Impaired blood pressure control in children with obstructive sleep apnea

Impaired blood pressure control in children with obstructive sleep apnea

Sleep Medicine 14 (2013) 858–866 Contents lists available at SciVerse ScienceDirect Sleep Medicine journal homepage: www.elsevier.com/locate/sleep ...

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Sleep Medicine 14 (2013) 858–866

Contents lists available at SciVerse ScienceDirect

Sleep Medicine journal homepage: www.elsevier.com/locate/sleep

Original Article

Impaired blood pressure control in children with obstructive sleep apnea Lisa M. Walter a,⇑, Stephanie R. Yiallourou a, Anna Vlahandonis a, Scott A. Sands a,b, Candice A. Johnson a, Gillian M. Nixon a,c, Margot J. Davey a,c, John Trinder d, Adrian M. Walker a, Rosemary S.C. Horne a a

The Ritchie Centre, Monash Institute of Medical Research, Monash University, Melbourne, Australia Division of Sleep Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA c Melbourne Children’s Sleep Centre, Monash Children’s, Monash Medical Centre, Melbourne, Australia d Discipline of Psychological Sciences, University of Melbourne, Melbourne, Victoria, Australia b

a r t i c l e

i n f o

Article history: Received 16 October 2012 Received in revised form 22 January 2013 Accepted 25 January 2013 Available online 13 June 2013 Keywords: Pediatric Sleep Baroreflex sensitivity Heart period delay Blood pressure variability Sleep-disordered breathing Sleep apnea

a b s t r a c t Background: Obstructive sleep apnea (OSA) in adults has been associated with hypertension, low baroreflex sensitivity (BRS), a delayed heart rate response to changing blood pressure (heart period delay [HPD]), and increased blood pressure variability (BPV). Poor BRS may contribute to hypertension by impairing the control of blood pressure (BP), with increased BPV and HPD. Although children with OSA have elevated BP, there are scant data on BRS, BPV, or HPD in this group. Methods: 105 children ages 7–12 years referred for assessment of OSA and 36 nonsnoring controls were studied. Overnight polysomnography (PSG) was performed with continuous BP monitoring. Subjects were assigned to groups according to their obstructive apnea–hypopnea index (OAHI): primary snoring (PS) (OAHI 61 event/h), mild OSA (OAHI > 1–65 events/h) and moderate/severe (MS) OSA (OAHI > 5 events/h). BRS and HPD were calculated using cross spectral analysis and BPV using power spectral analysis. Results: Subjects with OSA had significantly lower BRS (p < .05 for both) and a longer HPD (PS and MS OSA, p < .01; mild OSA, p < .05) response to spontaneous BP changes compared with controls. In all frequencies of BPV, the MS group had higher power compared with the control and PS groups (low frequency [LF], p < .05; high frequency [HF], p < .001). Conclusions: Our study demonstrates reduced BRS, longer HPD, and increased BPV in subjects with OSA compared to controls. This finding suggests that children with OSA have altered baroreflex function. Longitudinal studies are required to ascertain if this dampening of the normal baroreflex response can be reversed with treatment. Ó 2013 Elsevier B.V. All rights reserved.

1. Introduction Sleep-disordered breathing (SDB) describes a group of disorders characterized by abnormalities of respiratory pattern. Obstructive SDB, subsequently referred to as SDB, ranges in severity from primary snoring (PS), in which the snoring is not accompanied by any gas exchange abnormalities or sleep disruption to obstructive sleep apnea (OSA). OSA is associated with hypoxia, hypercapnia, and/or repeated arousals from sleep. PS is a common childhood condition occurring in 3–15% of children, with 1–4% of children diagnosed with OSA [1]. Until recently only OSA had been associated with elevated blood pressure (BP) in children [2,3], and this elevated BP has been linked to a higher left ventricular (LV) mass [2,4] and LV diastolic dysfunction [5]. More recently studies have identified that children with PS also have elevated BP [6–8]. ⇑ Corresponding author. Address: The Ritchie Centre, P.O. Box 5418, Clayton, Victoria 3168, Australia. Tel.: +61 3 9594 5474; fax: +61 3 9594 6811. E-mail address: [email protected] (L.M. Walter). 1389-9457/$ - see front matter Ó 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.sleep.2013.01.015

The pathogenic effects of SDB on cardiovascular health in adults including the development of hypertension are believed to be mediated by elevated sympathetic nervous system activity and poor baroreflex function [5,9,10]. The baroreflex is responsible for short-term BP control and regulates heart rate on a beatto-beat basis via rapid-onset parasympathetic activity that elicits a change in heart rate within 200–600 ms [11]. However, despite the fundamental importance of this reflex, no studies have investigated how OSA might affect the time delay between the change in BP and the subsequent change in heart rate, known as the heart period delay (HPD). Impaired baroreflex function reduces both the normal baroreflex restraint on sympathetic activity and baroreflex enhancement of vagal activity that controls heart rate [12], which is reflected in major changes in HPD [10]. Prolonged HPD during decreased parasympathetic activity may contribute to an unstable regulation of heart rate [10], and the degree of variability in HPD may define an unstable blood pressure–heart rate interaction [13]. It has been

