Graphical abstract
We conducted a randomised, parallel, prospective, controlled trial to evaluate the effect of continuous positive airway pressure (CPAP) on blood pressure (BP) in normotensive individuals with a dipping BP pattern and severe obstructive sleep apnoea (OSA). Participants were randomly assigned to receive either CPAP therapy or usual care for 12 weeks. The primary outcome was the change in ambulatory BP monitoring (ABPM) parameters from baseline to 3 months. Intention-to-treat analysis showed no significant BP changes with CPAP, whereas the usual care group exhibited increases in ABPM parameters, leading to a significant difference in night-time diastolic BP (DBP) between groups. Per-protocol analysis revealed significant differences in all primary end-points except daytime systolic BP (SBP). AHI: apnoea–hypopnoea index. #: statistical significance inter-group: *: statistical significance intra-group. Figure partially created with BioRender.com.
Abstract
Background
The effects of continuous positive airway pressure (CPAP) on blood pressure (BP) in normotensive subjects, particularly among those with a dipping BP pattern, remain uncertain, raising questions about its indication for this group of patients. We assessed the impact of CPAP on BP in normotensive subjects with a dipping BP pattern and severe obstructive sleep apnoea (OSA).
Methods
This was a randomised, parallel, prospective, controlled trial. Inclusion criteria were: age ≥18 years, apnoea–hypopnoea index ≥30 events·h−1, mean 24-h BP <130/80 mmHg and daytime to night-time BP reduction ≥10%. Patients were randomly assigned to receive either CPAP treatment or usual care for 12 weeks. The primary outcome was the change in ambulatory BP monitoring (ABPM) parameters from baseline to the 3-month follow-up.
Results
The 60 patients who completed the follow-up had a mean±sd age of 52.2±10.8 years and 40 (66.7%) were male. The intention-to-treat analysis showed no significant changes with CPAP, whereas the usual care group experienced increases in ABPM parameters. This resulted in a mean difference of −3.4 mmHg (95% CI −6.124– −0.676; p=0.015) in night-time diastolic BP between the groups. The per-protocol analysis indicated significant differences between the CPAP and usual care groups for all primary end-points, except for daytime systolic BP. For night-time systolic BP, the mean difference was −6.052 mmHg (95% CI −10.895– −1.208; p=0.016).
Conclusion
These findings suggest a protective effect of CPAP, highlighting the importance of CPAP prescription for this population to control potential increases in BP and possibly prevent the onset of hypertension.
Shareable abstract
The use of continuous positive airway pressure prevents an increase in blood pressure in normotensive subjects with a dipping blood pressure pattern and severe obstructive sleep apnoea https://bit.ly/4aVCr4v
Introduction
Obstructive sleep apnoea (OSA) is a significant public health concern, affecting over 500 million people worldwide [1]. This prevalent condition is characterised by recurrent episodes of partial or complete upper airway collapse during sleep, leading to intermittent hypoxia, hypercapnia, fluctuations in intrathoracic pressure and sleep fragmentation [2]. Short-term consequences include daytime sleepiness and a reduced quality of life [3], whereas long-term outcomes encompass neurocognitive deficits [4], compromised mental health [5] and notably, an elevated risk of cardiovascular comorbidities [6].
Epidemiological and clinical research suggests that OSA plays a role in the initiation and progression of several cardiovascular diseases [7, 8]. The heightened sympathetic activity induced by OSA leads to a marked increase in blood pressure (BP), especially at night [2]. This results in the loss of the physiological circadian pattern of BP and the development of nocturnal hypertension, both of which are associated with an adverse cardiovascular prognosis, regardless of whether the individuals are normotensive or hypertensive [9].
Treatment with continuous positive airway pressure (CPAP) is considered the primary therapeutic approach for patients with severe and/or symptomatic OSA, effectively preventing upper airway collapse and several of its associated consequences [10]. The effectiveness of CPAP in lowering BP has been widely demonstrated across various contexts, but its impact is highly variable according to OSA severity [11], the presence of daytime sleepiness [12], treatment adherence [13], the circadian pattern of BP [14] and baseline BP levels [15]. Accordingly, while CPAP is clearly associated with decreases in 24-h and night-time BP among hypertensive patients, its effects on normotensive subjects are less certain. Observational studies suggest that both daytime and night-time BP decrease after CPAP treatment in asymptomatic, middle-aged, normotensive patients [16, 17]. Conversely, an individual patient data meta-analysis revealed that, while patients with uncontrolled BP benefit from CPAP therapy in terms of BP reduction, normotensive individuals experience no significant changes [18]. Findings from Sapiña-Beltrán et al. [19] indicated that the effect of CPAP on normotensive patients may depend on their circadian BP pattern: those with a non-dipping pattern show a reduction in mean nocturnal BP, whereas those with a dipping pattern may experience an increase after 6 months of CPAP treatment.
Considering this, further research is crucial to elucidate the effects of CPAP on BP, particularly among patients whose clinical profiles are associated with unfavourable outcomes. Therefore, the main objective of this study was to assess the impact of CPAP treatment on BP in normotensive patients with a dipping BP pattern and severe OSA.
Methods
Setting and participants
In this randomised, open-label, parallel-group, single-centre, prospective, controlled trial (ClinicalTrials.gov: NCT03948373), patients referred to the sleep unit of the Hospital Universitari Arnau de Vilanova-Santa Maria (Lleida, Spain) for suspected OSA, who met all of the inclusion criteria and none of the exclusion criteria, were recruited. Inclusion criteria were: 1) age ≥18 years; 2) severe OSA (apnoea–hypopnoea index (AHI) ≥30 events·h−1 based on overnight polygraphy) [20]; 3) normotension (mean 24-h BP <130/80 mmHg according to 24-h ambulatory BP monitoring (ABPM)) [21]; and 4) dipping pattern of BP (daytime to night-time BP reduction ≥10%) [21]. Exclusion criteria were: 1) current or prior use of antihypertensive medication; 2) prior or active CPAP treatment; 3) significant somnolence (defined by Epworth Sleepiness Scale (ESS) score >18) [22]; 4) inability to complete questionnaires due to psychophysical reasons; 5) previous diagnosis or suspicion of another sleep disorder; 6) >50% of central apnoea or Cheyne–Stokes respiration; 7) chronic conditions such as neoplasia, renal failure, severe obstructive pulmonary disease and depression; 8) medical history that could interfere with study objectives or compromise conclusions; 9) social or geographical factors affecting patient compliance such as issues with electricity supply at home, difficulty contacting patients by telephone or logistical problems with attending follow-up visits; and 10) high-risk occupational duties (e.g. professional drivers).
