Differentiation of the Non-dipping Blood Pressure Phenotype in Obstructive Sleep Apnea: An Observational Study

Background: Absence of nocturnal decrease in Blood Pressure (BP) (“non-dipping”) has been shown to be a strong and independent predictor of cardiovascular events, target organ damage, cardiovascular sequela and cardiovascular mortality. Obstructive Sleep Apnea (OSA) has been associated with non-dipping with an estimated prevalence of approximately 50%, but factors associated with non-dipping in OSA patients remain poorly understood. In this study, we examined clinically relevant variables associated with non-dipping in OSA.

Methods: Patients (n = 35) undergoing overnight valuation for OSA, laboratory-based polysomnography, structured clinical interviews, and comprehensive metabolic and anthropometric evaluations, and ambulatory BP monitoring for 24 hours. Patients were classified into a) dipping BP group or b) non-dipping BP group, based on (a) a nocturnal systolic BP decrease of 10% - 20% or (b) a systolic BP decrease of < 10%.

Results: Patients had moderate and severe OSA (AHI = 34.8 ± 29.1), and 42.9% demonstrated a non-dipping BP pattern. The severity of OSA measures did not differ between dipping group and non-dipping group. However, Wake after Sleep Onset (WASO) and chronicity of insomnia predicts non-dipping BP independent of demographics, sleep stages, anthropometrics, metabolic measures, or arterial stiffness.

Conclusion: These findings contribute to a better understanding of the cardiovascular impacts of OSA and indicate that sleep quality should be incorporated into clinical assessments and management of OSA patients.

AASM: American Academy of Sleep Medicine; ABPM: Ambulatory Blood Pressure Measurement; AHI: Apnea and Hypopnea Index; ASI: Arterial Stiffness Index; AUC: Area Under the Curve; BMI: Body Mass Index; BP: Blood Pressure; BPV: Blood Pressure Variability; CAP: College of American Pathologists; CDC: Center for Disease Control; CPAP: Continuous Positive Airway Pressure; CV: Cardiovascular; ECG: Electrocardiogram; GERD: Gastroesophageal Reflux Disease; HDL: High-Density Lipoprotein; HPLC: High Performance Liquid Chromatography; INCMNSZ: National Institute of Medical Science and Nutrition Salvador Zubirán; LDL: Low-Density Lipoprotein; MSI: Morning Surge Index; NGSP: National Glycohemoglobin Standardization Program; ODI: Oxygen Desaturation Index; OSA: Obstructive Sleep Apnea; PLMS: Periodic Limb Movements of Sleep; PLMSI: Periodic Limb Movements of Sleep-Index; PSG: Polysomnographic; PUD: Peptic Ulcer Disease; REM: Rapid Eye Movement; ROC: Receiver Operating Characteristic; SD: Standard Deviation; SpO₂: Blood Oxygen Saturation Percentage; WASO: Wake after Sleep Onset

Blood Pressure (BP) follows a circadian rhythm, decreasing (10% - 20%) during sleep, and this decrease has been associated with cardiovascular health [1,2]. In contrast, a lack of BP decrease (non-dipping) [3,4] is an independent predictor of cardiovascular events, target organ damage, cardiovascular sequelae and cardiovascular mortality [5-11].

BP non-dipping may be modulated by the sympathetic nervous system [12], disturbed baroreflex sensitivity [13], or increased salt sensitivity [14], as well as metabolic alterations such as Glycemic imbalance and type 2 diabetes mellitus [15-17].

In addition to these conditions, there is substantial evidence in both cross-sectional and longitudinal studies that respiratory alterations during sleep, such as Obstructive Sleep Apnea (OSA), are associated with increased frequency of non-dipping of systolic BP in a dose‒dependent manner [18-21]. For example, a recent study [22] characterized sleep parameters using Polysomnography (PSG) associated with alterations in the circadian blood pressure pattern revealed that respiratory parameters such as the apnea/hypopnea index (AHI), oxygen saturation level, and respiratory arousal index contributed to the non-dipping of BP.

In this study, we expanded the range of possible variables contributing to non-dipping in OSA patients by studying OSA patients presenting at a tertiary care hospital in Mexico City.

Participants

Consecutive patients (n = 67) referred to the National Institute of Medical Sciences and Nutrition Salvador Zubirán (INCMNSZ) Sleep Laboratory for suspected breathing disorders during sleep were invited to participate in the study. Those with other comorbidities that could affect the cardiovascular system or sleep characteristics (psychiatric, neurological disorders, and current use of psychotropics) were excluded (Figure 1), resulting in 35 eligible participants.