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demonstrated that abnormal blood pressure variability (BPV) is a prognostic indicator of cardiovascular morbidity even in the absence of hypertension [14]. Studies in adults with OSA have reported depressed baroreflex sensitivity (BRS) [15] and increased BPV [16]. There is a circadian variation in BP across the night in adults [17]. BP normally decreases at sleep onset and continues to progressively fall until approximately 3:00 AM when it begins to rise again throughout the early hours of the morning prior to wakening [17]. It also has been identified that the normal circadian rhythm of sympathetic vasomotor control across the sleep period observed in healthy adults [18] is altered in adults with hypertension [19]. The rationale for our study was that despite the prevalence of SDB in children, there have only been two studies in children that examined BRS and BPV during sleep. However, these studies only examined children with OSA and did not include children with PS, who make up the majority of snoring children [20,21]. To date, there have been no studies in children examining the effect of SDB on HPD. Thus, the aim of our study was to perform a comprehensive comparison of BRS, HPD, and BPV in children across the range of severities of SDB and nonsnoring control children. Our analysis was performed across the whole night of sleep taking into account the effects of sleep stage and the length of time asleep. We hypothesized that, similar to adults, children would have decreased BRS and increased BPV relative to SDB severity. We also hypothesized that compared with controls, children with SDB would have a longer HPD response to spontaneous changes in BP.

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dominant hand as previously described [6]. The Finometer™ has an inbuilt self-calibration system (physiocal) that is activated at the start of each BP measurement; calibration is affected through an automated algorithm that adjusts the transmural pressure of the artery to equal zero. The BP signal was simultaneously digitized with the other polygraphic recordings. All electrophysiologic signals were recorded using a commercially available PSG system (S-Series or E-Series, Compumedics, Melbourne, Australia). Electrodes for recording EEG (Cz, C4–A1, C3–A2, O2–A1, O1–A2), left and right EOG, submental electromyogram, left and right anterior tibialis muscle electromyogram, and electrocardiogram were attached. Thoracic and abdominal breathing movements were detected using respiratory inductance plethysmography (Pro-Tech zRIP™ Effort Sensor, Pro-Tech Services Inc., Mukilteo, WA, USA). Transcutaneous carbon dioxide (TcCO2 TCM4/40, Radiometer, Denmark, Copenhagen), nasal pressure using a nasal cannula (Salter StyleÒ, Salter Labs, Arvin, CA, USA), and oronasal airflow using a thermistor (SandmanÒBreathSensor™, Child Airflow Thermistor, Tyco Healthcare, UK) also were recorded. The 3-prong airflow thermistor was affixed under the child’s nose and monitors combined nasal and oral airflow. Pulse oximeter oxygen saturation (SpO2) was measured using Masimo RadicalÒ SET, (Masimo Corp., CA, USA) set to 2-s averaging time. Following the PSG study, data were transferred via European data format to data analysis software (Chart 6, ADInstruments, Sydney, Australia) for detailed cardiovascular analysis.

2. Methods Ethical approval for our study was granted by the Southern Health (05052C) and Monash University (2005/459) human research ethics committees. Written informed consent was obtained from parents and verbal assent from children prior to commencement of the study. No monetary incentive was provided for participation. 2.1. Subjects One hundred and five children ages 7–12 years referred to the Melbourne Children’s Sleep Centre for assessment of suspected SDB were recruited. In addition, 36 control subjects with no history or parental concerns of snoring were recruited from the community. The effects of SDB on overnight BP [6], heart rate variability [22], sleep quality [23,24], behavior and neurocognition [25,26], and cardiovascular variability during periodic leg movements in sleep [27] have been previously published from this cohort. All subjects were otherwise healthy apart from 22 children diagnosed with asthma, who were not on long-acting bronchodilators and did not use short-acting bronchodilators within 24 h of the study. Children with conditions known to affect sleep, breathing, BP, or neurocognitive functioning, as well as children taking medications known to affect sleep, breathing, BP, or neurocognitive functioning were not recruited. 2.2. Protocol All subjects underwent one night of routine pediatric polysomnography (PSG) following a thorough medical examination performed by a pediatrician. Weight and height were recorded and converted to a body mass index (BMI) z score to adjust for gender and age [28]. Overnight BP was measured using a Finometer™ (Finometer, FMS, Finapres Medical Systems, the Netherlands) which provided a noninvasive continuous measure of BP using a photoplethysmographic cuff placed on the middle finger of the