Informed consent was obtained from all participants. This study received approval from the Medical Ethics Committee of the Hospital Universitari Arnau de Vilanova-Santa Maria (CEIC-2005) and adhered to the principles outlined in the Declaration of Helsinki.
Procedures to confirm eligibility
A detailed description of the procedures to confirm eligibility (overnight polygraphy, 24-h ABPM and ESS) is provided in the supplementary material.
Baseline evaluations
Eligible patients were assessed for sociodemographic and anthropometric data, including age, sex, weight, height, body mass index (BMI), and neck, hip and waist circumference. Lifestyle habits (e.g. alcohol and tobacco consumption), sleep-related symptoms and clinical history (diabetes mellitus, coronary artery disease or stroke) were also evaluated. Fasting venous blood samples were collected the morning after the sleep study for biochemical analysis.
Randomisation and intervention
Eligible patients were randomly assigned in a 1:1 ratio to receive either CPAP treatment or usual care for 12 weeks. A detailed description of how randomisation was conducted is provided in the supplementary material. Due to the nature of the intervention, both patients and researchers were not blinded to the treatment allocations.
CPAP titration was performed with an auto-CPAP device (Autoset-T; ResMed, San Diego, CA, USA) according to a previously described protocol [23]. Briefly, the optimal pressure was visually determined from the device's raw data by analysing the pressure curve, focusing on periods with a leak <0.4 L·s−1 (90th percentile). CPAP adherence was monitored using the machine's internal clock, which recorded the number of hours of use per night. Adequate adherence was defined as a mean use of ≥4 h·night−1 during the follow-up period.
All participants received sleep hygiene advice and dietary counselling for weight loss from the sleep unit staff.
Follow-up, outcomes and additional analyses
Patients were evaluated at baseline and after 12 weeks of follow-up. At the 3-month visit, anthropometric data were updated, BP was assessed through office BP measurements and 24-h ABPM, daytime somnolence was examined through the ESS, and fasting venous blood samples were collected for biochemical analysis.
The primary outcome was the change from baseline to the 3-month follow-up in the following ABPM parameters: mean 24-h BP, mean daytime BP, mean night-time BP, 24-h systolic BP (SBP), 24-h diastolic BP (DBP), daytime SBP, daytime DBP, night-time SBP and night-time DBP. The secondary outcome was the change in these ABPM parameters from baseline to the 3-month follow-up according to adherence to CPAP treatment.
Additional analyses considered the change from baseline to the 3-month follow-up in relation to: 1) other ABPM parameters (maximum daytime SBP, maximum daytime DBP, maximum night-time SBP, maximum night-time DBP, minimum daytime SBP, minimum daytime DBP, minimum night-time SBP, minimum night-time DBP, mean 24-h heart rate, mean daytime heart rate, mean night-time heart rate, mean 24-h BP variability, mean daytime BP variability, mean night-time BP variability and dipping ratio); 2) anthropometric data (BMI, waist circumference, hip circumference, neck circumference and waist/hip index); 3) office BP (SBP and DBP); 4) daytime somnolence (ESS score); and 5) biochemical markers (cholesterol, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, triglycerides and N-terminal pro-brain natriuretic peptide).
Sample size
The sample size was calculated based on the expected change in night-time SBP after 3 months. Assuming an α of 0.05 and a power of 0.8 for a two-tailed test, 31 subjects per group were required to detect a difference of ≥3 mmHg as statistically significant. The common standard deviation was estimated to be 3.95 mmHg and a dropout rate of 10% was anticipated.
Statistical analysis
Descriptive statistics were used to summarise the baseline characteristics of the study population. Absolute and relative frequencies were used for qualitative data, while means and standard deviations were estimated for quantitative variables. Baseline parameters between the CPAP and usual care arms were compared using the t-test or Chi-squared test, depending on the characteristics of the variables.
Primary and additional analyses were performed using both intention-to-treat and per-protocol approaches. The intention-to-treat analysis considered all patients included in the study, while the per-protocol approach excluded patients in the CPAP arm with insufficient CPAP adherence (<4 h·night−1). Differences between the study arms (CPAP versus usual care) in relation to the primary outcome and additional parameters were assessed using linear models, with a two-sided p-value and 95% confidence interval.
Differences between patients with adequate CPAP adherence (≥4 h·night−1) and those with insufficient adherence (<4 h·night−1) in relation to ABPM parameters were assessed using linear models, with a two-sided p-value and 95% confidence interval. Generalised additive models with a penalised thin plate regression spline were fitted to evaluate non-linear relationships between ABPM parameters and adherence to CPAP treatment.
The p-value threshold defining statistical significance was set at <0.05. All analyses were performed using R version 4.0.1 (R Project for Statistical Computing, Vienna, Austria).
Results
Characteristics of the study population
Between May 2019 and March 2024, 123 individuals referred to the sleep unit at Hospital Universitari Arnau de Vilanova-Santa Maria were confirmed to have severe OSA based on overnight polygraphy and were normotensive according to office BP measurements (figure 1). Of these, 32 participants (26.0%) did not meet the inclusion criteria due to the presence of masked hypertension, and 26 participants (21.1%) exhibited a non-dipping BP pattern based on 24-h ABPM. Ultimately, 64 patients with severe OSA, who were normotensive with a dipping BP pattern according to 24-h ABPM, were enrolled and randomised to receive either CPAP treatment (n=33) or usual care (n=31). After 3 months of follow-up, 60 patients (93.7%) completed the study, with 30 in the CPAP group and 30 in the usual care group.
FIGURE 1.