Study design

We conducted a cross-sectional, observational study comparing OSA patients with a dipping BP pattern with those with a non-dipping BP pattern. Dipping was defined as a nocturnal systolic BP decrease (10% - 20%) during sleep, whereas non-dipping was defined as a nocturnal BP decrease < 10%. Over a 10-month period (June 2022–March 2023), all consecutive patients who were referred to the Sleep Laboratory of INCMNSZ were invited to participate in the study. The study was approved by the INCMNSZ Institutional Committee for Biomedical Research Involving Humans (protocol # 2338); written informed consent was obtained from all participants. To be included in the study, patients were required to complete overnight PSG and demonstrate an AHI ≥ 5 per hour, as well as willingness to undergo laboratory tests and 24-hour ambulatory BP monitoring. To determine the sample size and statistical power, we considered two-tailed type I error (α) = 0.05, with a power of 85% (type B error = 0.15), and assumed a prevalence of non-dipping of 59%. This was projected to a sample size of n = 50; however, we allowed for 15 cases with incomplete data, resulting in a targeted sample of n = 65. Sixty-seven cases were approached, but after eliminating cases with inadequate data collection or confounding comorbidities (Figure 1), the final sample was n = 35.

Anthropometry, health-related variables and habits questionnaire

Body weight was measured to the nearest 0.1 kg, and height was measured to the nearest 0.1 cm using a Seca 284 stadimeter with a 1 mm gradation. Waist circumference was evaluated via an inelastic Seca 201 tape with 0.1 cm precision placed directly over the skin at the midpoint between the ribcage and the iliac crest with the tape parallel to the floor and standing with their feet together with weight evenly distributed, holding the arms in a relaxed position at the side, and breathing normally. Waist and hip circumferences were taken to derive the waist‒hip ratio.

Body Mass Index (BMI) was calculated as weight in kg divided by the squared product of height in meters. All anthropometric measurements were performed on the day of the sleep studies by a trained technician. The questionnaire collected data on demographics, drinking behavior, and smoking habits [Smoking Index (number of cigarettes per day by number of years)/20 cigarettes)] has been previously described [23]. The questionnaires were administered prior to PSG.

Ambulatory BP Measurement (ABPM)

ABPM was performed 36 hours before Polysomnography (PSG) to prevent the disruptive influence of cuff inflation on sleep. BP monitoring was performed with the Spacelabs healthcare device (OnTrak 90227 monitor, Snoqualmie, WA, USA), which measures blood pressure and heart rate over 24-hour periods, providing the following parameters: blood pressure variability (calculated as the Standard Deviation (SD) of all BP observations over the measurement period); estimation of mean blood pressure by day, night and over 24 h; and drop in blood pressure overnight measured as a ratio of nocturnal mean BP to day mean BP expressed as a percentage. We also examined the morning surge index (MSI), which was calculated as the mean systolic BP during the 2 hours after awakening minus the mean systolic BP during the 1 hour that included the lowest sleep BP. MSI has been associated with adverse cardiovascular events [24,25], and is a relevant measure of changes in BP. We also derived the Arterial Stiffness Index (AASI), which was calculated as 1 minus the regression slope of the diastolic versus the systolic pressure over the 24-hour BP recordings. The AASI has been proposed as a marker of arterial stiffness, as it is correlated with markers of preclinical target organ damage and CV outcomes, especially stroke [26,27].

We followed the technical recommendations of the position statement on ABPM by the Spanish Society of Hypertension [28], which is based on the European Society of Hypertension Working Group [29]. We considered recordings acceptable with a minimum of one reading per hour and to have at least 70% valid measurements obtained at least every 30 minutes during the nighttime sleep period and every 15 minutes during the daytime. ABPM recordings were independently reviewed by a cardiologist specializing in hypertension. We used sleep diaries provided to each patient during ABPM to determine the start and end of the sleep period during the 24 hours in which they wore the unit.