2.3. Clinical analysis of PSG data All subjects needed a minimum of 4 h of sleep to be included in the study. All PSG studies were manually sleep staged in 30-s epochs by experienced pediatric sleep technologists according to criteria [29] that were standard at the time of scoring into wake before sleep onset, nonrapid eye movement sleep stages 1 and 2 (NREM1, NREM2), slow-wave sleep (SWS), and rapid eye movement (REM) sleep. Concordance for our laboratory has previously been assessed as 87% (interscorer) and 89% (intrascorer) based on sleep staging, respiratory disturbance, and arousal indices [30]. Respiratory events P2 respiratory cycles in duration were scored. Obstructive apnea and hypopnea scoring was based on the American Academy of Sleep Medicine (AASM) criteria [31] with minor modifications. Obstructive apneas required a decrease to <10% of baseline in flow signal with continued or increased respiratory effort. There was no requirement for the event to terminate with either P3% SpO2 desaturation or arousal. Obstructive hypopneas required a clear reduction from baseline in flow signal, continued respiratory effort with paradox or phase shift, the presence of snoring or noisy breathing, and an associated arousal or P3% SpO2 desaturation. The obstructive apnea–hypopnea index (OAHI) was defined as the total number of obstructive apneas, mixed apneas, and obstructive hypopneas per hour of total sleep time (TST). Criteria for the categorization of SDB severity mirrored current clinical practice: subjects were classified as having PS (OAHI 6 1 event/h); mild OSA (OAHI between >1–65 events/h); or moderate/ severe (MS) OSA (OAHI > 5 events/h). Other calculated variables included the central apnea–hypopnea index (CnAHI), sleep efficiency, arousal index (ArI), and SpO2 nadir. The CnAHI was defined as the total number of central apneas and hypopneas per hour of TST. Sleep efficiency was defined as a percentage of the time available for sleep. The ArI was defined as the total number of cortical and subcortical spontaneous, respiratory, and periodic leg movement arousals per hour of TST.

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2.4. Data analysis Within each sleep stage, beat-to-beat data were obtained from every 3-min epoch from the entire overnight recording only excluding those epochs containing respiratory events (e.g., central and obstructive apneas and hypopneas), movement artifact, sighs, snoring, or change of sleep stage. Because there were not sufficient 3-min epochs with uninterrupted signals, in which heart rate and blood pressure signals were not disrupted by movement artifact in wake and NREM1 suitable for inclusion, analyses were only performed on NREM2, SWS, and REM. Analysis was performed using MATLAB (Mathworks; Natick MA). Systolic BP and heart period interval were determined via peak detection. Beat-to-beat data for systolic BP and heart period were resampled (200 Hz, cubic interpolation) to achieve a continuous equidistantly spaced time series. 2.4.1. Baroreflex sensitivity BRS was calculated using cross-spectral analysis, a technique based on the principal that spontaneous oscillations in systolic BP elicit oscillations at the same frequency in relative risk intervals via the arterial baroreflex [32]. This technique has been widely used in adults [33], children [20,34], and infants [35,36]. BRS was calculated using a cross-spectral analysis protocol for BRS analysis as previously described [35,36]. Briefly cross-spectral analysis utilizes the transfer function which calculates the gain, coherence, and phase delay. The gain, as a measure of BRS, represents the expected amplitude of oscillation in heart period due to a 1-mmHg oscillation in systolic BP [33]. The coherence represents the frequency-dependent correlation between the systolic BP and heart period oscillations, and the phase lag represents the time delay between systolic BP and heart period oscillations. BRS was calculated as the gain at the frequency of maximum coherence between the systolic BP and heart period within the low-frequency (LF) range (0.04–0.15 Hz), which is considered to be the frequency range in which baroreflex-mediated oscillations primarily occur [37]. Values with a negative delay were excluded, as negative delay indicates that the baroreflex was unlikely to have sufficiently contributed to the oscillation in heart period during this period of time to be considered for analysis and thus removes centrally mediated change. 2.4.2. Heart period delay HPD was calculated from the phase lag taken from the transfer function at the frequency of maximum coherence. The phase lag represents the time delay between the change in BP and the corresponding change in heart rate. As this lag varies with frequency, in even the simplest systems we needed to address the possibility that differences in HPD between groups were not due to a confounding difference in the frequency that the delay was measured. That is, differences in delay were indeed the reflection of slower or faster baroreflex function. No effect between groups or conditions on the frequency of maximum coherence implies that reduced HPD is the result of a slower baroreflex. 2.4.3. Blood pressure variability Power spectral analysis of BP separates systolic BP oscillations into high frequency (HF) and LF components for BPV analysis. The LF component of BPV is considered to reflect sympathetic vasomotor modulation [38,39]. Although the HF range of BPV is dominated by direct mechanical effects of ventilation and intrathoracic pressure swings of 5 cmH2O amplitude (3 mmHg) are typical, it also is influenced by parasympathetically mediated changes in cardiac stroke volume and relative risk interval [38–40]. Beat-to-beat data for systolic BP were analyzed using power spectral analysis to determine BPV. The 3-min epochs were detr-