Patient flowchart. Out of 123 individuals with severe obstructive sleep apnoea (OSA) who were normotensive according to office blood pressure (BP) measurements, 64 met all the inclusion criteria and none of the exclusion criteria. These individuals were randomised to either 3-month continuous positive airway pressure (CPAP) treatment or usual care. After 3 months of follow-up, 60 patients completed the study (30 per group). Eight participants from the CPAP group were excluded from the per-protocol analysis due to insufficient adherence to treatment (<4 h·night−1).
The study population was mostly composed of males (40 patients (66.7%)), with a mean±sd age of 52.2±10.8 years and a BMI of 31.8±4.95 kg·m−2. The mean±sd AHI was 44.6±13.2 events·h−1 and ESS score was 7.95±5.17. The most prevalent sleep-related symptoms were snoring and nocturia (supplementary table S1). Baseline characteristics were balanced between the treatment arms in both the intention-to-treat analysis (table 1) and the per-protocol analysis (supplementary table S2).
TABLE 1.
Baseline characteristics of the study population
| CPAP (n=30) | Usual care (n=30) | p-value | |
|---|---|---|---|
| Sociodemographic and anthropometric data | |||
| Male | 18 (60.0) | 22 (73.3) | 0.411 |
| Age, years | 51.0±12.0 | 53.3±9.43 | 0.408 |
| BMI, kg·m−2 | 33.0±4.87 | 30.6±4.81 | 0.061 |
| Waist circumference, cm | 105±12.5 | 102±9.43 | 0.321 |
| Hip circumference, cm | 112±9.47 | 110±10.3 | 0.438 |
| Waist/hip index | 0.94±0.09 | 0.94±0.07 | 0.694 |
| Neck circumference, cm | 41.9±3.20 | 41.1±3.12 | 0.351 |
| Office BP | |||
| Systolic BP, mmHg | 121.1±11.1 | 122.9±13.2 | 0.544 |
| Diastolic BP, mmHg | 82.1±7.52 | 78.8±7.49 | 0.083 |
| Lifestyle habits | |||
| Current smoker | 6 (20.0) | 7 (23.3) | 1 |
| Chronic alcohol consumption | 13 (44.8) | 16 (53.3) | 0.694 |
| Clinical history | |||
| Diabetes mellitus | 5 (16.7) | 3 (10.0) | 0.706 |
| Coronary artery disease | 1 (3.33) | 0 (0.00) | 1 |
| Stroke | 0 (0.00) | 0 (0.00) | 1 |
| Sleep and respiratory parameters | |||
| Polygraphy | |||
| AHI, events·h−1 | 46.6±15.5 | 42.8±10.7 | 0.289 |
| Apnoea index, events·h−1 | 24.0±16.9 | 19.8±11.3 | 0.263 |
| Hypopnoea index, events·h−1 | 24.0±10.8 | 23.0±9.72 | 0.698 |
| Minimum SaO2, % | 74.4±7.84 | 76.9±6.69 | 0.195 |
| Mean SaO2, % | 92.4±1.98 | 92.0±2.35 | 0.508 |
| CT90, % | 15.1±18.1 | 19.2±23.3 | 0.458 |
| ODI-3%, events·h−1 | 47.1±13.2 | 43.3±12.9 | 0.270 |
| Questionnaire | |||
| ESS score | 7.57±4.75 | 8.33±5.61 | 0.570 |
Data are presented as n (%) or mean±sd, unless otherwise stated. CPAP: continuous positive airway pressure; BMI: body mass index; BP: blood pressure; AHI: apnoea–hypopnoea index; SaO2: arterial oxygen saturation; CT90: percentage of time with SaO2 <90%; ODI-3%: oxygen desaturation index ≥3%; ESS: Epworth Sleepiness Scale.
Primary objective: effect of CPAP treatment on BP
The intention-to-treat analysis showed no significant changes in ABPM parameters after 3 months of CPAP treatment (table 2 and supplementary figure S1). In contrast, the usual care group experienced significant increases in 24-h SBP, mean night-time BP, night-time SBP and night-time DBP, which led to a mean difference of −3.4 mmHg (95% CI −6.124– −0.676; p=0.015) in night-time DBP between the groups. The per-protocol analysis revealed significant differences between the CPAP and usual care groups for all primary end-points except for daytime SBP. The mean differences were −4.024 mmHg (95% CI −7.727– −0.322; p=0.034) for 24-h SBP, −6.052 mmHg (95% CI −10.895– −1.208; p=0.016) for night-time SBP and −4.758 mmHg (95% CI −7.734– −1.781; p=0.002) for night-time DBP.
TABLE 2.
