**Epidemiology, Pathomechanism and Treatment**

Special Issue Editors

**Andras Bikov Silvano Dragonieri**

MDPI • Basel • Beijing • Wuhan • Barcelona • Belgrade • Manchester • Tokyo • Cluj • Tianjin

*Special Issue Editors* Andras Bikov University of Manchester UK

Silvano Dragonieri University of Bari Italy

*Editorial Office* MDPI St. Alban-Anlage 66 4052 Basel, Switzerland

This is a reprint of articles from the Special Issue published online in the open access journal *Medicina* (ISSN 1010-660X) (available at: https://www.mdpi.com/journal/medicina/special issues/ Obstructive Sleep Apnea).

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c 2020 by the authors. Articles in this book are Open Access and distributed under the Creative Commons Attribution (CC BY) license, which allows users to download, copy and build upon published articles, as long as the author and publisher are properly credited, which ensures maximum dissemination and a wider impact of our publications.

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## **Contents**


### **About the Special Issue Editors**

**Andras Bikov** is a post-doc clinical fellow, a specialist in respiratory medicine and an ESRS-certified somnologist with an academic interest in chronic airway diseases and sleep medicine. He graduated as a medical doctor from Semmelweis University, Budapest, Hungary, in 2009, and he completed his PhD training on exhaled breath analysis in 2014 under the supervision of Prof. Ildiko Horvath. As part of his PhD, he received a long-term fellowship from the European Respiratory Society at the National Heart and Lung Institute, Imperial College London, under the supervision of Prof. Peter Barnes. He received his board certificate in respiratory medicine in 2017 and ESRS certification in somnology in 2019. He is currently a consultant in respiratory medicine with a special interest in sleep and ventilation at the Wythenshawe Hospital, Manchester University NHS Foundation Trust, an honorary research physician at the Medicines Evaluation Unit and an honorary clinical fellow at the Division of Infection, Immunity & Respiratory Medicine working in the group led by Professor Jorgen Vestbo. Dr Bikov's research currently focuses on the pathomechanism of COPD using various methods, including the forced oscillation technique, exhaled breath analysis, induced sputum and bronchoalveolar lavage. He has contributed to more than 60 research papers, including the European Respiratory Society technical standard document on exhaled breath analyses. He has an H-index of 16 on Scopus. He is a regular reviewer for many respiratory journals, editor-in-chief for Asthma Research and Practice and an editorial board member for the *Journal of Asthma*. Dr. Bikov is actively involved in the European Respiratory Society, having served as the Early Career Members representative for Assembly 5 (Airway Diseases) between 2013 and 2016.

**Silvano Dragonieri** is a researcher, assistant professor at the University of Bari, Italy, and a pulmonologist at the University Hospital Policlinico, Bari, Italy. He graduated as a medical doctor from the University of Bari, Italy, in 2003, specialized in Pulmonology at the University of Bari in 2007 and completed his PhD training in 2012 under the supervision of Prof. Peter Sterk at the University of Amsterdam, Netherlands, on exhaled breath analysis by electronic nose. He has contributed to more than 50 research papers. He has an H-index of 16 on Scopus and is a regular reviewer for many respiratory journals. Dr Dragonieri Bikov is an active member of the European Respiratory Society in Assembly 5.2 (Monitoring Airway Diseases).

### *Editorial* **Obstructive Sleep Apnea: A View from the Back Door**

#### **Silvano Dragonieri 1,\* and Andras Bikov 2,3,\***


Received: 22 April 2020; Accepted: 23 April 2020; Published: 25 April 2020

**Abstract:** Obstructive sleep apnea (OSA) is a common disease that may affect up to 50% of the adult population and whose incidence continues to rise, as well as its health and socio-economic burden. OSA is a well-known risk factor for motor vehicles accidents and decline in work performance and it is frequently accompanied by cardiovascular diseases. The aim of this Special Issue is to focus on the characteristics of OSA in special populations which are less frequently investigated. In this regard, seven groups of experts in the field of sleep medicine gave their contribution in the realization of noteworthy manuscripts which will support all physicians in improving their understanding of OSA with the latest knowledge about its epidemiology, pathophysiology and comorbidities in special populations, which will serve as a basis for future research.

**Keywords:** obstructive sleep apnea; sleep disordered breathing; cardiovascular comorbidities; biomarkers; inflammation; volatile organic compounds; accident risk; non-communicable diseases; risk assessment

#### **1. Introduction**

Obstructive sleep apnea (OSA) is a common disease that may affect up to 50% of the adult population [1]. These percentages are comparable to arterial hypertension [2], and even higher than in diabetes mellitus [3]. Although the exact prevalence in different communities is still unknown, the incidence of OSA continues to rise, as well as its health and socio-economic burden [4]. This Special Issue focuses on the characteristics of OSA in special populations which are less frequently investigated.

OSA is a well-known risk factor for motor vehicles accidents and decline in work performance [5,6]. Alexandropolou et al. concluded that OSA affects around 20% of the Greek nurses and 8% of the nurses have OSA with excessive daytime sleepiness [7]. Celikhisar et al. studied 965 heavy equipment operators in Turkey and found that around 7% of them had OSA [8]. More importantly, the severity of OSA was directly related to the number of work-related accidents [8].

Despite the increasing awareness of OSA and its consequences, most of the patients with OSA remain undiagnosed and untreated [9]. Data on OSA prevalence mainly originate from high-income countries with good healthcare access [4]. In contrast, low- or middle-income countries are lessrepresented in epidemiological studies. Mathiyalagen et al. screened a population of patients attending non-communicable disease clinics in a rural health training center in South India and reported a 25.8% incidence of OSA [10].

Cardiovascular diseases frequently accompany OSA [11]. Chronic intermittent hypoxia in OSA leads to airway inflammation [12] which can be analyzed in exhaled breath samples [13]. In this issue, Finamore et al. provide a comprehensive summary on the current knowledge of exhaled breath analysis in OSA [14]. Airway inflammation, together with intermittent hypoxia and surges in the sympathetic activity, induce systemic inflammation [15] which could be a potential link to cardiovascular diseases in OSA. The soluble urokinase type plasminogen activator receptor (suPAR) is a promising biomarker of cardiovascular disease [16]. However, Bocskei et al. reported unaltered suPAR levels in OSA [17]. Despite the relationship between cardiovascular disease and OSA, little is known about the characteristics of obstructive sleep apnea in special subgroups of patients. Ardelean et al. studied 143 patients with heart failure and OSA [18]. They concluded that patients with midrange ejection fraction (40%–49%) are characterized by a different profile of comorbidities compared to low and preserved ejection fraction subgroups [18]. Finally, in their excellent study, Zota el al. concluded that OSA is related to exercise limitation which is improved after continuous positive airway treatment [19].

Taken together, these studies will support all physicians in improving their understanding of OSA with the latest knowledge about its epidemiology, pathophysiology and comorbidities in special populations, which will serve as a basis for future research.

**Acknowledgments:** Andras Bikov is supported by the NIHR Manchester BRC.

#### **References**


*Medicina* **2020**, *56*, 208


© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

*Article*

## **CPAP E**ff**ect on Cardiopulmonary Exercise Testing Performance in Patients with Moderate-Severe OSA and Cardiometabolic Comorbidities**

**Ioana Mădălina Zota 1,**† **, Cristian Stătescu <sup>1</sup> , Radu Andy Sascău 1, Mihai Roca 1,\*, Radu Sebastian Gavril 1, Teodor Flaviu Vasilcu 1, Daniela Bois,teanu 2, Alexandra Mas,taleru <sup>1</sup> , Alexandra Jitaru 1, Maria Magdalena Leon Constantin <sup>1</sup> and Florin Mitu <sup>1</sup>**


Received: 27 January 2020; Accepted: 12 February 2020; Published: 15 February 2020

**Abstract:** *Background and Objectives:* Obstructive sleep apnea (OSA) is associated with daytime somnolence, cognitive impairment and high cardiovascular morbidity and mortality. Obesity, associated cardiovascular comorbidities, accelerated erythropoiesis and muscular mitochondrial energetic dysfunctions negatively influence exercise tolerance in moderate-severe OSA patients. The cardiopulmonary exercise testing (CPET) offers an integrated assessment of the individual's aerobic capacity and helps distinguish the main causes of exercise limitation. The purpose of this study is to evaluate the aerobic capacity of OSA patients, before and after short-term continuous positive airway pressure (CPAP). *Materials and Methods:* Our prospective study included 64 patients with newly diagnosed moderate-severe OSA (apnea hypopnea index (AHI) 39.96 ± 19.04 events/h) who underwent CPET before and after CPAP. Thirteen patients were unable to tolerate CPAP or were lost during follow-up. Results: 49.29% of our patients exhibited a moderate or severe decrease in functional capacity (Weber C or D). CPET performance was influenced by gender but not by apnea severity. Eight weeks of CPAP induced significant improvements in maximal exercise load (Δ = 14.23 W, *p* = 0.0004), maximum oxygen uptake (Δ = 203.87 mL/min, *p* = 0.004), anaerobic threshold (Δ = 316.4 mL/min, *p* = 0.001), minute ventilation (Δ = 5.1 L/min, *p* = 0.01) and peak oxygen pulse (Δ = 2.46, *p* = 0.007) as well as a decrease in basal metabolic rate (BMR) (Δ = −8.3 kCal/24 h, *p* = 0.04) and average Epworth score (Δ = −4.58 points, *p* < 0.000001). *Conclusions:* Patients with moderate-severe OSA have mediocre functional capacity. Apnea severity (AHI) was correlated with basal metabolic rate, resting heart rate and percent predicted maximum effort but not with anaerobic threshold or maximum oxygen uptake. Although CPET performance was similar in the two apnea severity subgroups, short-term CPAP therapy significantly improved most CPET parameters, suggesting that OSA per se has a negative influence on effort capacity.

**Keywords:** obstructive sleep apnea; continuous positive airway treatment; cardiopulmonary exercise testing; functional capacity; cardiovascular rehabilitation

#### **1. Introduction**

Repetitive nocturnal upper airway collapse, with subsequent hypoxic episodes and microawakenings, is the hallmark of obstructive sleep apnea (OSA) [1]. While chronic sleep fragmentation leads to excessive daytime somnolence and cognitive impairment [2], hypoxia is associated with autonomic and hormonal imbalance, endothelial dysfunction and oxidative stress [3], explaining the high cardiovascular morbidity and mortality described among OSA patients [1,3].

In-hospital polysomnography is the diagnostic standard for OSA, with cardio-respiratory polygraphy considered an acceptable alternative [4–6]. According to the apnea–hypopnea index (AHI), defined as the number of apneic or hypopneic episodes per hour of sleep, OSA is classified as mild, moderate or severe [7]. Daytime sleepiness is the main symptom in OSA, a subjective parameter that can be objectively assessed using the Epworth questionnaire.

Treatment is recommended in all cases of moderate-severe OSA (AHI ≥ 15 events/h), as well as in patients with mild OSA who associate symptoms or cerebrovascular comorbidities [8]. Current therapy options include continuous positive airway pressure (CPAP), mandibular advancement devices, maxillo-facial surgery and nocturnal hypoglossal nerve stimulation [9,10]. Although CPAP remains the gold-standard treatment option for moderate-severe OSA, its use is limited by poor treatment adherence, especially among children.