Polysomnography (PSG)

We recorded one night of laboratory-based PSG using standard techniques. Recordings began at each subject’s usual bedtime and ended at their typical wake-up time in the morning. Quantitative evaluations of sleep stages were generated visually by an experienced technologist via the American Academy of Sleep Medicine (AASM) rules [30]. All the recordings were generated on a NicVue system (version 3.0.6, 2013, Natus Medical Incorporated, Middleton, WI, USA) and consisted of simultaneous monitoring of surface electroencephalogram, electro-oculogram, electromyograms of the mentalis and bilateral anterior tibialis muscles, and an Electrocardiogram (ECG) (Lead II). Respiratory movements were monitored via a plethysmography belt. Oral/nasal airflow was monitored with a four-bead thermistor system and a pressure transducer airflow sensor. Oxygen saturation (SpO2) was recorded with an ear pulse oximeter (Capnocheck Sleep 9004-001, Smith Medical PM, Inc., Waukesha, WI, USA).

The number of abnormal breathing events per hour of sleep was quantified as the AHI. Obstructive apneas were defined by a ≥ 90% drop in peak signal excursion of pre-event baseline using the oral/nasal thermal sensor for a duration ≥ 10 s with continued inspiratory effort. Central apneas were scored if the apnea criteria were met but with no inspiratory effort throughout the entire period of absent airflow. Mixed apneas were scored if the apnea criteria were met, and the event was associated with concomitant absent inspiratory effort in the initial portion of the event, followed by resumption of inspiratory effort in the second portion of the event. Hypopneas were scored if the nasal pressure excursion signal dropped by 30% of baseline for a minimum duration of at least 10 s accompanied by a ≥ 3% drop in oxygen saturation (SpO2) from the pre-event baseline. We defined the presence of sleep apnea as an AHI ≥ 5 events per hour, although the majority of the patients studied had moderate or severe sleep apnea (AHI > 30) (see Results). The Oxygen Desaturation Index (ODI) was defined as the number of drops in SpO2 ≥ 3% per hour of sleep. Periodic Limb Movements of Sleep (PLMS) were scored on the basis of AASM rules [30] and were quantified as the number of leg movements per hour to yield a PLMS index (PLMSI).

Biochemical and metabolic measurements

Following the night of PSG, patients underwent a morning fasting venous blood draw for serum glucose and lipid panel testing. The values of glycosylated hemoglobin were determined via HPLC on a Bio-Rad model D-100 platform, with accreditation by NGSP level 1 and CAP. The plasma glucose concentration was measured with an automated glucose analyzer (Yellow Springs Instruments, Yellow Springs, OH, USA), and lipid profiling was carried out using an enzymatic method with a Beckman Coulter AU 5800 platform accredited by the CAP and certified by the CDC as of 2021. The diagnosis of diabetes was made by an internist or an endocrinologist on the basis of the American Diabetes Association Guidelines, 2019 [31].

Statistical analysis

Data were analyzed via SPSS version 21.0 software (IBM Corporation). For continuous data, the Shapiro–Wilk test was used to determine a normal distribution, assuming normality when p > 0.05. For these variables, we used parametric tests. Non-normally distributed data were analyzed using nonparametric methods. Categorical data are presented as frequencies and proportions. Missing values (< 20%) were imputed using median substitution for variables with fewer than 20% missing cases.

We employed multivariate logistic regression models to examine which variables best predicted blood pressure status, assigning non-dipping status as 1 and dipping status as 0. All hypothesis testing was 2tailed with an alpha level of 0.05. We used ROC analysis to further clarify the accuracy of the predictive variables and to determine the cutoff values for the presence or absence of a dipping BP pattern.

Forty-three percent of the samples (n = 15) exhibited a nocturnal blood pressure reduction of less than 10% and were therefore classified as non-dipping. There were no significant differences in age, BMI, other measures of adiposity or smoking history. The non-dipping group presented elevated glycosylated hemoglobin and reduced triglyceride levels (see Table 1). Chronic insomnia was more common among non-dippers (defined as a sleep complaint of poor sleep for ≥ 2 years) (Table 1).

Medication use did not differ between the two groups (Table 2). One patient in the dipping group and two in the non-dipping group were taking three or more antihypertensive drugs, including a diuretic.

Table 3 shows in detail the multiple BP measures derived from the 24-hour monitoring. By definition, there were statistically significant group differences in the decrease in nocturnal systolic blood pressure and in the decrease in nocturnal diastolic blood pressure. Compared with dippers, non-dippers presented a lower MSI and higher AASI.