Changes in ambulatory blood pressure (BP) monitoring parameters during the 3-month follow-up
| Intention to treat | Per protocol | |||||||
|---|---|---|---|---|---|---|---|---|
| CPAP (n=30) | Usual care (n=30) | CPAP versus usual care difference | p-value | CPAP (n=22) | Usual care (n=30) | CPAP versus usual care difference | p-value | |
| 24-h SBP, mmHg | ||||||||
| Baseline | 115 (112–118) | 114 (111–116) | 117 (114–121) | 114 (111–116) | ||||
| 3-month | 116 (113–119) | 116 (113–120) | 116 (113–120) | 116 (113–120) | ||||
| Difference | 0.70 (−1.97–3.37) | 2.93 (0.33–5.54)* | −2.23 (−5.887–1.42) | 0.226 | −1.09 (−3.85–1.67) | 2.93 (0.33–5.54)* | −4.02 (−7.727– −0.322) | 0.034 |
| 24-h DBP, mmHg | ||||||||
| Baseline | 73.0 (71.1–74.9) | 72.7 (70.8–74.6) | 74.1 (72.1–76.1) | 72.7 (70.8–74.6) | ||||
| 3-month | 72.3 (70.7–73.9) | 74.2 (72.2–76.1) | 72.2 (70.1–74.2) | 74.2 (72.2–76.1) | ||||
| Difference | −0.73 (−2.35–0.88) | 1.47 (−0.25–3.19) | −2.2 (−4.509–0.109) | 0.061 | −1.91 (−3.38– −0.44)* | 1.47 (−0.25–3.19) | −3.37 (−5.584– −1.167) | 0.003 |
| Mean 24-h BP, mmHg | ||||||||
| Baseline | 86.7 (84.8–88.7) | 86.1 (84.2–88.1) | 88.0 (85.9–90.0) | 86.1 (84.2–88.1) | ||||
| 3-month | 86.4 (84.8–88.0) | 87.9 (85.7–90.0) | 86.5 (84.3–88.6) | 87.9 (85.7–90.0) | ||||
| Difference | −0.33 (−2.01–1.35) | 1.73 (−0.19–3.65) | −2.06 (−4.565–0.432) | 0.103 | −1.50 (−3.21–0.21) | 1.73 (−0.19–3.65) | −3.23 (−5.74– −0.727) | 0.012 |
| Daytime SBP, mmHg | ||||||||
| Baseline | 121 (118–124) | 118 (116–121) | 122 (119–125) | 118 (116–121) | ||||
| 3-month | 121 (118–124) | 121 (118–124) | 121 (117–125) | 121 (118–124) | ||||
| Difference | 0.37 (−2.46–3.19) | 2.43 (−0.31–5.18) | −2.06 (−5.924–1.791) | 0.288 | −0.91 (−3.90–2.08) | 2.43 (−0.31–5.18) | −3.34 (−7.298–0.613) | 0.096 |
| Daytime DBP, mmHg | ||||||||
| Baseline | 77.4 (75.3–79.4) | 76.8 (74.9–78.8) | 78.2 (76.1–80.2) | 76.8 (74.9–78.8) | ||||
| 3-month | 76.1 (74.5–77.7) | 77.7 (75.7–79.6) | 75.8 (73.8–77.9) | 77.7 (75.7–79.6) | ||||
| Difference | −1.27 (−3.23–0.70) | 0.83 (−0.87–2.54) | −2.1 (−4.649–0.449) | 0.105 | −2.36 (−4.17– −0.56)* | 0.83 (−0.87–2.54) | −3.19 (−5.62– −0.774) | 0.011 |
| Mean daytime BP, mmHg | ||||||||
| Baseline | 91.3 (89.3–93.4) | 90.2 (88.3–92.2) | 92.2 (90.0–94.5) | 90.2 (88.3–92.2) | ||||
| 3-month | 90.3 (88.8–91.8) | 91.4 (89.3–93.4) | 90.2 (88.1–92.3) | 91.4 (89.3–93.4) | ||||
| Difference | −1.03 (−2.91–0.85) | 1.13 (−0.82–3.09) | −2.16 (−4.823–0.49) | 0.108 | −2.05 (−3.89– −0.20)* | 1.13 (−0.82–3.09) | −3.17 (−5.8– −0.558) | 0.018 |
| Night-time SBP, mmHg | ||||||||
| Baseline | 102 (98.5–105) | 99.9 (97.4–102) | 105 (101–108) | 99.9 (97.4–102) | ||||
| 3-month | 103 (100.0–107) | 105 (101–109) | 104 (99.4–108) | 105 (101–109) | ||||
| Difference | 1.47 (−1.98–4.92) | 5.23 (2.30–8.17)* | −3.76 (−8.202–0.669) | 0.094 | −0.82 (−4.82–3.18) | 5.23 (2.30–8.17)* | −6.05 (−10.89– −1.208) | 0.016 |
| Night-time DBP, mmHg | ||||||||
| Baseline | 61.6 (59.3–64.0) | 61.9 (59.7–64.0) | 63.2 (60.5–65.9) | 61.9 (59.7–64.0) | ||||
| 3-month | 62.4 (60.1–64.7) | 66.0 (63.7–68.3) | 62.6 (59.7–65.5) | 66.0 (63.7–68.3) | ||||
| Difference | 0.77 (−1.23–2.76) | 4.17 (2.23–6.11)* | −3.4 (−6.124– −0.676) | 0.015 | −0.59 (−2.95–1.77) | 4.17 (2.23–6.11)* | −4.75 (−7.734– −1.781) | 0.002 |
| Mean night-time BP, mmHg | ||||||||
| Baseline | 75.1 (72.9–77.4) | 75.1 (72.9–77.3) | 76.8 (74.3–79.2) | 75.1 (72.9–77.3) | ||||
| 3-month | 76.7 (74.4–79.0) | 79.0 (76.4–81.6) | 77.1 (74.1–80.1) | 79.0 (76.4–81.6) | ||||
| Difference | 1.57 (−0.58–3.72) | 3.83 (1.54–6.12)* | −2.26 (−5.342–0.809) | 0.146 | 0.32 (−2.27–2.90) | 3.83 (1.54–6.12)* | −3.51 (−6.884– −0.147) | 0.041 |
Data are presented as mean or mean difference with 95% confidence intervals, unless otherwise stated. CPAP: continuous positive airway pressure; SBP: systolic blood pressure; DBP: diastolic blood pressure. *: statistically significant (p<0.05) difference between baseline and 3-month follow-up.
Differences between treatment arms were also observed for other ABPM parameters, such as maximum daytime DBP, maximum night-time DBP and night-time heart rate, in both intention-to-treat and per-protocol analyses (supplementary table S3).
Secondary objective: impact of adherence to CPAP treatment on BP
The mean CPAP adherence was 5.74 h·night−1 (95% CI 3.91–6.20). There were 22 patients (73.3%) with a mean adherence of ≥4 h·night−1 and eight patients (26.7%) with a mean adherence of <4 h·night−1.
The analysis to accomplish the secondary objective revealed that patients with adequate adherence (≥4 h·night−1) to CPAP exhibited significant reductions in 24-h DBP, daytime DBP and mean daytime BP (table 3). Conversely, those with adherence <4 h·night−1 experienced significant increases in mean night-time BP, night-time SBP and night-time DBP. This led to significant differences between the groups in several ABPM parameters, including mean 24-h BP, mean night-time BP, night-time SBP and night-time DBP. Further analysis indicated a dose–response relationship between adherence to CPAP treatment and reductions in 24-h DBP and night-time SBP (figure 2). Trends without statistical significance were also observed for mean 24-h BP and 24-h SBP.