ended and subdivided into four overlapping segments with 75% overlap and a Hamming window was used to reduce spectral leakage. LF (0.04–0.15 Hz), HF (individualized for each epoch based on each child’s respiratory rate and defined by the 90th and 10th centiles of the respiratory rate), and total power for each epoch were calculated from the area under the power spectral density functions in the appropriate frequency range. These power values represent the square of the peak amplitude of an oscillatory signal in the frequency range; for example, a BPV in the LF range of 4 mmHg2 can be interpreted as a 2-mmHg peak oscillation in the LF band. 2.5. Statistical analyses Statistical analyses were performed using SPSSÒ (IBMÒ Statistics version 20). Data were first tested for normality and equal variance. Demographic data, polysomnographic characteristics, and the frequency of maximum coherence were compared between SDB severity groups using Kruskal–Wallis one-way analysis of variance (ANOVA) on ranks followed by Dunn post hoc analyses if abnormally distributed or by one-way ANOVA with NewmanKeuls post hoc analysis if normally distributed. BRS, HPD, and BPV data were analyzed (1) across the whole night of sleep; (2) in each hour of the sleep period across the night from sleep onset regardless of sleep stage; and (3) in the different sleep stages NREM2, SWS, and REM. To determine if the length of time asleep had an effect on BRS, HPD, and BPV, the first 6 h of each child’s sleep period was divided into hourly intervals. This period was chosen because 97.5% of subjects had P6 h sleep period time, whereas after this time the number of subjects who remained asleep was significantly reduced. Time into the sleep period and SDB severity comparisons were performed using a mixed-model linear regression analysis. Autocorrelation was tested prior to mixed-model linear regression using the Durbin–Watson test, which specifically tests if adjacent residuals are correlated. The Durbin–Watson statistic was close to two in all cases, indicating that the adjacent residuals were unlikely to be correlated. SDB severity and sleep stage comparisons of BRS, HPD, and BPV were performed using univariate ANOVA with Bonferroni post hoc tests. To verify that changes in BRS were not solely attributable to any underlying prehypertension in these subjects, the analysis was repeated covarying for systolic BP. Additionally, to verify that changes in BRS, HPD, and BPV were independent of obesity, the analysis was repeated covarying for BMI z score. Linear regression analysis was performed to determine significant predictors of BRS with BRS entered as the dependant variable and OAHI, age, and BMI z score entered as the independent variables. Data are presented as mean ± standard error of the mean with p < .05 considered to be significant. 3. Results One hundred and forty-one pediatric subjects (control, n = 36; PS, n = 61; mild OSA, n = 23; MS OSA, n = 21) were included in the study. A mean of 39 ± 2 three-minute epochs (NREM2, 17 ± 1 epochs; SWS, 12 ± 1 epochs; REM, 10 ± 1 epochs) were analyzed per subject. Demographic and PSG data are presented in Table 1. There were no group differences in age between the severity groups. Subjects in the MS OSA group had a higher BMI than the other three groups (p < .05 for all). As expected the OAHI, ArI, and SpO2 nadir differed between the groups. There was no difference in CnAHI between the groups. TST and sleep efficiency were significantly lower in the MS OSA group compared to controls (p < .05 for both). There was no difference in the amount of time spent in NREM1, NREM2, SWS, or REM between the four groups.

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L.M. Walter et al. / Sleep Medicine 14 (2013) 858–866 Table 1 Demographic and polysomnographic characteristics of controls and children with sleep-disordered breathing.

Gender Age (y) BMI z score OAHI (h/TST) CnAHI ArI SpO2 nadir TST (min) Sleep efficiency (%) NREM1 (%) NREM2 (%) SWS (%) REM (%) WASO (%) Wake HR (bpm) Sleep HR (bpm)

Control (n = 36)

PS (n = 61)

Mild OSA (n = 23)

MS OSA (n = 21)

18 M/18 F 9.6 (1.7) 0.5 (0.9) 0.1 (0.2) 0.6 (0.5) 11.8 (3) 93.8 (4) 413 (44) 86 (8) 10 (4) 47 (5) 25 (5) 18 (4) 11 (7) 83 (2) 73 (2)

42 M/19 F 9.8 (1.6) 0.5 (1.2) 0.3 (0.3) 0.6 (1.1) 11.8 (4) 93.4 (3) 395 (51) 83 (10) 8.0(4) 50 (5) 24 (5) 18 (4) 11 (9) 86 (1) 75 (1)

12 M/11 F 9.0 (1.3) 0.5 (1.0) 2.4 (1.0)*,à 0.8 (0.9) 14.6 (4)à 92.6 (3) 396 (43) 82 (8) 10(3) 46 (5) 27 (6) 17 (4) 12 (7) 86 (2) 76 (2)

8 M/13 F 9.3 (1.7) 1.4 (1.0) *, ,à 16.6 (12.4)*,à 0.8 (0.9) 24.7 (4)*, ,à 86.4 (6)*,à,  361 (58)* 79 (10)* 11(4) 47 (6) 25 (6) 17 (7) 14 (10) 92 (2)* 81 (2)*

Abbreviations: PS, primary snoring; MS OSA, moderate/severe obstructive sleep apnea; y, years; BMI, body mass index; OAHI, obstructive apnea–hypopnea index; TST, total sleep time; CnAHI, central apnea–hypopnea index; ArI, arousal index; SpO2, pulse oximeter oxygen saturation; NREM, nonrapid eye movement; SWS, slow-wave sleep; REM, rapid eye movement; WASO, wake after sleep onset; HR, heart rate. * p < .05 vs control.   p < .05 vs mild OSA. à p < .05 vs PS.