Obesity and weight-related lung-function abnormalities (decreased functional residual capacity and expiratory reserve volume, impaired respiratory system compliance) are highly prevalent among OSA patients [10]. Associated cardiovascular comorbidities (hypertension, heart failure, pulmonary hypertension), hypoxia-induced erythropoiesis [11] with subsequent hematological alterations and muscular mitochondrial dysfunctions also contribute to a decreased exercise tolerance [10,12]. The cardiopulmonary exercise testing (CPET) provides an integrative assessment of the cardiopulmonary, muscular, neuropsychological and hematopoietic systems, which directly impact the individual's functional capacity [13]. CPET is a valuable cardiovascular instrument for risk stratification and prognosis assessment, helping to establish a personalized exercise training program for OSA patients. Current literature [14] offers conflicting results regarding CPET results in OSA patients and the role of CPAP in improving exercise performance. As such, the purpose of this study is to evaluate the impact of short-term (8 weeks) CPAP therapy on exercise capacity of patients with moderate-severe OSA and cardiometabolic comorbidities.

#### **2. Materials and Methods**

We performed a prospective study that included newly diagnosed patients with moderate-severe OSA (prior to the initiation of CPAP therapy), admitted in our local cardiovascular rehabilitation clinic between October 2017 and December 2018. OSA diagnosis was made by ambulatory or in-hospital six-channel cardio-respiratory polygraphy, using either a Philips Respironics Alice Night One or a DeVilbiss Porti 7 device. The recordings were manually scored by a trained physician, according to the American Academy of Sleep Medicine (AASM) standards. Patients with an apnea–hypopnea index (AHI) of 15–30 and >30 were considered to have moderate and severe OSA, respectively. A Philips Respironics DreamStation Auto CPAP or a Resmed Airsense 10 Autoset were used for CPAP effective pressure autotitration in the sleep laboratory.

All patients signed a written informed consent for inclusion. The study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the Ethics Committee of the "Grigore T. Popa" University of Medicine and Pharmacy in Ias,i (ethical approval code 1183). All subjects underwent physical examination, lipid profile, cardiopulmonary exercise testing and were asked to complete the Epworth questionnaire, before and after 2 months of CPAP therapy. Obesity was defined as a body mass index (BMI <sup>≥</sup> 30 kg/m2). High blood pressure (HBP) was defined as current BP lowering treatment, prior diagnosis of HBP or resting BP values greater than 140 and 90 mmHg for systolic and diastolic BP, respectively. Dyslipidemia was defined as total cholesterol ≥ 200 mg/dL and/or triglycerides ≥ 150 mg/dL. Ischemic heart disease was defined as history of myocardial infarction or prior angiographically documented significant coronary artery stenosis. According to the results of the Epworth questionnaire, daytime sleepiness was categorized as normal, mild, moderate and severe (0–10 points, 11–12 points, 13–15 points and 16–24 points, respectively). Functional capacity was assessed according to peak oxygen uptake (VO2), using the Weber classification, as follows: Weber A (little or no impairment): >20 mL/kg/min, Weber B (mild to moderate impairment): 16–20 mL/kg/min, Weber C (moderate to severe impairment): 10–16 mL/kg/min and Weber D (severe impairment) < 10 mL/kg/min.

CPET was performed under the direction of a certified pulmonologist on the Piston PRE-201 ergospirometer. This started with a 2 min resting period followed by 1 min warm up pedaling against no resistance and an incremental test protocol of 10 W/min. The CPET was performed under continuous heart rate (HR), ECG (electrocardiographic) and pulse oximetry (SpO2) monitoring. BP was recorded every 2 min. Indications for exercise termination included extreme fatigue, myocardial ischemia, complex ventricular premature beats, grade 2 or grade 3 atrio-ventricular block, a sudden drop in BP levels by more than 20 mmHg, increased BP (systolic blood pressure (SBP) > 220 mmHg, diastolic blood pressure (DBP) > 120 mmHg), SpO2 < 80%, confusion, dizziness and sudden pallor.

Statistical analysis was performed in SPSS v. 20.0, using chi-square and student's t-test for comparisons between groups. A potential relationship between variables was evaluated using Pearson's correlation coefficient. The ANCOVA test was used for BMI-adjusted comparison of CPET performance before and after CPAP use. Descriptive data were expressed as means ± SD (standard deviation) or percentages, as appropriate. A *p* value < 0.05 was considered statistically significant.

#### **3. Results**

Our initial study group included 64 patients aged 36–79 years old (57.53 ± 8.74 years old), mean BMI 34.04 <sup>±</sup> 5.30 kg/m2, with newly diagnosed OSA (AHI 39.96 <sup>±</sup> 19.04 events/h, desaturation index 38.67 ± 19.67 events/h, average nocturnal SpO2 91.63% ± 3.64%, CPAP pressure 11.27 ± 2.43 cmH20). Almost two-thirds of our study group presented severe OSA (59.37%) (Figure 1). Male sex was predominant in our study group, with a M/F ratio of 2.55 (Figure 2). Cardiometabolic comorbidities (particularly hypertension) were highly prevalent among our patients (Figure 3).

**Figure 1.** Prevalence of moderate and severe obstructive sleep apnea (OSA) in our study group.

**Figure 2.** Gender distribution in our study group.

**Figure 3.** Prevalence of cardio-metabolic comorbidities in our study group.

Of our patients, 49.21% exhibited a moderate or severe decrease in functional capacity, according to the Weber classification (Weber C or D) (Figure 4). Only one in five patients with moderate-severe OSA had a normal functional capacity (Weber A). We found no significant differences regarding average AHI values between the four functional capacity subgroups (Weber A to D) (*p* > 0,05).

Apart from maximal instantaneous forced expiratory flow (MEF)25% that was higher in the severe OSA subgroup, we did not find any statistically significant differences regarding spirometry results between patients with moderate and severe OSA (Table 1).


**Table 1.** Spirometry results in patients with moderate-severe OSA.

FVC—forced vital capacity; FVC%—percent predicted forced vital capacity; FEV1—forced expiratory volume in one second; FEV1%—percent predicted forced expiratory volume in one second; PEF—peak expiratory flow; PEF%—percent predicted peak expiratory flow; MEF—maximal instantaneous forced expiratory flow; MEF%—percent predicted maximal instantaneous forced expiratory flow.

CPET performance was influenced by gender but not by apnea severity (Tables 2 and 3).


**Table 2.** Gender influence on cardiopulmonary exercise testing (CPET) parameters among patients with moderate-severe OSA.

CPET—cardiopulmonary stress test; OSA—obstructive sleep apnea; BMR—basal metabolic rate; VO2—peak oxygen uptake; AT—anaerobic threshold; VCO2—peak CO2 output; VE—minute ventilation; HR—heart rate; SBP—systolic blood pressure; DBP—diastolic blood pressure.



CPET—cardiopulmonary stress test; OSA—obstructive sleep apnea; BMR—basal metabolic rate; VO2—peak oxygen uptake; AT—anaerobic threshold; VCO2—peak CO2 output; VE—minute ventilation; HR—heart rate; SBP—systolic blood pressure; DBP—diastolic blood pressure.

Except for baseline SBP, CPET parameters did not significantly differ between the two apnea severity subgroups (Table 2). Basal metabolic rate (BMR) and minute ventilation (VE) max were significantly higher among males (Δ = 366 kCal/24 h and Δ = 8.35 L/min, respectively). Although males achieved a higher average peak workload (Δ = 34.07 W), % predicted workload and % predicted VO2 max were significantly higher in the female subgroup (Δ = 13.33% and Δ = 20.24%, respectively).

Apnea severity was significantly correlated with resting HR (r = −0.30, *p* = 0,01) (Figure 5), % predicted workload (r = −0.30, *p* = 0.01) (Figure 6) and BMR (r = 0.33, *p* = 0.008) (Figure 7) (Table 4). We did not find any statistically significant correlations between AHI and the analyzed spirometry parameters (*p* > 0.05).

**Figure 5.** Correlation between apnea severity and resting heart rate among patients with moderate-severe OSA (r = 0.25, *p* = 0.04). HR—heart rate; AHI—apnea hypopnea index; OSA—obstructive sleep apnea.

**Figure 6.** Correlation between apnea severity and % predicted peak workload among patients with moderate-severe OSA (r = −0.30, *p* = 0.01). AHI—apnea hypopnea index; OSA—obstructive sleep apnea.

**Figure 7.** Correlation between apnea severity and BMR among patients with moderate-severe OSA (r = 0.33, *p* = 0.008). BMR—basal metabolic rate; AHI—apnea hypopnea index; OSA—obstructive sleep apnea.

**Table 4.** Correlations between AHI and CPET results among patients with moderate-severe OSA.


AHI—apnea hypopnea index; CPET—cardiopulmonary stress test; OSA—obstructive sleep apnea; BMR—basal metabolic rate; VO2—peak oxygen uptake; AT—anaerobic threshold; VCO2—peak CO2 output; VE—minute ventilation; HR—heart rate.

All subjects started appropriate continuous positive airway pressure therapy. Thirteen patients were unable to tolerate CPAP or were lost during follow-up. Fifty-one patients successfully completed the CPET and the Epworth questionnaires before and after 2 months of CPAP.

The average Epworth score in our study group was 8.11 ± 5.23 points. Average CPAP use was 241.67 (±128.38) minutes/night. Only 51.16% of our patients used the device as recommended—at least 4 h/night. CPAP use did not significantly impact basal blood pressure values (SBP Δ = −4.58 mmHg, *p* = 0.13; DBP Δ = −1.52 mmHg, *p* = 0.35) and was not associated with statistically significant weight loss (Δ = −1.01 kg, *p* = 0.57).

After 2 months of CPAP our study group exhibited significant improvements in maximal exercise load (Δ = 14.23 W, *p* = 0.0004), VO2 max (Δ = 203.87 mL/min, *p* = 0.004), anaerobic threshold (AT) (Δ = 316.4 mL/min, *p* = 0.001) and VE max (Δ = 5.1 L/min, *p* = 0.01) (Table 5, Figures 8 and 9). Maximal exercise load and VO2 max improvement remained significant after adjustment for BMI (Table 5. *p* = 0.04 and *p* = 0.02, respectively). We also observed an increase in peak oxygen pulse (Δ = 2.46, *p* = 0.007) and VCO2 max (Δ = 232.14 mL/min, *p* = 0.0006), which remained significant after adjusting for BMI (Table 5, Figures 8 and 9, *p* = 0.02 and *p* = 0.01, respectively). The Epworth score in our study group decreased by 4.58 points (*p* < 0.000001).


**Table 5.** CPAP impact on CPET parameters in moderate-severe OSA patients.

CPAP—continuous positive airway pressure; CPET—cardiopulmonary stress test; OSA—obstructive sleep apnea; BMR—basal metabolic rate; VO2—peak oxygen uptake; AT—anaerobic threshold; VCO2—peak CO2 output; VE—minute ventilation; HR—heart rate; SBP—systolic blood pressure; DBP—diastolic blood pressure; *p* \*—statistical significance for non-adjusted student's t-test; *p* \*\*—statistical significance for BMI-adjusted results of ANCOVA test.