TABLE 3.
Changes in ambulatory blood pressure (BP) monitoring parameters during the 3-month follow-up according to adherence per night to continuous positive airway pressure treatment
| Adherence ≥4 h (n=22) | Adherence <4 h (n=8) | Adherence ≥4 versus <4 h difference | p-value | |
|---|---|---|---|---|
| 24-h SBP, mmHg | ||||
| Baseline | 117 (114–121) | 110 (104–116) | ||
| 3-month | 116 (113–120) | 116 (112–119) | ||
| Difference | −1.00 (−3.76–1.76) | 5.62 (−0.73–12.0) | −6.625 (−13.246– −0.004) | 0.05 |
| 24-h DBP, mmHg | ||||
| Baseline | 74.1 (72.1–76.1) | 70.1 (65.1–75.1) | ||
| 3-month | 72.2 (70.2–74.3) | 72.6 (70.1–75.1) | ||
| Difference | −1.86 (−3.34– −0.39)* | 2.50 (−1.92–6.92) | −4.364 (−8.883–0.155) | 0.057 |
| Mean 24-h BP, mmHg | ||||
| Baseline | 88.0 (85.9–90.0) | 83.4 (79.1–87.6) | ||
| 3-month | 86.5 (84.3–88.7) | 86.2 (84.2–88.3) | ||
| Difference | −1.45 (−3.16–0.25) | 2.88 (−1.16–6.91) | −4.33 (−8.522– −0.137) | 0.044 |
| Daytime SBP, mmHg | ||||
| Baseline | 122 (119–125) | 117 (110–124) | ||
| 3-month | 121 (117–125) | 121 (117–124) | ||
| Difference | −0.91 (−3.90–2.08) | 3.88 (−3.69–11.4) | −4.784 (−12.599–3.031) | 0.202 |
| Daytime DBP, mmHg | ||||
| Baseline | 78.2 (76.1–80.2) | 75.1 (69.1–81.1) | ||
| 3-month | 75.8 (73.8–77.9) | 76.9 (74.0–79.7) | ||
| Difference | −2.36 (−4.17– −0.56)* | 1.75 (−4.21–7.71) | −4.114 (−10.172–1.945) | 0.158 |
| Mean daytime BP, mmHg | ||||
| Baseline | 92.2 (90.0–94.5) | 88.9 (83.7–94.1) | ||
| 3-month | 90.2 (88.1–92.3) | 90.6 (88.7–92.5) | ||
| Difference | −2.05 (−3.89– −0.20)* | 1.75 (−3.61–7.11) | −3.795 (−9.278–1.687) | 0.152 |
| Night-time SBP, mmHg | ||||
| Baseline | 105 (101–108) | 94.2 (89.2–99.3) | ||
| 3-month | 104 (99.5–108) | 102 (96.7–107) | ||
| Difference | −0.73 (−4.75–3.30) | 7.75 (1.94–13.6)* | −8.477 (−15.093– −1.862) | 0.015 |
| Night-time DBP, mmHg | ||||
| Baseline | 63.2 (60.5–65.9) | 57.2 (53.5–61.0) | ||
| 3-month | 62.7 (59.8–65.7) | 61.8 (57.4–66.1) | ||
| Difference | −0.50 (−2.89–1.89) | 4.50 (1.60–7.40)* | −5 (−8.509– −1.491) | 0.008 |
| Mean night-time BP, mmHg | ||||
| Baseline | 76.8 (74.3–79.2) | 70.6 (66.4–74.8) | ||
| 3-month | 77.2 (74.2–80.2) | 75.6 (71.4–79.8) | ||
| Difference | 0.41 (−2.21–3.03) | 5.00 (1.57–8.43)* | −4.591 (−8.628– −0.554) | 0.028 |
Data are presented as mean (95% CI) or mean difference (95% CI), unless otherwise stated. SBP: systolic blood pressure; DBP: diastolic blood pressure. *: statistically significant (p<0.05) difference between baseline and 3-month follow-up.
FIGURE 2.
Dose–response relationship between adherence to continuous positive airway pressure (CPAP) treatment and changes in ambulatory blood pressure (BP) monitoring parameters during the 3-month follow-up. The p-value threshold defining statistical significance was set at <0.05. DBP: diastolic blood pressure; SBP: systolic blood pressure.
Additional analyses
The CPAP arm showed a mean reduction of −4.00 (95% CI −5.86– −2.14) in the ESS score after 3 months of follow-up based on the intention-to-treat analysis (supplementary table S4 and supplementary figure S2). This resulted in a significant difference of −3.67 (95% CI −5.919– −1.415; p=0.002) between the CPAP and usual care arms. Similarly, in the per-protocol analysis, the CPAP arm experienced a reduction of −3.86 (95% CI −5.93– −1.80) in the ESS score, leading to a difference of −3.53 (95% CI −5.943– −1.117; p=0.005) between the arms. No significant effect of the treatment was observed in relation to anthropometric parameters, office BP and biochemical markers in both intention-to-treat and per-protocol approaches.
Discussion
This study reveals that CPAP treatment prevents increases in BP among normotensive patients with a dipping BP pattern and severe OSA. Specifically, while subjects receiving usual care experienced a significant rise in the 24-h ABPM parameters over the 3-month follow-up period, those under CPAP treatment showed no changes or reductions during this time. Achieving these effects appears to depend on adherence to treatment, as patients with insufficient adherence to CPAP experienced BP increases comparable to those in the usual care group. Overall, our findings suggest a protective effect of CPAP in preventing an increment in BP among normotensive patients with a dipping BP pattern.