Heart rate during both wake and sleep was higher in the subjects with MS OSA compared with the control group (p < .05 for both). We have previously reported that mean arterial pressure was significantly elevated in the SDB groups compared to controls during both wake before sleep onset (controls, 62 ± 2 mmHg; PS, 74 ± 2 mmHg; mild OSA, 69 ± 3 mmHg; MS OSA, 71 ± 4 mmHg; p < .05 for all), and during overnight sleep (controls, 63 ± 1 mmHg; PS, 69 ± 1 mmHg; mild OSA, 71 ± 2 mmHg; MS OSA, 69 ± 2; p < .05 for all) [6].

3.1. Frequency of maximum coherence To reiterate, this analysis was performed to demonstrate that any change in HPD was due to a change in baroreflex properties and not simply a reflection of the typical frequency of cardiovascular stimulation that occurred between the groups. Therefore, the frequency of maximum coherence at which the HPD was measured also was compared between the SDB severity groups. There was no statistical difference between the groups for the frequency of maximum coherence during sleep (controls, 0.092 ± 0.001 Hz; PS, 0.093 ± 0.001 Hz; mild OSA, 0.087 ± 0.005 Hz; MS OSA, 0.094 ± 0.002 Hz; p = .9). This lack of difference implies that reduced HPD is the result of a slower baroreflex.

3.2. Effect of the time into the sleep period The effect of the time into the sleep period for BRS and HPD is presented in Fig. 1. Overall, there was a significant effect of time into the sleep period for BRS (p < .01; Fig. 1A) reflected in a trend for increased BRS with increased time into the sleep period in all of the SDB severity groups; however, this increase only reached significance in the control group (p < .05). There also was a significant overall time effect related to SDB severity (p < .01). Across the sleep period, the control group had significantly higher BRS compared with the mild OSA (p < .05) and MS OSA groups (p < .01). Subjects with PS also had significantly higher BRS compared with subjects in the mild and MS OSA groups (p < .05 for both), but there was no difference in BRS between the mild and MS OSA groups. There was no significant effect of time into the sleep period or SDB severity group for HPD (Fig. 1B).

Fig. 1. Baroreflex sensitivity (BRS) (A) and heart period delay (HPD) (B) in children ages 7–12 years assessed by cross-spectral analysis in control children, and children with primary snoring, mild obstructive sleep apnea (OSA) and moderate/severe OSA in 1-h periods across the total sleep period. The BRS significantly increased (p < .05) across the sleep period in the control group but not in the sleep-disordered breathing (SDB) groups. There was no significant difference in any SDB severity group for HPD across the sleep period. Symbols represent significant differences between SDB severity groups across the sleep period. ⁄p < .05, ⁄⁄p < .01. Data presented as mean and standard error of the mean.

Results of the analysis related to time into the sleep period for BPV are presented in Fig. 2. LF BPV was not affected by time into the sleep period or SDB severity group (Fig. 2A). HF BPV was not affected by time into the sleep period but was significantly affected by the SDB severity group (p < .001; Fig. 2B). The subjects with MS OSA had significantly higher HF BPV across the sleep period com-

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pared with the other SDB severity groups (p < .05 for all). Total BPV power also was not affected by time into the sleep period (Fig. 2C). However, there was an overall effect related to SDB severity group (p < .001). Total BPV power also was significantly higher in the MS group across the sleep period compared with the other SDB severity groups (control and PS, p < .001; mild OSA, p < .05). Similarly the LF/HF ratio was not affected by time into the sleep period, but there was an effect of SDB severity (p < .01; Fig. 2D). Again, the MS group had significantly lower LF/HF BPV ratios compared with the other SDB severity groups (p < .05 for all).

Fig. 2. Blood pressure variability in children ages 7–12 years assessed by power spectral analysis for low-frequency (LF) power (A), high-frequency (HF) power (B), total power (C), and LF/HF ratio (D), in control children and children with primary snoring, mild obstructive sleep apnea (OSA), and moderate/severe OSA in 1-h periods across the total sleep period. There were no significant differences for all BPV measures in any sleep-disordered breathing (SDB) severity group across the sleep period. Symbols represent significant differences between SDB severity groups. ⁄p < .05. Data presented as mean and standard error of the mean.

3.3. Effect of SDB severity and sleep stage The effects of SDB severity and sleep stage (NREM2, SWS, and REM) on BRS and HPD, are presented in Fig. 3. During all sleep stages, BRS in subjects with MS OSA was significantly lower compared with the control (p < .01) and PS groups (NREM2 and SWS, p < .05; REM, p < .01; Fig. 3A). The mild OSA group also had significantly lower BRS compared with the control and PS groups during NREM2 (p < .05 for all) and REM (control, p < .01; PS, p < .05), and compared with the controls during SWS (p < .05). However, within any group there were no significant differences between sleep stages. Further analyses covarying for systolic BP did not change the results. HPD was not significantly different between groups during NREM2 and SWS (Fig. 3B). During REM sleep the subjects with MS OSA had significantly higher HPD compared with the control subjects (p < .05). There were no significant differences between sleep stages for HPD in any group. The effects of SDB severity and sleep stages on BPV are presented in Fig. 4. LF BPV was not significantly different between groups during either NREM2 or SWS (Fig. 4A). During REM the subjects with mild and MS OSA had significantly higher LF BPV compared with both the control and PS groups (mild OSA, p < .05 for both; MS OSA, p < .01 for both). LF BPV was significantly lower during SWS compared to REM sleep in the control (p < .05), and NREM2 in the PS groups (p < .05; Fig. 4B). LF BPV also was significantly lower during SWS compared to both NREM2 and REM