**Figure 8.** CPAP induced changes in BMR (Δ = −8.38 kCal/24 h, *p* = 0.04), peak VO2 (Δ = 203.87 mL/min, *p* = 0.004), AT (Δ = 316.4 mL/min, *p* = 0.001) and peak VCO2 max (Δ = 232.14 mL/min, *p* = 0.0006). BMR—basal metabolic rate; peak VO2—peak oxygen uptake; AT—anaerobic threshold; peak VCO2—peak CO2 output.

**Figure 9.** CPAP induced changes in peak workload (Δ = 14.23 W, *p* = 0.0004), % predicted peak workload (Δ = 8.86 %, *p* = 0.0001), % predicted peak VO2 (Δ = 12.28 %, *p* = 0.000005), % predicted VE max (Δ = 4.45 %, *p* = 0.01) and resting HR (Δ = −4.18 bpm, *p* = 0.05), in moderate-severe OSA patients. CPAP—continuous positive airway pressure; OSA—obstructive sleep apnea; VO2—peak oxygen uptake; VE—minute ventilation; HR—heart rate.

#### **4. Discussion**

Our study included 64 patients aged 57.53 ± 8.74 years old with newly diagnosed moderate-severe OSA. This value is slightly higher than other reports concerning average OSA age at diagnosis (40–50 years old) [15]. Female sex hormones increase genioglossus contractility and prevent upper airway collapsibility during sleep [16,17]. Furthermore, the distinctive distribution of adipose tissue among the two genders (with central obesity being more strongly associated with OSA) [18], as well as the higher pharyngeal resistance in men [19], explain why OSA is more prevalent among male patients. Despite the evident predominance of the male sex in our study group, our male/female ratio is slightly lower than in previous studies (2.55:1 vs. 3:1–5:1) [20].

Similar to other literature reports [21], the main reason for premature test halt was dyspnea accompanied by muscular exhaustion. Extreme fatigue in OSA patients can be explained by the presence of energetic mitochondrial dysfunctions especially in muscle cells [12]. An exaggerated SBP response (SBP > 250 mmHg) was the second reason for premature test halt. None of our patients presented arrhythmic events, confusion or a decrease in BP values during exercise.

Our moderate-severe OSA patients presented baseline mediocre CPET performance. Only 20.63% of our subjects had a baseline normal functional capacity according to the Weber classification, and most cases (34.92%) were classified as moderately impaired. In comparison, Przybyłowski et al. [22] reported an overall better CPET performance in 111 obese OSA patients (% predicted peak VO2 85.3 ± 17.8, peak VCO2 2800 ± 900 mL/min, VE max 91.2 ± 24.7, % predicted maximum HR 92.5 ± 10.3), despite a minimal difference in OSA severity between the two groups (average AHI 47.2 ± 23.1 vs. 39.96 ± 19.04 events/h). However, Przybyłowski's study group included an unusually low percentage of hypertensives (29% vs. 95.31% in our study group), signaling that HBP could be an important confounding factor when analyzing CPET performance.

Previous reports regarding the impact of OSAS on cardiopulmonary exercise testing performance have conflicting results and included a limited number of patients [10,12,23–25]. Most studies that associated OSAS with an impaired exercise capacity (decreased exercise duration, workload, VO2, oxygen pulse, AT and/or VE max) were conducted on obese or overweight subjects [10,12,25]. Therefore, these reports could be biased by the known negative impact of obesity on exercise capacity, as shown by Rizzi et al. [26].

Consistent with this theory, another report [27] found that CPET performance is similar among normoponderal OSAS patients and controls, although it is worth mentioning that the analyzed group had a relatively low average AHI (15.4 ± 9.2) and included an unusually large proportion of females (63%).

Powell et al. [28] studied exercise performance among military personnel with and without moderate-severe OSAS. The lack of significant differences among the two subgroups could be explained by the low average age in the OSAS and control groups (40.7 and 39.4, respectively) but also by the higher grade of habitual physical activity (characteristic for this population subset) [29].

However, a recent meta-analysis [29] has shown that VO2 max is significantly lower in OSA subjects compared to controls (Δ = 2.7 mL/kg/min), the difference being of greater clinical impact among non-obese patients (Δ = 4.1 mL/kg/min).

Rizzi et al. [26] reported that male sex associated with diabetes negatively impacts VO2 max. Consistent with their results, our female subgroup obtained significantly higher percent predicted workload and percent-predicted maximum HR, suggesting a higher effort capacity.

Apnea severity was previously correlated with several CPET parameters including VO2 max [25], percent predicted peak VO2 [30] and BP rise during exercise [22]. However, our analysis only found a significant association between AHI resting HR, BMR and percent predicted workload. The high prevalence of cardio-metabolic comorbidities in our study group (especially obesity and hypertension) could explain the lack of statistically significant correlations between AHI and other CPET variables.

Previous studies [24,25,29] reported that OSAS patients have higher DBP values and decreased HR recovery compared to controls. When analyzing the two apnea severity subgroups, we observed significantly higher baseline and AT-SBP values (but no significant differences regarding HR response during exercise) in the severe OSA subgroup.

Literature reports regarding the impact of CPAP on VO2 max in OSAS patients have yielded inconsistent results. Different CPAP therapy lengths (1 week–8 months) were associated with significant VO2 max improvements [29,31–33]. However, in another study [34], VO2 max displayed a mild negative trend (22.52 ± 6.62 mL/min/kg to 21.32 ± 5.26 mL/min/kg; *p* = 0.111) in CPAP compliant patients and a borderline statistically significant decline in patients with suboptimal CPAP use (21.31 ± 5.66 mL/min/kg to 19.92 ± 5.40 mL/min/kg, *p* = 0.05).

Despite a mediocre CPAP adherence (241.67 min/night), our patients exhibited a significant improvement in percent predicted maximum workload, percent predicted VO2 max, AT and oxygen pulse. Improvements regarding maximal load, VO2 max, VCO2 max, %VE and peak O2 pulse remained significant even after adjusting for BMI. We observed no statistically significant gender-related differences regarding these changes. Quadri et al. [33] also studied the effect of 2 months of CPAP in a smaller group of moderate-severe OSAS patients and reported similar improvements in percent predicted maximum workload (9 vs. 8.86 W%) and percent predicted VO2 peak (9.7% vs. 12.28%) but a less marked increase regarding AT (99 vs. 316.4 mL/min). On the other hand, Tapan et al. [21] analyzed the benefit of 8 weeks of CPAP in patients with severe OSA and observed a greater improvement in maximum workload and VE (16.9 W and 10.3 L/min respectively) but a less important increase in percentage-predicted peak VO2 (7.6% vs. 12.28%).

Previous research [35] reported diurnal variations in spirometric indices in OSA patients (especially among males). The same study [35] observed significant associations between AHI, evening forced expiratory volume in one second (FEV1) and forced vital capacity (FVC) and demonstrated the important influence of BMI, hypertension, dyslipidemia and several cardiovascular drugs on the relationship between lung function and apnea severity. The fact that most of our patients presented cardio-metabolic comorbidities, and were under treatment with a statin, beta blocker or a renin-angiotensin-aldosterone axis inhibitor [35], could explain the lack of association between AHI and the analyzed spirometry parameters (*p* > 0.05).

The main limitations of our study are the lack of a control group and the high prevalence of cardio-metabolic comorbidities among our patients. Although obesity and hypertension are important confounders regarding the decrease in CPET performance described in OSAS patients, the presence of these comorbidities reflects the typical, everyday OSAS patient and, in our opinion, should not be excluded from analysis. Although baseline CPET results did not significantly differ between the two apnea severity subgroups, the fact that our 2 months of CPAP improved most CPET parameters in the absence of statistically significant weight loss (Δ = −1.01 kg, *p* = 0.57) or basal BP changes (SBP Δ = −4.58 mmHg *p* = 0.13; DBP Δ = −1.52 mmHg *p* = 0.35) suggests that OSAS per se impacts exercise capacity.

#### **5. Conclusions**

Moderate-severe OSA patients have a mediocre baseline CPET performance. AHI was correlated with some CPET parameters (BMR, % predicted effort, resting HR) but not with VO2 or AT. Two months of CPAP improved most CPET parameters (in the absence of statistically significant weight loss or basal BP changes) suggesting that OSAS per se negatively impacts effort capacity.

**Author Contributions:** All authors have equally contributed to this work. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

### *Article* **Circulating Soluble Urokinase-Type Plasminogen Activator Receptor in Obstructive Sleep Apnoea**

**Renata Marietta Bocskei 1,2 , Martina Meszaros 1, Adam Domonkos Tarnoki 3, David Laszlo Tarnoki 3, Laszlo Kunos 1, Zsofia Lazar <sup>1</sup> and Andras Bikov 1,4,\***


Received: 4 January 2020; Accepted: 5 February 2020; Published: 14 February 2020

**Abstract:** *Background and Objectives*: Obstructive sleep apnoea (OSA) is associated with heightened systemic inflammation and a hypercoagulation state. Soluble urokinase-type plasminogen activator receptor (suPAR) plays a role in fibrinolysis and systemic inflammation. However, suPAR has not been investigated in OSA. *Materials and Methods*: A total of 53 patients with OSA and 15 control volunteers participated in the study. Medical history was taken and in-hospital sleep studies were performed. Plasma suPAR levels were determined by ELISA. *Results*: There was no difference in plasma suPAR values between patients with OSA (2.198 ± 0.675 ng/mL) and control subjects (2.088 ± 0.976 ng/mL, *p* = 0.62). Neither was there any difference when patients with OSA were divided into mild (2.134 ± 0.799 ng/mL), moderate (2.274 ± 0.597 ng/mL) and severe groups (2.128 ± 0.744 ng/mL, *p* = 0.84). There was no significant correlation between plasma suPAR and indices of OSA severity, blood results or comorbidities, such as hypertension, diabetes, dyslipidaemia or cardiovascular disease. Plasma suPAR levels were higher in women when all subjects were analysed together (2.487 ± 0.683 vs. 1.895 ± 0.692 ng/mL, *p* < 0.01), and also separately in controls (2.539 ± 0.956 vs. 1.411 ± 0.534 ng/mL, *p* = 0.02) and patients (2.467 ± 0.568 vs. 1.991 ± 0.686 ng/mL, *p* < 0.01). *Conclusions*: Our results suggest that suPAR does not play a significant role in the pathophysiology of OSA. The significant gender difference needs to be considered when conducting studies on circulating suPAR.

**Keywords:** biomarkers; fibrinolysis; inflammation; OSAHS; sleep disordered breathing

#### **1. Introduction**

Obstructive sleep apnoea (OSA) is a common disease which is characterised by repetitive collapse of the upper airways during sleep which results in intermittent hypoxia and frequent microarousals. These processes lead to the development of cardiometabolic comorbidities, such as hypertension, cardiovascular disease, diabetes and dyslipidaemia, which frequently accompany OSA.

Chronic intermittent hypoxia and increased sympathetic tone induce production of pro-inflammatory molecules, such as interleukin (IL)-6, IL-1β, tumour necrosis factor-α [1] or complement elements [2] and suppresses the release of anti-inflammatory [3,4] molecules. Linked to inflammation and sympathetic activity, OSA is characterised by a hypercoagulation state [5–9]. Accelerated systemic inflammation and increased coagulation may contribute to the development of cardiovascular disease and acute cardiovascular events [10].