The effectiveness of OSA treatment with CPAP in lowering BP among hypertensive patients is well documented [24–26], but its impact on normotensive subjects is less certain. Observational studies suggest a reduction in both daytime and night-time BP after CPAP treatment among asymptomatic normotensive middle-aged patients [16, 17]. However, this effect is not generalisable. Castro-Grattoni et al. [27] observed that nocturnal hypertension, night-time heart rate and the circadian pattern of BP could predict the BP response to CPAP treatment. Specifically, a dipping BP pattern and low night-time heart rate (<68 beats·min−1) were associated with increased night-time BP and new diagnoses of nocturnal hypertension after CPAP treatment. Sapiña-Beltrán et al. [19] found that patients with a non-dipping pattern experienced a reduction in mean nocturnal BP after 6 months of CPAP treatment, while those with a dipping pattern showed an increase. Differently, our current data reveal that BP was maintained among normotensive patients with a dipping BP pattern after 12 weeks of CPAP treatment, suggesting a role for CPAP in preventing BP increases observed in subjects receiving usual care. Despite similarities among these studies in terms of the median age of the population, severity of OSA and absence of significant somnolence, the divergent results could be related to the different designs of the studies. While the other studies were observational studies, the current study is a randomised controlled trial which effectively evaluates the effects of CPAP treatment among true normotensive patients with a dipping BP pattern.
Differences between the CPAP and usual care groups were more pronounced in the per-protocol analysis, and a dose–response relationship between adherence and reductions in BP, particularly in 24-h DBP and night-time SBP, was found. Additionally, patients with insufficient adherence to CPAP treatment showed a marked increase in mean night-time BP, night-time SBP and night-time DBP. These data suggest a relevant role of adherence in achieving the desired therapeutic effects, as widely acknowledged in previous studies [10, 26]. Nevertheless, the potential influence of the healthy user effect cannot be excluded, wherein individuals adhering to CPAP treatment may also be more likely to maintain a healthy diet and exercise, thereby mitigating BP increases [28–30]. Furthermore, the limited sample size of the non-adherent group constrains the ability to draw definitive conclusions. Future studies incorporating measures of healthy user and healthy adherer bias will be crucial to confirm these findings and assess whether specific CPAP usage patterns among non-adherent subjects may lead to undesirable effects.
According to the international consensus document on OSA [20], CPAP therapy should be considered depending on OSA severity (AHI ≥15 events·h−1) and the presence of one or more of the following characteristics: 1) daytime somnolence (ESS >10), 2) diagnosis of hypertension and 3) sleep-related compromised quality of life. Based on these criteria, some normotensive and asymptomatic subjects in our cohort might not have been allocated to CPAP treatment in a real-world scenario. This could entail a risk, given the observed BP increase during the 3-month follow-up among those not assigned to CPAP treatment. Notably, this increment was especially significant at night, and nocturnal hypertension is often observed during the early stages of hypertension development, being an important predictor of adverse cardiovascular outcomes [28, 29]. Together, these highlight the relevance of considering timely CPAP treatment for normotensive patients with a dipping BP pattern and severe OSA. Future studies are necessary to explore this matter and to investigate the potential of CPAP in mitigating the development of hypertension.
The current data should be interpreted in light of their limitations. First, the generalisability of the findings may be restricted by the single-centre design. Additionally, the per-protocol analysis offers valuable insights into the effects of CPAP among adherent patients but may introduce selection bias by excluding non-adherent individuals, who could differ significantly (e.g. more severe OSA or distinct lifestyle factors) from those adhering to treatment. Although baseline characteristics, such as OSA severity, were comparable between individuals receiving CPAP in the per-protocol and intention-to-treat analyses, a comprehensive evaluation of lifestyle factors, commonly associated with treatment adherence, was not performed. Future studies addressing healthy user and healthy adherer bias will be critical to confirm these findings. Second, 24-h ABPM is subject to significant variability, and some parameters, including nocturnal BP and circadian BP patterns, demonstrate poor reproducibility [30]. Third, although BP was objectively assessed, research personnel were not blinded to patient allocation. Fourth, despite no patient being diagnosed with or suspected of having comorbid insomnia and sleep apnoea (COMISA), this possibility cannot be ruled out given its prevalence, variability in definition and associated diagnostic challenges. Fifth, although several ABPM variables were designated as primary outcomes to align with the study objectives, the sample size estimation was based on changes in mean night-time SBP after 3 months of follow-up. This limited the statistical power to correct for multiple comparisons. Furthermore, the sample size and optimistic estimate of CPAP's effect size in normotensive patients (≥3 mmHg) likely contributed to the null findings for ABPM parameters in the intention-to-treat analysis. The ≥3 mmHg threshold was chosen based on clinical relevance established for hypertensive patients, yet smaller differences, such as 2 mmHg, could have meaningful clinical implications for normotensive OSA patients [31–33]. In fact, some differences between study arms, although not statistically significant due to the sample size, could still be considered clinically relevant. Future studies with larger sample sizes and adequate power are essential to confirm these findings and control appropriately for type I error.
The main strength of this study is its design as the first randomised controlled trial to evaluate the effects of CPAP on BP of normotensive patients with OSA. Here, we present data from a well-characterised cohort, in which severe OSA was accurately diagnosed with polygraphy, true normotensive patients with a dipping BP pattern were included based on 24-h ABPM and patients allocated to CPAP treatment demonstrated overall good adherence.
In conclusion, our findings reveal a protective effect of CPAP treatment in preventing an increase in BP among normotensive subjects with a dipping BP pattern and severe OSA. This effect appears to be related to proper adherence to treatment. These results highlight the importance of considering timely CPAP prescription for this population to control potential increases in BP and possibly prevent the onset of hypertension.
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Footnotes
This clinical trial was prospectively registered at ClinicalTrials.gov with identifier number NCT03948373.
Ethics statement: Informed consent was obtained from all participants. The study received approval from the Medical Ethics Committee of the Hospital Universitari Arnau de Vilanova-Santa Maria (Lleida, Spain) (CEIC-2005) and adhered to principles outlined in the Declaration of Helsinki.