Fig. 3. Baroreflex sensitivity (BRS) (A) and heart period delay (HPD) (B) in children ages 7–12 years assessed by cross-spectral analysis in control children and children with primary snoring, mild obstructive sleep apnea (OSA), and moderate/severe OSA during nonrapid eye movement sleep stage 2, slow-wave sleep, and rapid eye movement sleep. There were no significant differences between sleep stages for either BRS or HPD in any sleep-disordered breathing (SDB) severity group. Symbols represent significant differences between SDB severity groups within each sleep stage. ⁄p < .05, ⁄⁄p < .01. Data presented as mean and standard error of the mean.

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Fig. 4. Blood pressure variability in children ages 7–12 years assessed by power spectral analysis for low-frequency (LF) power (A, B), high-frequency (HF) power (C, D), total power (E, F), and LF/HF ratio (G, H) in control children and in children with primary snoring, mild obstructive sleep apnea (OSA), and moderate/severe OSA during nonrapid eye movement sleep stage 2, slow-wave sleep, and rapid eye movement sleep. Graphs represent the same data set displayed to highlight the effect of sleep-disordered breathing severity (A, C, E, and G) and the effect of sleep stage (B, D, F, and G). ⁄p < .05, ⁄⁄p < .01, ⁄⁄⁄p < .001. Data presented as mean and standard error of the mean.

(p < .001 for both) in the mild OSA group. There were no significant differences in the MS OSA group between sleep stages. HF BPV was significantly higher in the MS group compared with the other SDB severity groups during all sleep stages (p < .001 for all; Fig. 4C). There were no differences in HF BPV between sleep stages in the control, mild OSA, or MS OSA groups (Fig. 4D). In

the PS group, the HF BPV was significantly higher during SWS compared to REM (p < .05). Similarly for total BPV power the subjects with MS OSA had significantly higher power compared with the other SDB severity groups during all sleep stages (control and PS, p < .001 for all; mild OSA, p < .05 during REM and SWS; Fig. 4E). Total BPV power also

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was significantly higher in the mild OSA group compared with the control group during NREM2 (p < .05). Total BPV power was not significantly different between the sleep stages for any severity group (Fig. 4F). The LF/HF BPV ratio was significantly lower in subjects with MS OSA compared with the other groups during all sleep stages (control = p < .01 during NREM2 and REM, p < .05; during SWS; PS and mild OSA = p < .05 for all sleep stages) (Fig. 4G). The LF/HF BPV ratio did not differ between the sleep stages for the control, PS, and MS OSA groups (Fig. 4H). In the mild OSA group, the LF/HF BPV ratio was significantly lower during SWS compared with during NREM2 and REM (p < .05 for both). 3.4. Determinants of BRS During the analysis periods of total sleep (i.e., NREM2, SWS, REM) the value of OAHI was a significant negative predictor of BRS (total sleep = STD ß, 0.22; model R2, 0.04; p < .001; NREM = STD ß, 0.18; model R2, 0.02; p < .05; SWS = STD ß, 0.25; model R2, 0.04; p < .01; REM = STD ß, 0.25; model R2, 0.06; p < .05). Age and BMI z score were not significant determinants of BRS during either total sleep or any sleep stage. 4. Discussion Our study in subjects aged 7–12 years has confirmed our hypotheses that BRS is depressed and BPV is elevated in children with mild or MS OSA. We also have shown that an increase in BRS across the sleep period observed in the control subjects is abrogated in subjects with PS and those with either mild or MS OSA. Importantly, we have demonstrated that children with any severity of SDB including PS have a delayed HPD to changes in BP. We have previously demonstrated that BP in these subjects is elevated by 10 mmHg compared to controls [6]. Although these BP levels do not represent clinical hypertension, these subjects do nevertheless display significantly abnormal autonomic BP control. Analysis of BRS in subjects with SDB as a function of the time in hours into the sleep period identified an increase in BRS with time into the sleep period in the control subjects only. Further, this increase was not found in the subjects with SDB irrespective of severity. To date there have only been two other investigations of BRS during sleep in pediatric subjects with OSA, and these investigations were from the same research group; the later study focused on the effect of adenotonsillectomy [20,21]. Our findings are similar to the first study [20] which analyzed BRS in the time domain in children aged 7–13 years and reported an increase in BRS as the time into the sleep period progressed in the control and mild OSA (obstructive index >1 and <5 events/h) groups; this result was not apparent in children with MS OSA (obstructive index 65 events/h). However, in contrast to our study when BRS was examined in the frequency domain no increase was observed in LF BRS across the night in any group [20]. However, taken together these studies suggest that children with OSA are not able to maintain the increase in BRS that normally occurs as the night progresses. BRS was significantly lower in the subjects with MS OSA in all sleep stages compared with controls and subjects with PS. These findings are consistent with studies in adults that also report significantly lower BRS in subjects with OSA; however, the sleep stage in which the reduction in BRS occurs differs between studies [15,41]. Our results concur with McConnell et al. [20] who also demonstrated that children with severe OSA had significantly lower BRS during all sleep stages compared with control and mild OSA children. Our study differed from McConnell et al., as they used