Soluble urokinase-type plasminogen activator receptor (suPAR) is a molecule which plays a role in both inflammation and coagulation. It is produced upon cleavage of the membrane-bound urokinase-type plasminogen activator receptor (uPAR). The cleavage is facilitated by urokinase-type plasminogen activator (uPA), plasmin, matrix metalloproteases, neutrophil elastase and cathepsin G [11]. The urokinase receptor (also known as uPAR) is expressed by endothelial cells, macrophages, monocytes, neutrophils, lymphocytes, smooth muscle cells and fibroblasts [12,13]. It is upregulated under infections and as an effect of pro-inflammatory cytokines [11,13–15], while suPAR contributes to plasminogen activation, cell adhesion, chemotaxis and immune cell activation [16]. However, uPAR also acts as a scavenger receptor for uPA, inhibiting its actions [17]. In large studies, plasma suPAR was elevated in coronary artery disease and cerebrovascular disease and correlated with their severity and cardiovascular mortality [18–20]. Higher suPAR levels were also observed in obesity [21], which is the main etiological factor for OSA in the Western population [22].

Only one study has investigated suPAR in probable OSA so far [23]. Patients were categorised to high and low-risk OSA based on the Berlin questionnaire and neck circumferences; however, no objective sleep tests were performed. In this study, there was a tendency for higher suPAR levels in the high-risk group, but the difference did not reach a significant level [23].

We hypothesised that circulating suPAR concentrations are elevated in OSA compared to health and probably relate to disease severity. The aim of the study was to investigate these using standardised diagnostic tests.

#### **2. Materials and Methods**

#### *2.1. Study Design and Subjects*

We recruited 68 volunteers (54 ± 13 years, 36 men) who were referred for a sleep study to the Sleep Unit, Department of Pulmonology, Semmelweis University due to suspected OSA (i.e., snoring, daytime tiredness, obesity, comorbidities). After giving informed consent, medical history was taken and patients filled out the ESS, which was followed by in-laboratory cardiorespiratory polygraphy (*n* = 20) or polysomnography (*n* = 48). In the morning, blood pressure was measured; fasting venous blood was taken for lipid profile, glucose, creatinine, C-reactive protein (CRP) and suPAR measurements between 6:00 and 8:00 a.m. Glomerular filtration rate (GFR) was calculated using the Modification of Diet in Renal Disease equation.

Comorbidities were defined according to the participants' report, available medical records, medications, morning blood pressures and fasting blood laboratory results. In detail, hypertension was excluded if there was no history for high blood pressure. Participants did not take anti-hypertensive medications, and morning blood pressure was within the normal range. In line with this, diabetes and dyslipidaemia were excluded if there was no history for these comorbidities, participants did not take antidiabetic or anti-dyslipidaemia medications, and the fasting blood glucose and lipid results were in the normal range. Cardiovascular disease was excluded based on absence of symptoms and negative medical history.

#### *2.2. Sleep Studies*

Inpatient polysomnography and cardiorespiratory polygraphy were performed as described previously [2–4] using Somnoscreen Plus Tele PSG (Somnomedics GMBH Germany). Sleep stages, movements and cardiopulmonary events were scored manually according to the American Academy of Sleep Medicine [24] guidelines. Apnoea was defined as a 90% airflow decrease, which lasted for more than 10 s, and hypopnoea was defined as at least 30% airflow decrease lasting for at least 10 s, which was related to a ≥3% oxygen desideration or an arousal. Total sleep time (TST), sleep period time (SPT), total sleep time spent with oxygen saturation below 90% (TST90%) and minimal oxygen saturation (minSatO2) were recorded, and apnoea–hypopnoea index (AHI), oxygen desaturation index

(ODI) and arousal index (AI) were calculated. Obstructive sleep apnoea was defined as having an AHI ≥ 5/h.

#### *2.3. SuPAR Measurements*

Venous blood was taken into EDTA tubes. Within 30 minutes, blood samples were centrifuged at 4 ◦C for 10 min at 1500 rpm, and the plasma was stored at −80 ◦C until further analysis. Plasma suPAR levels were measured using a commercially available ELISA kit (ViroGates A/S, Birkerød, Denmark) as described previously [25]. The samples were measured in duplicates, and the mean concentration was used. The intra-assay coefficient of variation was 9 ± 11% with a lower limit of detection of 0.1 ng/mL. All suPAR concentrations were above the detection limit.

#### *2.4. Statistical Analyses*

Statistica 12 (StatSoft, Inc., Tulsa, OK, USA) was used for statistical analyses. The normality of the data was checked with the Kolmogorov–Smirnov test, which showed normal distribution for suPAR concentrations. Patient and control groups were compared with unpaired t-test, Mann–Whitney, Chi-square and Fisher tests. Plasma suPAR was related to clinical and demographic variables using linear and logistic regression and compared among different OSA severities with general mixed linear models. These analyses were repeated following adjustment for age, gender, body mass index (BMI), type of the sleep tests, anticoagulant and antithrombotic medications and GFR as well. To avoid the confounding effect of hypertension and diabetes, OSA and control groups were compared when subjects affected by these comorbidities were excluded. A *p* value <0.05 was considered significant. The suPAR results are presented as mean ± standard deviation with 95% confidence intervals.

The minimal sample size was estimated to detect differences in plasma suPAR levels between the OSA and control groups with an effect size of 0.80, power of 0.80 and alpha of 0.05 [26]. These numbers were based on a distribution of plasma suPAR values in control subjects [25]. Post-hoc sensitivity analyses ensured it was possible to detect correlations between suPAR and clinical variables within −0.23 and 0.23, minimal and maximal critical r values, statistical power of 0.80 and alpha of 0.05 [26].

The study was approved by the Semmelweis University Ethics Committee (TUKEB 30/2014 and 172/2018, approved on 26 October 2018) and was conducted according to the Declaration of Helsinki. Patients provided their written consent.

#### **3. Results**

#### *3.1. Patient Characteristics*

OSA was diagnosed in 53 cases (6 mild, 25 moderate and 22 severe; AHI 5–14.9/h, 15–29.9/h and ≥30/h, respectively). Patients with OSA had higher BMI, systolic (SBP) and diastolic blood pressure (DBP), AHI, ODI, SPT, TST, TST 90% and lower high density lipoprotein cholesterol (HDL-C) and MinSatO2 compared to controls (all *p* < 0.05, Table 1). In addition, patients with OSA tended to be older (*p* = 0.08) and sleepier (*p* = 0.05), and the prevalence of dyslipidaemia tended to be higher in OSA (*p* = 0.07, Table 1).


**Table 1.** Subjects' characteristics \*.


**Table 1.** *Cont*.

\* Data are presented as mean ± standard deviation or median/25%–75% percentile/. Significant differences are highlighted in bold. AHI—apnoea–hypopnoea index, BMI—body mass index, DBP—diastolic blood pressure, CRP—C-reactive protein, ESS—Epworth Sleepiness Scale, GFR—glomerular filtration rate, HDL-C—high density lipoprotein cholesterol, LDL-C—low density lipoprotein cholesterol, ODI—oxygen desaturation index, SBP—systolic blood pressure, SPT—sleep period time, TST—total sleep time, TST90%—total sleep time spent with oxygen saturation below 90%.

#### *3.2. Circulating suPAR Results*

There was no difference in plasma suPAR concentrations between the controls (2.088 ± 0.976/1.548–2.628/ ng/mL) and patients with OSA (2.198 ± 0.675/2.012–2.384/ ng/mL, unadjusted *p* = 0.62, *p* = 0.99 after adjustment, Figure 1). Similarly, there was no difference between mild (2.134 ± 0.799/1.295–2.974/ ng/mL), moderate (2.274 ± 0.597/2.028–2.521/ ng/mL) and severe patients (2.128 ± 0.744/1.798–2.458/ ng/mL, unadjusted *p* = 0.84, *p* = 0.78 after adjustment, Figure 2). In line with this, there was no relationship between plasma suPAR levels and AHI (*p* = 0.65), ODI (*p* = 0.58), TST90% (*p* = 0.35), minSatO2 (*p* = 0.16), AI (*p* = 0.38), TST (*p* = 0.60), SPT (*p* = 0.41) or ESS (*p* = 0.44). We noted two outliers in the control group. These subjects did not differ in their demographics or clinical characteristics from other controls. Excluding them from analyses resulted in tendency for higher suPAR levels in OSA (*p* = 0.050); however, after adjustment for covariates this difference was not significant (*p* = 0.259).

Plasma suPAR directly correlated with age when all subjects were analysed together (r = 0.33, *p* < 0.01), or when patients with OSA were investigated separately (r = 0.30, *p* = 0.02). However, when adjusting for covariates, these correlations were no longer significant (both *p*>0.05).

Plasma suPAR levels were higher in women when all subjects were analysed together (2.487 ± 0.683/2.241–2.733/ vs. 1.895 ± 0.692/2.221–2.713/ ng/mL, *p* < 0.01), in controls (2.539 ± 0.956/1.804–3.474/ vs. 1.411 ± 0.534/0.851–1.971/ ng/mL, *p* = 0.02) and in OSA (2.467 ± 0.568/2.221–2.713/ vs. 1.991 ± 0.686/1.735–2.247/ ng/mL, *p* < 0.01, Figure 3). These intergender differences remained significant even after adjustment for covariates. Due to the asymmetric gender distribution in the OSA and control groups, plasma suPAR levels were compared in control and OSA women and men separately. There was no difference in women (2.467 ± 0.568/2.221–2.713/ vs. 2.539 ± 0.956/1.804–3.474/ ng/mL, OSA vs. controls, *p* = 0.79). However, plasma suPAR tended to be higher in male patients with OSA (1.991 ± 0.686/1.735–2.247/ ng/mL, *n* = 30) compared to controls (1.411 ± 0.534/0.851–1.971/ ng/mL, *n* = 6,

*p* = 0.059). Despite this potential signal, there was no relationship between AHI and suPAR levels in either men or women (both *p* > 0.05).

**Figure 1.** Comparison of plasma soluble urokinase-type plasminogen activator receptor (suPAR) levels between patients with OSA and controls. There was no difference between the two groups in plasma suPAR levels (*p* = 0.62). Mean ± standard deviation is presented.

**Figure 2.** Comparison of plasma suPAR levels among different disease severities. There was no difference among the groups in plasma suPAR levels (*p* = 0.87). Mean ± standard deviation is presented.

**Figure 3.** Comparison of plasma suPAR levels between women and men. Plasma suPAR levels were higher in women in patients with OSA, in controls and when the subjects were analysed together. Mean ± standard deviation is presented.

Patients with OSA who took anticoagulants had higher plasma suPAR levels (2.739 ± 0.547/2.164–3.313/ vs. 2.129 ± 0.663/1.934–2.323/ ng/mL, *p* = 0.03); however, this difference became insignificant after adjusting for covariates (*p*>0.05). None of the other correlations between plasma suPAR concentrations, demographics, clinical variables or comorbidities, such as hypertension, diabetes, dyslipidaemia or cardiovascular disease were significant in any of the studied groups (all *p*>0.05).