Author contributions: Conceptualisation: A.D.S. Targa, G. Torres, I.D. Benitez, L. Pinilla, M. Sánchez-de-la-Torre and F. Barbé. Recruitment and clinical evaluation: G. Torres and F. Barbé. Follow-up: R. Vaca, L. Pascual Arnó, O. Mínguez, M. Aguilà, D. Martínez and S. Balsells Garriga. Data management and statistical analyses: I.D. Benitez. Data interpretation: A.D.S. Targa, G. Torres, I.D. Benitez, M. Henríquez-Beltrán, A. Galan Gonzalez and F. Barbé. Writing: A.D.S. Targa and M. Henríquez-Beltrán. Revision: all authors. All authors had full access to all the data in the study and had final responsibility for the decision to submit for publication.
Conflict of interest: The authors declare that they have no potential conflicts of interest.
This article has an editorial commentary: https://doi.org/10.1183/13993003.00450-2025
Support statement: This study was supported by Instituto de Salud Carlos III (ISCIII) through project PI22/00636, co-funded by the European Union (Fondo Europeo de Desarrollo Regional-FEDER “Una manera de hacer Europa”), Sociedad Española de Neumología y Cirugía Torácica (SEPAR, 2018/662) and Diputació de Lleida (PIRS22/01). M. Henríquez-Beltrán and A.D.S. Targa have received financial support from ISCIII (PFIS 2023: FI23/00253 and Miguel Servet 2023: CP23/00095, respectively), co-funded by the European Union (Fondo Social Europeo Plus, FSE+). F. Barbé is supported by the ICREA Academia programme. Funding information for this article has been deposited with the Crossref Funder Registry.
Supplementary material
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ERJ-01954-2024.Supplement
Data availability
De-identified individual participant data that underlie the results reported in this article (including text, tables, figures and appendices) will be available to researchers upon reasonable request to the corresponding author after publication. A detailed protocol for the proposed study must be provided, along with approval from an ethics committee.
References
- 1.Benjafield AV, Ayas NT, Eastwood PR, et al. Estimation of the global prevalence and burden of obstructive sleep apnoea: a literature-based analysis. Lancet Respir Med 2019; 7: 687–698. doi: 10.1016/S2213-2600(19)30198-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Martinez-Garcia MA, Campos-Rodriguez F, Barbé F, et al. Precision medicine in obstructive sleep apnoea. Lancet Respir Med 2019; 7: 456–464. doi: 10.1016/S2213-2600(19)30044-X [DOI] [PubMed] [Google Scholar]
- 3.Javaheri S, Javaheri S. Update on persistent excessive daytime sleepiness in OSA. Chest 2020; 158: 776–786. doi: 10.1016/j.chest.2020.02.036 [DOI] [PubMed] [Google Scholar]
- 4.Pase MP, Harrison S, Misialek JR, et al. Sleep architecture, obstructive sleep apnea, and cognitive function in adults. JAMA Netw Open 2023; 6: e2325152. doi: 10.1001/jamanetworkopen.2023.25152 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Kerner NA, Roose SP. Obstructive sleep apnea is linked to depression and cognitive impairment: evidence and potential mechanisms. Am J Geriatr Psychiatry 2016; 24: 496–508. doi: 10.1016/j.jagp.2016.01.134 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Redline S, Azarbarzin A, Peker Y. Obstructive sleep apnoea heterogeneity and cardiovascular disease. Nat Rev Cardiol 2023; 20: 560–573. doi: 10.1038/s41569-023-00846-6 [DOI] [PubMed] [Google Scholar]
- 7.Gottlieb DJ, Yenokyan G, Newman AB, et al. Prospective study of obstructive sleep apnea and incident coronary heart disease and heart failure: the Sleep Heart Health Study. Circulation 2010; 122: 352–360. doi: 10.1161/CIRCULATIONAHA.109.901801 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Yaggi HK, Concato J, Kernan WN, et al. Obstructive sleep apnea as a risk factor for stroke and death. N Engl J Med 2005; 353: 2034–2041. doi: 10.1056/NEJMoa043104 [DOI] [PubMed] [Google Scholar]
- 9.Tang A, Yang E, Ebinger JE. Non-dipping blood pressure or nocturnal hypertension: does one matter more? Curr Hypertens Rep 2024; 26: 21–30. doi: 10.1007/s11906-023-01273-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Sánchez-de-la-Torre M, Gracia-Lavedan E, Benitez ID, et al. Adherence to CPAP treatment and the risk of recurrent cardiovascular events: a meta-analysis. JAMA 2023; 330: 1255–1265. doi: 10.1001/jama.2023.17465 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Fava C, Dorigoni S, Dalle Vedove F, et al. Effect of CPAP on blood pressure in patients with OSA/hypopnea: a systematic review and meta-analysis. Chest 2014; 145: 762–771. doi: 10.1378/chest.13-1115 [DOI] [PubMed] [Google Scholar]
- 12.Montesi SB, Edwards BA, Malhotra A, et al. Effect of continuous positive airway pressure treatment on blood pressure: a systematic review and meta-analysis of randomized controlled trials. J Clin Sleep Med 2012; 8: 587–596. doi: 10.5664/jcsm.2170 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Bratton DJ, Stradling JR, Barbé F, et al. Effect of CPAP on blood pressure in patients with minimally symptomatic obstructive sleep apnoea: a meta-analysis using individual patient data from four randomised controlled trials. Thorax 2014; 69: 1128–1135. doi: 10.1136/thoraxjnl-2013-204993 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Sapiña-Beltrán E, Torres G, Benítez I, et al. Differential blood pressure response to continuous positive airway pressure treatment according to the circadian pattern in hypertensive patients with obstructive sleep apnoea. Eur Respir J 2019; 54: 1900098. doi: 10.1183/13993003.00098-2019 [DOI] [PubMed] [Google Scholar]
- 15.Pengo MF, Soranna D, Giontella A, et al. Obstructive sleep apnoea treatment and blood pressure: which phenotypes predict a response? A systematic review and meta-analysis. Eur Respir J 2020; 55: 1901945. doi: 10.1183/13993003.01945-2019 [DOI] [PubMed] [Google Scholar]