There were no significant differences between groups for measures of OSA severity. However, the non-dippers presented a substantially less consolidated sleep pattern, with an increase in wakefulness after sleep onset (WASO), which was greater than double the value of the dippers (Table 4). Logistic regressions (Table 5) incorporating triglycerides (Model 1) or glycosylated hemoglobin (Model 2), as well as age, BMI, (AASI), and sleep disruption variables (time awake after sleep onset, chronicity of reported insomnia), indicated that the latter variables were the most salient predictors of non-dipping status (R² Nagelkerke = 0.691). Using nonparametric ROC analysis, we evaluated the area under the curve (AUC) for two predictive variables: insomnia lasting ≥ 2 years and WASO. The AUC for reported insomnia years was not significant (AUC = 0.667 ± 0.096, 95% CI: 0.479–0.854, p = 0.096). In contrast, the AUC for WASO was significant (AUC = 0.762 ± 0.087, 95% CI: 0.592–0.931, p = 0.009). The optimal cutoff point for WASO was determined to be 35 minutes, with a sensitivity of 0.800 and a specificity of 0.650.

In a final sample of 35 patients diagnosed with moderate to severe OSA, slightly fewer than half exhibited a non-dipping blood pressure pattern during 24-hour ABPM. The severity of OSA did not predict a non-dipping pattern. However, WASO and the chronicity of insomnia were significant predictors, independent of anthropometric, metabolic, or vascular stiffness factors. ROC curve analysis indicated that WASO had moderate predictive accuracy value, with a cutoff point of 35 minutes.

This appears to be the first report on the prevalence of non-dipping blood pressure in a Mexican population with mixed European and indigenous ancestry, specifically in patients with OSA living at moderate altitudes (2,240 meters above sea level). Our findings fall within the prevalence range reported in the literature. A recent meta-analysis of 14 studies, with 66% of participants of Asian origin, conducted by Cuspidi et al. [21], reported a pooled prevalence of 59%, although higher rates (up to 84%) have been reported in other studies [32]. The variability in prevalence seems to be influenced by racial disparities in the non-dipping pattern. For example, Booth et al. [33], reported that black individuals exhibited a statistically significantly smaller decline in blood pressure, from wakefulness to sleep, than white individuals did, even after adjusting for multiple variables. In our sample of Mexican individuals receiving tertiary-level care, non-African Americans and non-Asians were included.

Using a different methodology, longitudinal data over a 7.2-year period demonstrated a significant association between baseline OSA severity and the development of nocturnal systolic BP non-dipping in participants who initially exhibited normal nocturnal dipping [20]. Notably, Mokhlesi et al. [34], reported that, in a population-based sample, Rapid eye movement (REM)-related OSA was independently associated with the onset of BP non-dipping. Future longitudinal clinical studies are needed to further confirm these findings.

In our cross-sectional analysis of variables associated with non-dripping in OSA patients, we included measures of glycemic control, which previous studies have emphasized as important [35]. Although glycosylated hemoglobin and triglycerides differed between dippers and non-dippers, these associations lost significance in multivariate analyses. Another potential factor, arterial stiffness—an indicator of vascular tone and endothelial function and often considered an independent risk factor for cardiovascular disease—also became nonsignificant after adjustment. Our data suggest that only variables related to poor sleep quality, particularly objective measures such as the WASO, predict non-dipping status. These findings are consistent with studies showing that poor sleep quality can affect nighttime BP regulation, even in the absence of OSA [36]. These results suggest that for some patients, treating OSA alone (e.g., with continuous positive airway pressure (CPAP)) may not fully reverse normal nocturnal BP declines without also addressing sleep quality. Future clinical intervention studies are needed to test this hypothesis.

Interestingly, and perhaps not coincidentally, high altitude has known to affect sleep quality [37] and can negatively impact breathing during sleep [38]. These effects are typically acute and resolve over time with prolonged exposure to elevation. Since all of our patients resided at moderate altitude, they were likely acclimatized to these conditions. However, it remains possible that the poor sleep quality observed in our study sample may partially reflect residual effects of altitude [39].

This work was supported by UNAM-PAPIIT 32-IN216919.

We thank and gratefully acknowledge Dr. Daniel Illescas Zarate for his valuable input on the manuscript.

Authors’ contributions

All authors contributed to this work and had final approval of the submitted and published versions. MVF, VSA study design, patient recruitment, and data analysis; MF, interpretation of AMBP; DLB data analysis and manuscript writing; JOM, Medical diagnosis and treatment; CA, laboratory analysis; MR, GG, RMN, MM, polysomnographic studies.

Data availability

The data supporting this study’s findings are available from the corresponding author upon reasonable request.

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