#### *3.3. Plasma suPAR Results in Control and OSA Participants without Hypertension or Diabetes*

There was no difference in plasma suPAR levels when controls without hypertension (1.903 ± 0.922/1.244–2.562/ ng/mL, *n* = 10) were compared to OSA patients without hypertension (2.110 ± 0.671/1.753–2.467/ ng/mL, *n* = 16, *p* = 0.51). Similarly, no difference was found between controls without diabetes (2.088 ± 0.976/1.548–2.628/ ng/mL, *n* = 15) and OSA patients without diabetes (2.203 ± 0.676/2.002-2.403/, *n* = 46, *p* = 0.61). In line with this, there was no difference when controls without hypertension and diabetes (1.903 ± 0.922/1.244–2.562/ ng/mL, *n* = 10) were compared to OSA patients without hypertension and diabetes (2.165 ± 0.656/1.802–2.528/ ng/mL, *n* = 15, *p* = 0.41).

#### **4. Discussion**

In the current study, we analysed plasma suPAR levels in OSA, but did not find any difference compared to controls, nor did suPAR concentrations correlate with disease severity. This implies that suPAR may not play a significant role in the pathophysiology of OSA; however, due to the small number of controls and the significant gender effect on suPAR levels, our results must be interpreted carefully.

Obstructive sleep apnoea is associated with heightened systemic inflammation, theoretically contributing to higher uPAR expression [11,13–15]. However, the cleavage of uPAR may be slower in OSA due to decreased levels of uPA [8] and plasmin [5,6,23]. The expression of uPA is induced by female sexual hormones [20] and the proto-oncogenic survivin [27], which presented decreased expressions in OSA [3,28]. Plasmin is formed by plasminogen, and this reaction is blocked by plasminogen activator-inhibitors (PAI), shown to be upregulated in OSA [5,6,23]. In addition, OSA is associated with decreased levels of transforming growth factor-β [8], an inducer of uPAR transcription [29,30]. These studies suggest that although uPAR expression may be upregulated by systemic inflammation in OSA [11,13–15]; this is counterbalanced by the reduced cleavage.

It has been shown that plasma suPAR levels are higher in women [20] and related to BMI and waist circumference only in females [20]. In addition, plasma suPAR levels were prognostic for cardiovascular events only in women [19] and more strongly related to coronary artery calcification in women than in men [31]. Our results are in line with the previous findings [20], namely that suPAR was higher in women in both OSA and controls. A potential reason for the gender differences is that uPA is released upon stimulation by progesterone and oestradiol [32] resulting in higher uPAR cleavage. Female sexual hormones are protective in OSA [33], contributing to male predominance in sleep apnoea [33,34]. To exclude this effect, analyses were performed after adjustment for gender and suPAR was compared between OSA and controls in women and men separately. Although there was a tendency for higher suPAR levels in men in OSA, there was no relationship with OSA severity in males. Of note, the number of men in the control group was small, and these analyses were underpowered. Nevertheless, this difference could be a potential signal which should be investigated in further studies. We believe that our current results would provide basis for further study design. In line with the previous findings [35], plasma suPAR levels were directly related to age; however, this correlation disappeared after adjustment for covariates. Although higher suPAR levels were associated with obesity [21], this has not been confirmed by the current study.

Our study has limitations. First, the sample size, especially in the control group, was low. This could have potentially led to type II error, especially due to significant difference in age, gender and comorbidity distribution. To avoid this, our analyses were adjusted on potential confounders. The plasma suPAR levels were not different between patients with OSA and controls either in unadjusted or adjusted comparisons. Still, our results should be interpreted carefully, especially considering the

exclusion of the two outliers which resulted in differences between the two groups. The sample size calculations were based on our previous study [25], showing higher suPAR levels in COPD. Although the number of participants may seem low, it may not be the likely reason for the lack of differences between OSA and controls considering the wide overlap of suPAR values between the two groups and the lack of significant relationship between markers of OSA severity and suPAR levels. In line with this, a second limitation is the unbalanced proportion of comorbidities in the OSA and control groups.

Elevated suPAR levels are associated with cardiovascular disease and diabetes [36]. OSA represents a risk for cardio metabolic disease [22], which was reflected in the asymmetric proportion of comorbidities in the OSA and control groups. However, we did not find any relationship between plasma suPAR concentrations and comorbidities. To further evaluate this, we performed additional analyses in participants without hypertension or diabetes. We did not find any difference in plasma suPAR values between controls and patients with OSA in non-hypertensive or nondiabetic volunteers. Of note, the study has not been powered to address this question. We believe our results could provide a basis to design further studies involving groups balanced on the profile of comorbidities. The third limitation is that although patients represented a large range of OSA severity, in average, they were minimally symptomatic. It has recently been reported that patients with OSA and excessive daytime sleepiness have a higher risk for cardiovascular disease [37]. Inclusion of more symptomatic patients in studies investigating systemic inflammation is therefore warranted. The fourth limitation is the significant gender-effect which has been discussed above. The strengths of the study include the application of objective sleep tests, detailed characterisation of the studied population and robust methodology for plasma suPAR measurement.

Only one study has examined suPAR in possible OSA. Von Kanel et al. divided 329 South African teachers based on their response to the Berlin questionnaire and/or neck circumference into a high-risk and low-risk OSA group. Most notably, no objective sleep study has been performed. Although the levels of fibrinogen and PAI-1 were elevated together with slower clot lysis time, there was only a tendency for higher suPAR levels in the high-risk group [23]. The Berlin questionnaire is a moderately sensitive, but not specific screening tool for OSA [38]; therefore, these results must be interpreted carefully. Nevertheless, the previous [23] and the current findings indicate that hyper-coagulation in OSA is driven by high fibrin formation, reduced plasminogen activation by increased PAI-1 and lower uPA without a significant difference in the uPAR signalling.

#### **5. Conclusions**

In conclusion, we did not find altered plasma suPAR levels in patients with OSA vs. controls. Our results suggest that this molecule does not play a significant role in hyper coagulation and accelerated systemic inflammation in OSA and cannot be applied as a readout signal for these pathophysiological processes. However, the significant gender differences are noteworthy and must be considered when designing future studies with suPAR.

**Author Contributions:** R.M.B. designed the study and acquired funding. M.M. performed ELISA measurements and sleep study analyses. A.D.T., D.L.T. and Z.L. participated in the design and contributed to recruiting and clinically characterising the patients. L.K. performed sleep study analyses. A.B. designed the study, drafted the manuscript and performed statistical and sleep study analyses. All authors read and approved the final manuscript.

**Funding:** The study was supported by Hungarian Respiratory Society grants to Andras Bikov and David L. Tarnoki as well as Semmelweis University grant to Laszlo Kunos. This publication was supported by the Janos Bolyai Research Scholarship of the Hungarian Academy of Sciences to Andras Bikov. Andras Bikov is supported by the NIHR Manchester BRC.

**Acknowledgments:** The authors are also grateful to Elektro-Oxigén Inc. for providing polysomnographic devices and Monika Banlaky for her assistance in sleep studies.

**Conflicts of Interest:** The authors state no conflict of interest. The funders had no role in the design of the study, collection, analyses, interpretation of data, writing of the manuscript or the decision to publish the results.

#### **References**


© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

### *Article* **The Association of Obstructive Sleep Apnea Syndrome and Accident Risk in Heavy Equipment Operators**

#### **Hakan Celikhisar 1,\* and Gulay Dasdemir Ilkhan <sup>2</sup>**


Received: 25 June 2019; Accepted: 10 September 2019; Published: 17 September 2019

**Abstract:** *Background and Objectives*: Obstructive sleep apnea syndrome (OSAS) is the most frequent sleep disorder, characterized by the repeated collapse of the upper respiratory tract during sleep. In this study, we aimed to determine the prevalence of OSAS in heavy equipment operators and to determine the relationship between the work accidents that these operators were involved in and the OSAS symptoms and severity. In doing this, we aimed to emphasize the association of OSAS, which is a treatable disease, and these accidents, which cause loss of manpower, financial hampering, and even death. *Materials and Methods*: STOP BANG questionnaire was provided to 965 heavy equipment operators and polysomnography (PSG) was performed, in Izmir Esrefpasa Municipality Hospital, to the operators at high risk for OSAS. Demographic data, health status, and accidents of these operators were recorded. *Results*: All operators who participated in the study were male. The ages of the cases ranged from 35 to 58 and the mean age was 45.07 ± 5.54 years. The mean STOP BANG questionnaire results were 4.36 ± 3.82. In total, 142 operators were identified with high risk for OSAS and PSG could be performed on 110 of these 142 operators. According to the PSG results of the operators, 41 (37.3%) patients had normal findings, while 35 (31.8%) had mild, 20 (18.2%) had moderate, and 14 (12.7%) had severe OSAS. Among those 110 patients, 71 (64.5%) of the cases had no history of any accidents, 25 (22.8%) were almost involved in an accident due to sleepiness, and 14 (12.7%) were actually involved in an accident. There was a statistically significant relationship between the accident rate and OSAS severity (*p*: 0.009). *Conclusion*: Based on the data acquired in the present study, a positive correlation was determined between the accident statuses of drivers with OSAS severity. We want to attract attention to the necessity of evaluating the OSAS symptoms in professional heavy equipment operators during the certification period and at various intervals afterwards, and to carry out OSAS evaluations by PSG for those having a certain risk.

**Keywords:** polysomnography; heavy equipment operators; STOP BANG Questionnaire; Obstructive Sleep Apnea Syndrome

#### **1. Introduction**

Sleep is the temporary, partial, periodic, and reversible loss of the communication of the organism with the environment, which is an indispensable factor for a healthy life [1]. Obstructive sleep apnea syndrome (OSAS) is characterized by the repeated collapse of the upper respiratory tract during sleep, causing nocturnal hypoxemia and interrupted sleep [1]. It is the most frequent sleep disorder. OSAS prevalence has been determined as 3%–7% for males and 2%–5% for females all over the world [2].

The most common night symptom of OSAS is snoring, while the day symptom is excessive sleepiness [3,4]. The most important risk factors are indicated as male gender, advanced age, neck circumference, and obesity [5].

Various questionnaires are used for identifying risky groups and Berlin questionnaire is one of these arranged for community screenings. There are a total of 10 questions in three categories. Positive results in two or more categories indicate that the participant is carrying high risk [6]. STOP-BANG is another questionnaire defined to have high sensitivity to predict OSAS [7]. Polysomnography (PSG) is the gold standard in OSAS diagnosis and treatment management [5]. OSAS has been classified into three different classes according to apnea hypopnea index (AHI) as mild OSAS (AHI = 5–15), moderate OSAS (AHI = 15–30), and severe OSAS (AHI > 30) in accordance with the American Academy of Sleep Medicine Criteria [5]. Continuous positive air pressure (CPAP) is the standard treatment for OSAS [8,9].

Even though the nighttime symptoms of OSAS are generally ignored by the patient, its daytime symptoms are quite striking. Daytime excessive sleepiness may be so severe that it may affect work performance, prevent driving a vehicle carefully, and increase the accident risks [3]. For that reason, patients under high risk for OSAS should be diagnosed and treated. In this study, we aimed to determine the prevalence of OSAS in heavy equipment operators and to determine the relationship between the work accidents that these operators were involved in and the OSAS symptoms and severity. In this way, we aimed to emphasize the association of OSAS, which is a treatable disease, and these work accidents causing loss of manpower, financial hampering, and even death, which may be preventable.