- 16.Crinion SJ, Kleinerova J, Kent B, et al. Non-dipping nocturnal blood pressure correlates with obstructive sleep apnoea severity in normotensive subjects and may reverse with therapy. ERJ Open Res 2021; 7: 00338-2021. doi: 10.1183/23120541.00338-2021 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Yorgun H, Kabakçi G, Canpolat U, et al. Predictors of blood pressure reduction with nocturnal continuous positive airway pressure therapy in patients with obstructive sleep apnea and prehypertension. Angiology 2014; 65: 98–103. doi: 10.1177/0003319713477908 [DOI] [PubMed] [Google Scholar]
- 18.Pengo M, Schwarz EI, Barbe F, et al. Predictors of BP reduction in patients with OSA treated with CPAP therapy – an individual patient data meta-analysis. Eur Respir J 2020; 60: Suppl. 66, 4478. doi: 10.1183/13993003.congress-2022.4478 [DOI] [Google Scholar]
- 19.Sapiña-Beltrán E, Santamaria-Martos F, Benítez I, et al. Normotensive patients with obstructive sleep apnoea: changes in 24-h ambulatory blood pressure monitoring with continuous positive airway pressure treatment. J Hypertens 2019; 34: 720–727. doi: 10.1097/HJH.0000000000001934 [DOI] [PubMed] [Google Scholar]
- 20.Mediano O, González Mangado N, Montserrat JM, et al. Documento internacional de consenso sobre apnea obstructiva del sueño. [International consensus document on obstructive sleep apnea.] Arch Bronconeumol 2022; 58: 52–68. doi: 10.1016/j.arbres.2021.03.017 [DOI] [PubMed] [Google Scholar]
- 21.Williams B, Mancia G, Spiering W, et al. 2018 practice guidelines for the management of arterial hypertension of the European Society of Cardiology and the European Society of Hypertension. Blood Press 2018; 27: 314–340. doi: 10.1080/08037051.2018.1527177 [DOI] [PubMed] [Google Scholar]
- 22.Chiner E, Arriero JM, Signes-Costa J, et al. Validación de la versión española del test de somnolencia Epworth en pacientes con síndrome de apnea de sueño. [Validation of the Spanish version of the Epworth Sleepiness Scale in patients with sleep apnea syndrome.] Arch Bronconeumol 1999; 35: 422–427. doi: 10.1016/S0300-2896(15)30037-5 [DOI] [PubMed] [Google Scholar]
- 23.Masa JF, Jiménez A, Durán J, et al. Alternative methods of titrating continuous positive airway pressure: a large multicenter study. Am J Respir Crit Care Med 2004; 170: 1218–1224. doi: 10.1164/rccm.200312-1787OC [DOI] [PubMed] [Google Scholar]
- 24.Barbé F, Durán-Cantolla J, Sánchez-de-la-Torre M, et al. Effect of continuous positive airway pressure on the incidence of hypertension and cardiovascular events in nonsleepy patients with obstructive sleep apnea: a randomized controlled trial. JAMA 2012; 307: 2161–2168. doi: 10.1001/jama.2012.4366 [DOI] [PubMed] [Google Scholar]
- 25.Labarca G, Schmidt A, Dreyse J, et al. Efficacy of continuous positive airway pressure (CPAP) in patients with obstructive sleep apnea (OSA) and resistant hypertension (RH): systematic review and meta-analysis. Sleep Med Rev 2021; 58: 101446. doi: 10.1016/j.smrv.2021.101446 [DOI] [PubMed] [Google Scholar]
- 26.Martínez-García MÁ, Capote F, Campos-Rodríguez F, et al. Effect of CPAP on blood pressure in patients with obstructive sleep apnea and resistant hypertension: the HIPARCO randomized clinical trial. JAMA 2013; 310: 2407–2415. doi: 10.1001/jama.2013.281250 [DOI] [PubMed] [Google Scholar]
- 27.Castro-Grattoni AL, Torres G, Martínez-Alonso M, et al. Blood pressure response to CPAP treatment in subjects with obstructive sleep apnoea: the predictive value of 24-h ambulatory blood pressure monitoring. Eur Respir J 2017; 50: 1700651. doi: 10.1183/13993003.00651-2017 [DOI] [PubMed] [Google Scholar]
- 28.Filippone EJ, Foy AJ, Naccarelli GV. Controversies in hypertension III: dipping, nocturnal hypertension, and the morning surge. Am J Med 2023; 136: 629–637. doi: 10.1016/j.amjmed.2023.02.018 [DOI] [PubMed] [Google Scholar]
- 29.Staplin N, de la Sierra A, Ruilope LM, et al. Relationship between clinic and ambulatory blood pressure and mortality: an observational cohort study in 59 124 patients. Lancet 2023; 401: 2041–2050. doi: 10.1016/S0140-6736(23)00733-X [DOI] [PubMed] [Google Scholar]
- 30.Manning G, Rushton L, Donnelly R, et al. Variability of diurnal changes in ambulatory blood pressure and nocturnal dipping status in untreated hypertensive and normotensive subjects. Am J Hypertens 2000; 13: 1035–1038. doi: 10.1016/S0895-7061(00)00261-2 [DOI] [PubMed] [Google Scholar]
- 31.Lewington S, Clarke R, Qizilbash N, et al. Age-specific relevance of usual blood pressure to vascular mortality: a meta-analysis of individual data for one million adults in 61 prospective studies. Lancet 2002; 360: 1903–1913. doi: 10.1016/S0140-6736(02)11911-8 [DOI] [PubMed] [Google Scholar]
- 32.Fuchs FD, Whelton PK. High blood pressure and cardiovascular disease. Hypertension 2020; 75: 285–292. doi: 10.1161/HYPERTENSIONAHA.119.14240 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Turnbull F, Neal B, Algert C, et al. Effects of different blood-pressure-lowering regimens on major cardiovascular events: results of prospectively-designed overviews of randomised trials. Lancet 2003; 362: 1527–1535. doi: 10.1016/s0140-6736(03)14739-3 [DOI] [PubMed] [Google Scholar]
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Data Availability Statement
De-identified individual participant data that underlie the results reported in this article (including text, tables, figures and appendices) will be available to researchers upon reasonable request to the corresponding author after publication. A detailed protocol for the proposed study must be provided, along with approval from an ethics committee.