the BMI, the AHI, the minimal SatO2 and the mean SatO2; however, when all these variables were introduced in a regression model, only the mean SatO2 (and not the differences in BMI, hypertension or AHI) explained the increased LF modulations during sleep in OSA. These results are consistent with fact that muscle sympathetic nerve activity is chronically elevated in patients with OSA [18], probably as a direct consequence of nocturnal hypoxemia [32] independent of the obesity status or the AHI. Our findings which show that changes in HRV during sleep after long-term CPAP are not associated with BMI is in accordance with a recent report [12]. Conversely, previous studies have observed that impairment of HRV in OSA is more severe if subjects also have obesity [28]. This discrepancy may be explained by the fact that the previous studies that found differences of HRV depending on the BMI did not perform regression analysis, so that their results may be cofounded by other variables, such as the AHI and SatO2, as obese subjects generally exhibit increased AHI and decreased nocturnal SatO2. Some pieces of research showed beneficial effects of CPAP therapy in cardiovascular autonomic tone during daytime, rather than during sleep [18, 19], whereas we found this improvement only during sleep. Our results would support the notion that CPAP treatment contributes to the resetting of cardiovascular reflexes and cardiorespiratory physiology during sleep, rather than during wakefulness. This is in keeping with the fact that OSA patients have a peak in sudden cardiac death during sleeping hours
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[5]. Long-term CPAP therapy may, therefore, have the potential to prevent sudden death in OSA, although more studies are needed to confirm this. It is known that HRV during sleep can be altered due to fluctuations mediated by respiratory changes in intrathoracic pressure. This was the main reason to exclude periods with apneas in our analysis. Our results support the importance of hypoxia per se, separately from respiratory phenomena in the abnormalities of cardiac autonomic modulation during sleep as seems to be the case in patients with sleep-related hypoxia with no apneas (i.e., sleep-related hypoventilation) [22]. These findings suggest that, from a clinical point of view, when OSA patients are submitted to CPAP treatment, an appropriate increase in nocturnal SatO2 concurrently with a decrease in AHI are necessary to revert the changes in cardiac autonomic modulation seen before CPAP. Patients with OSA frequently have other comorbidities. The main limitation of this work is that our sample was very homogeneous, free from conditions such as obesity, hypertension, or diabetes. Even mild hyperglycemia has been shown to influence HRV in OSA [26]. Further, very long-term prospective studies must study the effect of OSA comorbidities in cardiac autonomic modulation, taking into account the different phenotypes of OSA [21]. In conclusion, long-term treatment with CPAP in OSA patients reduced the sympathovagal imbalance and increased parasympathetic modulations to the heart, both during REM and non-REM sleep. Long-term CPAP treatment might therefore have potential to reduce the risk for death in this population, although more studies are needed to confirm this. From a therapeutic point of view, our results suggest that CPAP treatment exerts its changes in cardiac autonomic modulation by decreasing the burden of nocturnal hypoxia. Therefore, when assessing the efficacy of CPAP treatment, not only the apnea and hypopnea index but also the nocturnal oxygen saturation should be closely evaluated. Acknowledgments This work was funded in part by Asociacion de Amigos de la Universidad de Navarra. Conflict of interest The authors declare that they have no conflicts of interests.
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