#### **2. Patients and Methods**

The present study was planned as a prospective study at the Esrefpasa Municipality Hospital after the ethical approval was obtained from the Metropolitan Municipality (Number 54022451-050.05-04 from 1 February 2017). STOP BANG questionnaire was applied to 965 heavy equipment operators and PSG was performed in Izmir Esrefpasa Municipality Hospital to the operators at high risk for OSAS. The machines used by the operators include dumpers, hydraulic backhoes, crawled dozers, graders, wheeled front-end loaders, wheeled vibrating rollers, hydraulic breakers, drills, bucked wheel excavators, and backhoe loaders. Signed informed consent forms were obtained from all participants.

For the determination of STOP-BANG scores, snoring, daytime sleepiness (tiredness), observed apnea, high blood pressure (antihypertensive drug use), body mass index (positive if BMI > 35), age (positive if > 50 years), neck circumference (positive if > 40 cm.), and gender (male gender positive) were recorded. If participants chose the answer 'yes' for 3 of 8 questions; they were accepted as having high risk for OSAS.

Between February 2017 and March 2019, all of the operators examined in our hospital were male. Demographic characteristics such as age, weight, height, body mass index, neck circumference, waist/hip ratio, alcohol and cigarette smoking, and medical history were recorded. At the same time, the municipal official records of the accidents were questioned and recorded.

All patients included in our study were monitored all night by a trained sleep technician using a PSG device at our sleep center. At least 6 h of PSG recordings were acquired. Sleep staging and respiratory- and movement-scoring were done according to the American Academy of Sleep Medicine manual (version 2.0) by a sleep technician [10]. Apnea hypopnea index (AHI) was defined as the number of apneas and hypopneas per hour of sleep. Apnea was defined as the drop of airflow ≥90% of baseline for at least 10 s and hypopnea as a decrease in airflow of at least 30% for at least 10 s with oxygen desaturation of more than 4% from baseline. The severity of OSA was determined by the AHI as mild if AHI is between 5 and 15, as moderate if AHI is between 15 and 30, and severe if AHI is greater than 30.

#### **3. Statistical Analyses**

IBM SPSS Statistics v. 22 (IBM Corp., Armonk, NY, USA) software was used for statistical analyses. Shapiro–Wilks test was used for evaluating the accordance of the parameters with normal distribution. In addition to descriptive statistical methods (mean, standard deviation, frequency), one-way ANOVA test was performed for the comparison of quantitative data, as well as Tukey HDS test and Tamhane's T2 test for the intergroup comparison of parameters with normal distribution and the determination of the group that causes the difference. Kruskal–Wallis test was used for carrying out intergroup comparisons of parameters without normal distribution. Whereas Chi Square test and Fisher–Freeman–Halton tests were performed for comparing qualitative data. Regression analysis was performed to determine the factors affecting the accident risk. Level of significance was evaluated as *p* < 0.05.

#### **4. Results**

The study was carried out between February 2017 and March 2019. In total, 142 operators were identified with high risk for OSAS and PSG could be performed on 110 of these 142 operators. All operators were male and their ages ranged from 35 to 58 years. The mean age of the operators was 45.07 ± 5.54 years. According to the PSG results of the study, 41 (37.3%) of the operators had normal findings, 35 (31.8%) had mild, 20 (18.2%) had moderate, and 14 (12.7%) had severe OSAS. The distribution of the general characteristics of the operators who participated in the study is shown in Table 1.


**Table 1.** Distribution of general characteristics.

\*: Body Mass Index.

Of the cases, 87 (79.1%) did not have an accident, while 18 (16.4%) almost had an accident, and 5 (4.5%) had an accident. While 41 (37.3%) of the patients had normal PSG results, 35 (31.8%) had mild, 20 (18.2%) had moderate, and 14 (12.7%) had severe OSAS. The distribution of OSAS classification and accident status is summarized in Table 2. There was a statistically significant relationship between the accident rate and OSAS severity (*p*: 0.009).


**Table 2.** Distribution of obstructive sleep apnea syndrome (OSAS) classification and accident status.

The present results indicate that there was a statistically significant difference between accident histories with regard to apnea prevalence (*p*: 0.009). Apnea prevalence in those who had not been involved in any accident (37.2%) was observed to be lower at a statistically significant level in comparison to those who were almost involved in an accident due to sleepiness (64.8%) and those who were involved in an accident (61%) (*p*1: 0.019; *p*2: 0.047, respectively). No statistically significant difference with regard to apnea prevalence was observed between those who were almost involved in an accident due to sleepiness and those who had been involved in an accident (*p* > 0.05). A statistically significant difference was observed between accident histories with regard to snoring + apnea prevalence (*p*: 0.004). The snoring + apnea prevalence in those who had not been involved in any accident (35.8%) was observed to be lower at a statistically significant level in comparison to those who were almost involved in an accident (63.1%) and those who had been involved in an accident (62%) (*p*1: 0.011; *p*2: 0.031, respectively).

Table 3 presents the assessment of age, BMI, neck circumference, waist–hip ratio, and Epworth score among the OSAS classification groups.


**Table 3.** Evaluation of age, BMI, neck circumference, waist-hip ratio and STOP BANG, smoking and alcohol use states among OSAS classification groups.

\*: Obstructive sleep apnea syndrome, \*\*: Body mass index.

According to the results of the present study, there was a statistically significant difference between the OSAS groups determined by PSG, regarding the BMI values and neck circumference (*p*: 0.001). The patients without OSAS had statistically significantly lower BMI values than those with moderate and severe OSAS (*p*1: 0.032; *p*2: 0.000, respectively). The BMI values of those with severe OSAS were determined to be higher at a statistically significant level in comparison with the BMI values of those with mild or moderate OSAS (*p*1: 0.004; *p*2: 0.017, respectively). The neck circumference values of those without OSAS were determined to be lower at a statistically significant level in comparison with that of the patients with moderate or severe OSAS (*p*1: 0.019; *p*2: 0.001, respectively). Neck circumference values of those with severe OSAS were determined to be higher at a statistically significant level in comparison with the mild OSAS group (*p*: 0.001). The waist–hip ratio of individuals with mild OSAS was determined to be lower at a statistically significant level in comparison with the waist–hip ratio of

individuals with moderate or severe OSAS (*p*1: 0.046; *p*2: 0.004, respectively). Epworth score of the severe OSAS group was significantly higher than that of the healthy cases (*p*: 0.001).

There was a statistically significant difference between the OSAS groups with regard to accident status (*p*: 0.001;). The ratio of being almost involved in an accident due to sleepiness (0%) for drivers without OSAS was determined to be lower at a statistically significant level in comparison to drivers with mild (15.4%), moderate (28.6%), or severe (39.6%) OSAS (*p*1: 0.026; *p*2: 0.001; *p*3: 0.001, respectively). The ratio of being involved in an accident for drivers with severe OSAS (34%) was determined to be higher at a statistically significant level in comparison to those of drivers with mild (0%) or moderate (5.7%) OSAS (p:0.001). No statistically significant difference could be determined between the drivers with mild and moderate OSAS with regard to the ratios of being involved in an accident (*p* > 0.05).

In Table 4, the results of regression analysis performed to determine the factors increasing accident risk are summarized. Regarding these findings, only AHI had significant effects on accident risk.


**Table 4.** Regression analysis performed to determine the factors increasing accident risk.

#### **5. Discussion**

Daytime sleepiness and loss of concentration is a common cause of accidents [11]. Short-term and poor quality sleep causes excessive daytime sleepiness, which increases the risk of accidents. Sleep disturbances such as obstructive sleep apnea syndrome are common causes of excessive sleepiness. In this study, in heavy equipment operators we determined a significant increase in accident risk with an increase in OSAS severity and in a regression analysis, there was a strong relationship between the AHI score and accident risk.

The studies on this subject are generally performed on long-distance drivers or taxi drivers, and the number of studies with the heavy equipment operators is limited. The majority of these studies are survey-oriented and the results obtained from the surveys are based on subjective data. Various surveys conducted with truck drivers showed a positive correlation between daytime excessive sleepiness and accidents [12–14]. In our study, we found that daytime sleepiness in heavy equipment operators was not a significant risk factor for accidents. However, this result may be related to the fact that the work was planned specifically for the heavy equipment operators and the maximum speed of the machines they were driving was limited at a speed of 20 km/h. However, in the case of heavy equipment operators, a distraction may cause serious accidents that can result in loss of life and property [15].

According to the results of STOP BANG survey, operators who were found to be at high risk for OSAS were evaluated with the results of PSG, performed in our sleep laboratory. Our study is one of the studies with a large sample group, which was performed with the participation of professional operators and evaluated the relationship between the accidents and PSG results. On the one hand, working with objective data will give more accurate results, on the other hand, the difficulty of doing PSG for each participant may be the obvious reason for not giving up surveys. Subjective symptoms such as snoring and apnea, which are questioned in terms of OSAS, are the symptoms they cannot detect on their own. On the other hand, financial anxiety may result in bias and the surveys may not reflect the realities. Results with similar concerns may be deceptive during job applications or health checks. Therefore, OSAS symptom questioning alone may be inadequate, and delays in diagnosis and treatment of OSAS cases may cause severe issues for the patients as well as the society due to the

accidents [16,17]. With this fact, in some parts of the European Union countries, driver's licenses are not given to OSAS patients because they do not have healthy driver qualifications [18,19].

The STOP BANG questionnaire was used to determine daytime sleepiness. In our study, the STOP BANG questionnaire score was below three in the majority of patients, including those describing daytime sleepiness (EDS). Regarding this data, the STOP BANG questionnaire completed by the operators may be suggested as inadequate and unsuitable for determining the EDS. We also did not determine an association between STOP BANG score and accident risk in regression analysis. On the other hand, the work and material concerns of our patients may make the questionnaire-based survey results illusory.

When compared to normal healthy individuals, accidents with OSAS operators were reported to be seen seven times higher [20,21]. As the apnea hypopnea index (AHI) increased in the study of Young et al., the risk of accident was found to be increased, in accordance with our study [22]. In another study by Teran-Santos et al., a significant relationship was found between the risk of accident and presence of OSAS. According to the results of the same study, when the operators with OSAS were evaluated with the increase in severity of the disease, the risk of accidents also increased, in parallel with our study. Accident rates in our study were determined as 28.5% in severe OSAS, 5.0% in moderate OSAS, and 0% in mild OSAS patients. Statistically, these rates show a positive correlation between the severity of OSAS and accident frequency. On the other hand, according to the results of STOP BANG survey, PSG was performed on a high-risk group and some of them did not have OSAS. No accident history was found in healthy group. Regarding the demographic data in our study; body mass index (BMI), neck circumference, and waist–hip ratio did not show any association with the accident risk. However, in the study of Amra et al., increased neck circumference was determined as a factor increasing the risk of accident [13].