sleep stage as a time-varying covariate in their analysis of the effect of SDB severity, which did not allow for an assessment of the effect of sleep stage alone. We analyzed sleep stage as a separate factor to overcome this limitation and showed that there was no effect of sleep stage on BRS. Studies in adults have been conflicting, with some studies reporting that BRS is higher during REM compared to NREM sleep both spontaneously [42] and in response to pharmacologic pressor stimuli [43] and spontaneous pressor events [42,44]. In contrast, other studies reported no difference in BRS between sleep stages in response to spontaneous pressor [45] or depressor events [44]. The only other studies of BRS in children with OSA were performed while awake but also demonstrated reduced BRS [46,47]. The reduction in BRS both awake and during sleep is likely to reflect impairments in both the vagal and sympathetic components of heart rate control, particularly a reduction in vagal outflow, as age-related increases in BRS are directly proportional to cardiovagal activity [48]. There also is a contributory increase in sympathetic activity, which is a forerunner to cardiovascular disease [12]. These findings are suggestive of a possible long-term morbidity in these children, as the pathogenic effects of SDB on cardiovascular health in adults are believed to be mediated by elevated sympathetic nervous system activity and diminished baroreflex function [5,9,10]. In a short-term period, children with OSA already have elevated blood pressure [2,3], a higher LV mass [2,4], and LV diastolic dysfunction [5]. The mechanism underlying these maladaptive cardiovascular sequelae of OSA in children and the role of a dampened baroreflex response have yet to be elucidated. A novel aspect of our study was the analysis of the beat-to-beat heart rate response delay to changes in BP. Our study confirms our hypothesis that subjects with any severity of SDB have a prolonged HPD compared with controls, providing further evidence of decreased vagal activity in these children. When HPD is analyzed using cross-spectral analysis, the phase lag calculated from the transfer function between the systolic BP and the heart period changes is composed of a contribution from the pure delay (due to time lags in the pressure wave reaching the baroreceptors, receptor depolarization and action potentials traveling along nerves) of the baroreflex system in addition to a response lag (ascribed to time constraints such as the gradual uptake or release of neurotransmitters) of the system [35,49,50]. Therefore, there may be a slight overestimation of the pure delay. However, as all groups were similarly analyzed, this overestimation will not affect the differences in HPD identified between the severity groups. The mean difference in the HPD we identified in the subjects with SDB compared with controls was 3 s. Although this time is somewhat longer than the time frame of 200–600 ms, which represents parasympathetic activity, a 3-s delay still suggests that the difference arose from changes in the more rapidly acting vagal inhibition of heart rate rather than the slower acting sympathetic arm of the autonomic outflow [11]. In support of the HPD being mediated by vagal activity, the delay between acetylcholine release and depolarization of the sinoatrial node (the effector response) has been estimated to represent two-thirds of the baroreflex latency [51]. Therefore, as the effector response constitutes the majority of baroreflex latency, we speculate that the primary contributor to the observed HPD changes reflects impaired vagal activity. In addition as there was no statistically significant difference in the frequency of maximum coherence between the SDB severity groups, we are confident that the differences in HPD reflect the changes in baroreflex timing between the groups. We have previously identified in this cohort that subjects with all severities of SDB including PS have elevated BP compared to the control subjects [6]. Our analysis has identified that the children with PS, similar to the children with the more severe OSA, also have a prolonged HPD suggestive of impaired parasympathetic