In the light of all these data, it seems that the questioning of OSAS symptoms alone will not be enough to predict the risk of accidents. However, there are many studies demonstrating the relationship between the severity of OSAS and accident risk. Regarding the increased morbidity and mortality due to the accidents, it is a necessity to inquire about the OSAS symptoms in the professional drivers, such as heavy equipment operators, during the certification phase and to perform polysomnography on the drivers with high risk. It is concluded that in order to prevent the morbidity and mortalities, in the groups involved in the field of occupational risk, routine PSG studies may be required. For this purpose, it is necessary to question the number of sleep centers and to determine the OSAS symptoms periodically during the certification and after, and to increase the screening.

There are some limitations of this study that should be mentioned. The STOP BANG questionnaire is based on the patient responses, and this may carry some bias that the vehicle operators may not respond correctly; since they may be afraid of losing their job. Second, this is the report of single center results and larger, prospective studies investigating the treatment responses in this group of patients are warranted.

#### **6. Conclusions**

We want to attract attention to the necessity of evaluating the OSAS symptoms of professional heavy equipment operators during the certification period and at various intervals afterwards and to carry out OSAS evaluations by PSG for those with a certain risk.

**Author Contributions:** Conceptualization, H.C.; Methodology, G.D.I.; Software, H.C.; Validation, G.D.I.; Formal Analysis, G.D.I.; Investigation, H.C.; Resources, H.C.; Data Curation, G.D.I.; Writing—Original Draft Preparation G.D.I.; Writing—Review & Editing, H.C.; Visualization, G.D.I.; Supervision, G.D.I.; Project Administration, H.C.

**Funding:** This research received no external funding.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

### *Article* **Particularities of Older Patients with Obstructive Sleep Apnea and Heart Failure with Mid-Range Ejection Fraction**

**Carmen Loredana Ardelean 1,\* , Sorin Pescariu 2, Daniel Florin Lighezan 2, Roxana Pleava 1,\*, Sorin Ursoniu <sup>3</sup> , Valentin Nadasan <sup>4</sup> and Stefan Mihaicuta <sup>5</sup>**


Received: 22 June 2019; Accepted: 5 August 2019; Published: 7 August 2019

**Abstract:** *Background and objectives*: Obstructive sleep apnea syndrome (OSAS) and heart failure (HF) are increasing in prevalence with a greater impact on the health system. The aim of this study was to assess the particularities of patients with OSAS and HF, focusing on the new class of HF with mid-range ejection fraction (HFmrEF, EF = 40%–49%), and comparing it with reduced EF (HFrEF, EF < 40%) and preserved EF (HFpEF, EF ≥ 50%). *Materials and Methods*: A total of 143 patients with OSAS and HF were evaluated in three sleep labs of "Victor Babes" Hospital and Cardiovascular Institute, Timisoara, Western Romania. We collected socio-demographic data, anthropometric sleep-related measurements, symptoms through sleep questionnaires and comorbidity-related data. We performed blood tests, cardio-respiratory polygraphy and echocardiographic measurements. Patients were divided into three groups depending on ejection fraction. *Results*: Patients with HFmrEF were older (*p* = 0.0358), with higher values of the highest systolic blood pressure (mmHg) (*p* = 0.0016), higher serum creatinine (*p* = 0.0013), a lower glomerular filtration rate (*p* = 0.0003), higher glycemic levels (*p* = 0.008) and a larger left atrial diameter (*p* = 0.0002). Regarding comorbidities, data were presented as percentage, HFrEF vs. HFmrEF vs. HFpEF. Higher prevalence of diabetes mellitus (52.9 vs. 72.7 vs. 40.2, *p* = 0.006), chronic kidney disease (17.6 vs. 57.6 vs. 21.5, *p* < 0.001), tricuspid insufficiency (76.5 vs. 84.8 vs.59.1, *p* = 0.018) and aortic insufficiency (35.3 vs.42.4 vs. 20.4, *p* = 0.038) were observed in patients with HFmrEF, whereas chronic obstructive pulmonary disease(COPD) (52.9 vs. 24.2 vs.18.3, *p* = 0.009), coronary artery disease(CAD) (82.4 vs. 6.7 vs. 49.5, *p* = 0.026), myocardial infarction (35.3 vs. 24.2 vs. 5.4, *p* < 0.001) and impaired parietal heart kinetics (70.6 vs. 68.8 vs. 15.2, *p* < 0.001) were more prevalent in patients with HFrEF. *Conclusions*: Patients with OSAS and HF with mid-range EF may represent a new group with increased risk of developing life-long chronic kidney disease, diabetes mellitus, tricuspid and aortic insufficiency. COPD, myocardial infarction, impaired parietal kinetics and CAD are most prevalent comorbidities in HFrEF patients but they are closer in prevalence to HFmrEF than HFpEF.

**Keywords:** obstructive apnea; heart failure; risk factors; elderly; comorbidities

#### **1. Introduction**

In recent years, obstructive sleep apnea syndrome (OSAS) has increased in prevalence, occurring in up to 10% of healthy subjects, due to the greater frequency of obesity and the aging of the population. Consequently, this has had an increasingly important impact on the health system [1]. The prevalence of OSAS in subjects with cardiovascular disease, reported in earlier studies, was between 50% and 80% [2–4], and in half of subjects with heart failure (HF), it is associated with increased mortality [5] and worse prognosis [6].

OSAS is globally known as a major factor for the occurrence of cardiometabolic comorbidities due to intermittent hypoxia which leads to oxidative stress, endothelial dysfunction, increase of sympathetic activity and systemic inflammation [7]. Furthermore, activation of the sympathetic nervous system leads to activation of the renin-angiotensin-aldosterone system, which increases hydro-saline retention and thus the level of blood pressure [8]. However, hydro-saline retention due to heart failure can also play an important role in the pathogenesis of OSAS [9]. These data suggest that the relationship between HF and OSAS is not fully understood.

Large studies have demonstrated that OSAS prevalence is higher in patients with coronary artery disease (CAD), HF, resistant arterial hypertension associated with risk of stroke, and uncontrolled arrhythmias [10].

Patients with OSAS present a variety of symptoms that correlate with anthropometric measurements, smoking habits, sedentarism and association of comorbidities [1]. In recent years, new perspectives regarding clinical presentations of OSAS with description of different phenotypes and clusters have emerged [11–13].

Different structural or functional cardiac abnormalities can lead to occurrence of typical symptoms and signs of HF as defined by the European Society of Cardiology (ESC) guidelines, increased morbidity and mortality and higher costs for the health system [14]. HF is more common in elderly patients, especially those over 60 years [15].

The measurement of the left ventricle ejection fraction (LVEF) is used to define HF. Accordingly, HF is classified as HF with preserved LVEF, ≥50% (HFpEF) and HF with reduced LVEF, <40% (HFrEF). Recently, the latest guidelines on the diagnosis and management of heart failure published by the European Society of Cardiology proposed a new class of HF patients with LVEF = 40%–49% called HF with mid-range EF (HFmrEF), in order to better differentiate HF patients from the point of view of etiology, developing mechanisms and response to treatment strategy [16,17].

#### **2. Materials and Methods**

#### *2.1. Study Subjects*

We enrolled consecutive patients evaluated for OSAS at the "Victor Babes" Timisoara Hospital between 2014 and 2018 and for HF at the Timisoara Institute for Cardiovascular Diseases. Inclusion criteria were patients with age over 40 years, with a diagnosis of heart failure and OSAS who performed cardio-respiratory polygraphy, echocardiography and blood test evaluation. Patients with incomplete evaluation and those with no OSAS or with predominantly central sleep apnea (CSA) were excluded. This study was approved by the Ethical Committee of the University of Medicine and Pharmacy "Victor Babes" Timisoara as a subject for a PhD thesis (number 14728/15 NOV 2013). The clinics where the patient's evaluations were performed have an established agreement with the university through which all the data obtained from the patients may be used for research purposes. Informed consent was signed by all the patients.

Patients were initially evaluated through a standard datasheet with the following parameters: Age (years), gender (male/female), weight (kg) and height (cm), followed by measurement of body mass index (BMI = weight in kg/squared height in m), neck and abdominal circumference (cm), presence and duration of hypertension, maximum and current value of blood pressure, medication, reported apneas, snoring, sleepiness, Epworth Sleepiness Scale, SAS score (sleep apnea syndrome score), morning headache, restless sleep, nocturia, nocturnal awakenings, chronic obstructive pulmonary disease (COPD), diabetes, dyslipidemia, CAD, HF, arrhythmias, stroke, nasal septum deviation, polyposis, hypertrophic uvula and smoking status (pack × years). Since it is not routine practice in our cardio-respiratory unit, we did not collect data about physical activity.

For the sleep study we followed the European standards for diagnosis of OSAS [18].

Cardio-respiratory polygraphy recording was performed with Stardust Respironics and Porti. Several parameters were measured: The number of apnea (individually, central, obstructive and mixed) and hypopnea per hour of sleep and per night, the AHI (apnea-hypopnea index), the desaturation index, the mean saturation, the lowest saturation, and the longest desaturation period below 88% (seconds). Because we did not perform full night assisted polysomnography, data about sleep duration and duration of the lowest desaturation were not recorded. Approximately one-third of the patients enrolled in this study used CPAP (continuous positive airway pressure) due to the non-reimbursement of the cost of this therapy. Therefore, data related to the use of CPAP have not been included in this study.

The cardio-respiratory polygraphy recording was performed and scored manually as stated by American Academy of Sleep Medicine standards and European Sleep Research Society recommendations [19].

Laboratory tests were performed in Romanian Accreditation Association-RENAR certified medical laboratories, as follows: ESR (erythrocyte sedimentation rate) (mm/h), glucose (mg/dL), uric acid (mg/dL), creatinine (mg/dL), erythrocyte count (×106/μL), hemoglobin (g/dL), sodium (mmol/L), potassium (mmol/L) and lipid profile (total cholesterol, LDL (low-density lipoprotein)-cholesterol, HDL (high-density lipoprotein)-cholesterol, triglycerides, mg/dl). Glomerular filtration rate, (GFR, mL/min/1.73 m2) was calculated for each patient, using CKD-EPI (Chronic Kidney Disease Epidemiology Collaboration) formula [20]. Blood samples were collected early in the morning after fasting, and within 1–2 days of informed consent if signing took place at a different time of day.

Cardiological evaluation was performed, for all patients, at the Institute of Cardiovascular Diseases in Timisoara, using the same diagnostic algorithm and equipment. We used the modified Simpson's rule for echocardiographic measurement of EF [21], and HF was classified depending on the LVEF, HF with preserved ejection fraction, LVEF ≥ 50% (HFpEF); HF with reduced ejection fraction, LVEF < 40% (HFrEF); and HF with mid-range ejection fraction, LVEF = 40%–49% (HFmrEF). We also recorded end-diastolic volume (mL), end-systolic volume (mL), left atrium surface (cm2), left atrium diameter (cm), right ventricle diameter (cm), mitral E and A wave (m/s), E/A ratio, pulmonary artery pression (mm Hg) and percentage of patients with impaired parietal heart kinetics. Although, the assessment of the left ventricular internal dimension, left ventricular posterior wall, interventricular septum thickness is performed in current practice and provides valuable information about the HF etiology, in this study they were not recorded because we enrolled patients with heart failure, regardless of the underlying cardiac disease. We studied patients regarding LVEF only, as the main cardiac marker.