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activity. In adults it has been demonstrated that cardiac autonomic function is associated with hypertension and that reduced vagal function is associated with a risk for developing hypertension [52]. Studies in children with SDB also have demonstrated reduced vagal tone [53]. Children with PS often are currently not treated; therefore, vagal impairment in these children has the potential to predispose them to future development of hypertension. Lin and Guilleminault [54] have reported that the AASM criteria for scoring hypopneas markedly underscores hypopneas in comparison to other scoring protocols such as that used by Stanford Sleep Disorders Clinic, which raises the possibility that the PS group had unrecognized OSA. In our study scoring of hypopneas only required a clear reduction in airflow from baseline akin to the Stanford Sleep Disorders Clinic’s requirement for a 20% reduction, which is likely to be more sensitive to scoring hypopneas than the AASM criteria. However, more subtle events may still be missed, emphasizing the fact that children with habitual snoring would benefit from clinical monitoring into the future. Our study also has found that SDB severity had a significant effect on BPV in pediatric subjects. Most importantly, we have identified that during sleep subjects with OSA had a 30% increase in LF BPV, and those with MS OSA had a 7-fold increase in HF BPV compared with controls. Elevated LF BPV indicates that children with MS OSA have a higher sympathetic vasomotor modulation of their BP, and higher HF BPV may be due to the direct mechanical influence of the increased respiratory effort in these subjects. We also found that subjects with MS OSA had lower LF/HF, suggesting that they have reduced sympathovagal balance. Our results are in line with studies in both adults [16] and children [20] with OSA, which have demonstrated that BPV increases relative to increasing disease severity. The observation that children with OSA have increased BPV is in keeping with their decreased BRS. We speculate that as the baroreflex response to changing BP is inhibited, reflex alterations to heart rate, cardiac contractility, peripheral vascular resistance, and venous return may all be diminished, dampening control of BP and increasing its variability. In our study BPV was higher in subjects with OSA even though the periods of time containing respiratory events were not included in the analysis, indicating that the overall variability did not just reflect the swings in BP that occurs during and after a respiratory event. Sleep stage had a significant effect on BPV in the LF range for the control, PS, and mild OSA groups, in which the highest BPV was found during REM sleep and the nadir during SWS. These sleep stage differences were not apparent in the subjects with MS OSA, suggesting the normal sympathetic vasomotor modulation that occurs between the sleep states is impaired in this group. Furthermore, the subjects with MS OSA also had significantly higher LF BPV compared with the other SDB severity groups during REM sleep. A finding suggesting that the normal increase in BPV between SWS and REM seen in the other groups could not be attained by the MS OSA group, as these subjects already have increased sympathetic vasomotor modulation, which could not be increased further by a change in sleep state. In subjects with all severities of SDB there was a trend for the HF component of BPV to be lower during REM sleep compared with the other sleep states. However, this occurrence only reached significance in the PS group indicating decreased vagal activity during REM. This finding is in accordance with adult studies, which also have shown that parasympathetic activity is dominant in NREM compared to REM sleep [55]. There have been no other studies performed in children that compared BPV between the sleep stages. Although BPV was affected by sleep stage, there was no effect of the time into the sleep period, indicating that the differences were sleep-stage driven rather than due to any effect of the time across the night.

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Linear regression analysis was performed to identify potential physiologic determinants of BRS. During the total sleep period and all of the sleep stages, the OAHI was the only negative significant predictor of BRS. The finding that OAHI negatively predicted BRS during sleep is in accordance with the other results of decreased BRS in children with mild or MS OSA. 5. Conclusions We have demonstrated that children with OSA have impaired BRS and a delayed heart rate response to changes in BP, which in children with MS OSA concurrently occurred with increased BPV. Taken together these findings suggest that these children have decreased parasympathetic and increased sympathetic outflow to the heart. Importantly children with PS also exhibited a prolonged HPD, suggesting that even children with milder forms of SDB exhibit autonomic dysfunction. Our results suggest that impairment in autonomic function in children with MS OSA may precede the development of hypertension and supports the hypothesis that low BRS and high BPV may be the pathway that links OSA to its adverse cardiovascular consequences. The role of depressed BRS and elevated BPV in the development of clinically significant hypertension, however, is yet to be elucidated. We suggest that even though they have been exposed to the disease for a relatively short time, children with OSA are at risk for suffering hypertension which may progress to cardiovascular disease in adulthood. Accordingly, our study supports the necessity of early treatment of children with SDB. Funding source Funding for this project was awarded by the National Health and Medical Research Council of Australia (Project Grant No. 384142) and the Victorian Government’s Operational Infrastructure Support Programme. Conflict of interest The ICMJE Uniform Disclosure Form for Potential Conflicts of Interest associated with this article can be viewed by clicking on the following link: http://dx.doi.org/10.1016/j.sleep.2013.01.015.

Acknowledgments The authors would like to thank all of the children and their parents who participated in this study. We also acknowledge the invaluable technical assistance all the staff of the Melbourne Children’s Sleep Centre. References [1] Lumeng JC, Chervin RD. Epidemiology of pediatric obstructive sleep apnea. Proc Am Thorac Soc 2008;5:242–52. [2] Amin R, Somers VK, McConnell K, Willging P, Myer C, Sherman M, et al. Activity-adjusted 24-hour ambulatory blood pressure and cardiac remodeling in children with sleep disordered breathing. Hypertension 2008;51:84–91. [3] Amin RS, Carroll JL, Jeffries JL, Grone C, Bean JA, Chini B, et al. Twenty-four-hour ambulatory blood pressure in children with sleep-disordered breathing. Am J Respir Crit Care Med 2004;169:950–6. [4] Amin RS, Kimball TR, Bean JA, Jeffries JL, Willging JP, Cotton RT, et al. Left ventricular hypertrophy and abnormal ventricular geometry in children and adolescents with obstructive sleep apnea. Am J Respir Crit Care Med 2002;165:1395–9. [5] Imadojemu VA, Gleeson K, Gray KS, Sinoway LI, Leuenberger UA. Obstructive apnea during sleep is associated with peripheral vasoconstriction. Am J Respir Crit Care Med 2002;165:61–6.

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