The morphological aspect, area (cm), degree of regurgitation and stenoses and transvalvular pressure gradients were determined for the mitral, aortic, tricuspid and pulmonary valves [22].

#### *2.2. Statistical Analysis*

Data are presented as proportions, medians and interquartile range (IQR) for variables with a skewed distribution. The differences in the characteristics of the subjects were evaluated after being divided into three groups, depending on the EF (EF < 40%, EF = 40%–49%, EF ≥ 50%). We used the chi-squared test (two degrees of freedom) for comparison of categorical data between groups of patients. Continuous data were tested for normality using the Kolmogorov–Smirnov test. Data with non-normal distributions were compared using the Kruskal–Wallis test. The *p* values for all hypothesis tests were two-sided, and the *p* value was set to the statistical significance threshold of <0.005. All data analyses were performed with Stata 15.1 (Statacorp, TX, USA).

#### **3. Results**

A total of 143 patients with OSAS and HF were evaluated in three sleep labs of Timisoara "Victor Babes" Hospital, Western Romania.

#### *3.1. Socio-Demographic and Anthropometric Data*

Patients were divided into three groups depending on EF, with the following characteristics, presented as median and interquartile range: 17 patients (11.88%) with HFrEF, of which 15 male (88%), age 61 (56–69) years, BMI 35 (31–36) kg/m2, neck circumference 44 (39–46) cm, abdominal circumference 120 (114–128) cm; 33 patients (23.07%) with HFmrEF, of which 22 male (67%), age 64.5 (57.5–71) years, BMI 36 (31.5–41.5) kg/m2, neck circumference 45 (42–46) cm, abdominal circumference 120 (114–130) cm; 93 patients (65.93%) with HFpEF, of which 62 male (67%), BMI 35 (31–41) kg/m2, neck circumference 44 (41–46) cm, abdominal circumference 122 (115–130) cm (Table 1).



Data are presented as proportions, medians and interquartile range (IQR). EF, ejection fraction; HFrEF, heart failure with reduced ejection fraction; HFmrEF, heart failure with mid-range EF; HFpEF, heart failure with preserved EF; BMI, body mass index.

Patients from the HFmrEF group were significantly older. More males were found in the HFrEF group. There were no differences in terms of BMI, neck and abdominal circumference (Table 1).

#### *3.2. Sleep Study and Blood Pressure Data*

There were no differences between groups of patients regarding blood pressure (BP) measurement and sleep study, systolic and diastolic BP at visit, AHI, type of apneas, desaturation index, medium and lowest desaturation, longest desaturation <88% and sleep questionnaire. Significant differences were observed in patients with HFmrEF regarding the highest systolic BP reported by the patients (*p* = 0.016) (Table 2).



Data are presented as medians and interquartile range (IQR). BP, blood pressure; AHI, apnea-hypopnea index; SpO2, oxygen saturation; SAS score, sleep apneas syndrome score.

#### *3.3. Blood Tests*

Routine blood tests revealed significant statistical difference in HFmrEF patients regarding level of glucose (*p* = 0.0081), creatinine (*p* = 0.0013) and GFR (*p* = 0.0003) (Table 3). There were no differences for ESR, uric acid, erythrocytes, hemoglobin, Na, K, total cholesterol, LDL and HDL cholesterol, or triglycerides.

**Table 3.** Blood tests.


Data are presented as medians and interquartile range (IQR). ESR, erythrocyte sedimentation rate; GFR, glomerular Filtration Rate; LDL, low-density lipoprotein; HDL, high-density lipoprotein.

#### *3.4. Echocardiographic Measurements*

Regarding echocardiographic measurements, statistically significant differences were found for end-diastolic and end-systolic volumes, ejection fraction, and left atrial diameter. LA (left atrium) diameter was higher in patients with HFmrEF (*p* = 0.0002), similar to other publications (Table 4)

**Table 4.** Echocardiographic measurements.


Data are presented as medians and interquartile range (IQR). LA, Left atrium; RV, right ventricle; PAP, pulmonary artery pressure.

#### *3.5. Comorbidities*

Regarding comorbidities, data were presented as proportions, HFrEF vs. HFmrEF vs. HFpEF. We observed that the group with HFmrEF has significantly more cases of diabetes mellitus (52.9 vs. 72.7 vs. 40.2 *p* = 0.006), chronic kidney disease (17.6 vs. 57.6 vs. 21.5, *p* < 0.001), valvular disease, tricuspid insufficiency (76.5 vs. 84.8 vs. 59.1, *p* = 0.018) and aortic insufficiency (35.3 vs. 42.4 vs. 20.4, *p* = 0.038). The group with HFrEF had more cases of COPD (52.9 vs. 24.2 vs. 18.3, *p* = 0.009), myocardial infarction (35.3 vs. 24.2. vs 5.4, *p* < 0.001), CAD (82.4 vs. 66.7 vs. 49.5, *p* = 0.026) and impaired heart parietal kinetics (70.6 vs. 68.8 vs. 15.2, *p* < 0.001). The presence of myocardial infarction,

CAD and impaired heart parietal kinetics were much lower in HFpEF patients compared with HFmrEF and HFrEF (Table 5).


**Table 5.** Comorbidities.

COPD, chronic obstructive pulmonary disease; CAD, coronary artery disease; CDK, chronic kidney disease; PAH, pulmonary arterial hypertension.

#### **4. Discussion**

In our population, 23.07 % of the patients had HFmrEF, higher than reports from recent studies where the percentage of the HFmrEF category is between 13% and 17% [23–26].

Men are more likely to have OSAS in patients with HF. Moreover, men have a higher incidence of HF in patients with OSAS [27]. In our study, patients with HFmrEF were older, with no significant differences regarding gender or neck and abdominal circumferences.

It is well known that obesity is an important risk factor for heart failure, and this association leads to multiple complications. In addition, obesity seems to be more prevalent in HF patients with preserved ejection fraction; this may occur due to poor echocardiographic images and error in LVEF measurement [28]. In our study, we included only patients with OSAS, and patients with HFmrEF were in stage 2 of obesity, with higher BMIs, but differences were not statistically significant. Central sleep apnea (CSA) is particularly noted in patients with HFrEF, and decompensated HF has been recognized as a risk factor for CSA [29].

Some studies have demonstrated that patients with heart failure and OSAS are less symptomatic, regardless of AHI, and Epworth Sleepiness Scale does not correlate with AHI [30]. Questionnaires do not accurately predict OSAS in patients with cardio-vascular disease (CVD) [31]. Epworth Sleepiness Scale and SAS score can be beneficial in predicting OSAS, but in our groups of patients, although the values are high, differences between groups are insignificant [32].

In our group, all the patients have severe OSAS, regardless of EF. Patients with severe, untreated OSAS have a higher risk of fatal cardiovascular events, some studies show [33].

Our patients with HFmrEF have higher blood glucose, serum creatinine and decreased glomerular filtration rate.

Nielson demonstrated in a large study that patients with elevated blood glucose levels but without confirmed diabetes have an increased risk of developing HF. Therefore, these patients should be carefully monitored in order to prevent the onset of HF [34].

Many studies demonstrated that even mild impaired renal function, with transitory elevated level of serum creatinine, represents an important predictor for worsening of heart failure. The pathophysiology remains unclear, but venous congestion and intrabdominal pressure serve as a challenge for the development of new therapeutic approaches [35,36]. OSAS severity was correlated with elevated serum creatinine [37], while CKD stage 3 is considered a significant predictor of CSA, as was demonstrated by Fleischmann et al. [38].

In this study, lipid profile is not different as in a cohort with all severities of disease where OSAS severity was independently correlated with cholesterol and triglycerides levels, probably because all our patients have severe OSAS [39].

Often, patients with HFpEF present only increased wall thickness of the LV or the size of LA, which makes it even more difficult to diagnose. In our study, LA diameter was higher in patients with HFmrEF (*p* = 0.0002), similar to other publications [40]. Moreover, the role of the left atrium in modulating LV function is well-known [41], and there are considerable amounts of data demonstrating that the size of the LA is directly proportional to the increased risk of cardiovascular events; this parameter is not used enough in clinical practice to determine the HF progression [42].

Wang et al. demonstrated in a recent meta-analysis that patients with moderate to severe tricuspid regurgitation (TR) have a higher risk of hospitalization for worsening HF and cardiac mortality. Patients with TR, regardless of severity, have a higher risk of all-cause mortality, compared with patients without tricuspid valvular disease [43]. Asymptomatic patients with HFpEF, but with severe aortic regurgitation (AR), have a higher risk of fatal cardiac events [44].

Comorbidities are very important in HF. Thus, comorbidity management plays a leading role in the treatment and progression of heart failure.

COPD is significantly more prevalent in HFrEF in our population. COPD and OSAS have common pathophysiological mechanisms, such as activation of sympathetic nervous system and inflammation, which can lead to increased cardiovascular risk. Furthermore, patients with association of these diseases, so called "overlap syndrome", are exposed to an even greater risk [7].

Some patients with advanced stages of COPD have right HF with peripheral edema and have increased likelihood of OSAS because of the shift of the rostral fluid from the legs during the night [45].

Chronic kidney disease is significantly more prevalent in the group of HFmrEF. Reports from ESADA (Sleep apnea network/European sleep apnea database) cohort study identify that in OSAS patients, decrease of GFR was predicted by baseline characteristics like older age, female gender, obese patients and severe nocturnal hypoxemia and by comorbidities like heart failure and arterial hypertension [46].

Several studies reported that HFmrEF patients have an increased risk of CAD as HFrEF patients, but all-cause mortality was similar to HFpEF [47,48]. The prognosis of HF, regardless of EF, was correlated with common risk factors, such as age, underlying disease and comorbidities [49].

Chioncel et al. found that the long-term mortality rate in HFmrEF was between those patients with HFpEF and HFrEF [50], whereas Pascual-Figa et al. showed that HFmrEF patients match a clinical profile similar to HFrEF, with an increased risk of cardiovascular mortality, rather than HFpEF [51]. Still, there are contradictory data from other recent studies which showed that HFmrEF patients have a prognosis similar to HFpEF patients [52,53]. The results of treatment in the latest publication show increased controversies [54].

#### **5. Study Limitations**

This study has several limitations. The studied population is relatively small, and even smaller for the subjects with HFrEF. There are no data about sleep since we did not perform full-night assisted polysomnography. The results need to be confirmed by larger studies.

#### **6. Conclusions**

Patients with OSAS and HF with mid-range EF may represent a new group of patients with increased risk of developing life-long chronic kidney disease, diabetes mellitus, and tricuspid and aortic insufficiency. COPD, myocardial infarction, impaired heart parietal kinetics and CAD are the most prevalent comorbidities in HFrEF patients, but the prevalence of these is closer to that of HFmrEF than HFpEF. More studies are needed, on larger groups of patients, to determine how OSAS is involved in the progression of HF, from borderline ejection fraction to more severe heart failure.

**Author Contributions:** Data curation, C.L.A., R.P., S.P., D.F.L., S.M.; formal analysis, S.U. and V.N.; methodology, C.L.A., S.P., D.F.L., S.M.; supervision, S.P., D.F.L., S.M.; writing—original draft, C.L.A., R.P., S.M.; writing—review and editing, C.L.A., S.P., S.M.

**Funding:** This research received no external funding.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
