The Influence of Comorbidities on Chemokine and Cytokine Profile in Obstructive Sleep Apnea Patients: Preliminary Results

Chaszczewska-Markowska, Monika; Górna, Katarzyna; Bogunia-Kubik, Katarzyna; Brzecka, Anna; Kosacka, Monika

doi:10.3390/jcm12030801

Open AccessArticle

The Influence of Comorbidities on Chemokine and Cytokine Profile in Obstructive Sleep Apnea Patients: Preliminary Results

by

Monika Chaszczewska-Markowska

¹,

Katarzyna Górna

^1,*

,

Katarzyna Bogunia-Kubik

¹,

Anna Brzecka

^2,†

and

Monika Kosacka

^2,†

¹

Laboratory of Clinical Immunogenetics and Pharmacogenetics, Hirszfeld Institute of Immunology and Experimental Therapy, Polish Academy of Sciences, 50-422 Wroclaw, Poland

²

Department of Pulmonology and Lung Oncology, Wroclaw Medical University, 53-439 Wroclaw, Poland

^*

Author to whom correspondence should be addressed.

^†

These authors contributed equally to this work.

J. Clin. Med. 2023, 12(3), 801; https://doi.org/10.3390/jcm12030801

Submission received: 4 December 2022 / Revised: 14 January 2023 / Accepted: 16 January 2023 / Published: 19 January 2023

(This article belongs to the Special Issue Obstructive Sleep Apnea with Cardiovascular, Metabolic and Mental Health Disorders)

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Abstract

:

Introduction: Obstructive sleep apnea (OSA) is frequently associated with a chronic inflammatory state and cardiovascular/metabolic complications. The aim of this study was to evaluate the influence of certain comorbidities on a panel of 45 chemokines and cytokines in OSA patients with special regard to their possible association with cardiovascular diseases. Material and Methods: This cross-sectional study was performed on 61 newly diagnosed OSA patients. For the measurement of the plasma concentration of chemokines and cytokines, the magnetic bead-based multiplex assay for the Luminex^® platform was used. Results: In the patients with concomitant COPD, there were increased levels of pro-inflammatory cytokines (CCL11, CD-40 ligand) and decreased anti-inflammatory cytokine (IL-10), while in diabetes, there were increased levels of pro-inflammatory cytokines (IL-6, TRIAL). Obesity was associated with increased levels of both pro-inflammatory (IL-13) and anti-inflammatory (IL-1RA) cytokines. Hypertension was associated with increased levels of both pro-inflammatory (CCL3) and anti-inflammatory (IL-10) cytokines. Increased daytime pCO₂, low mean nocturnal SaO₂, and the oxygen desaturation index were associated with increased levels of pro-inflammatory cytokines (CXCL1, PDGF-AB, TNF-α, and IL-15). Conclusions: In OSA patients with concomitant diabetes and COPD, elevated levels of certain pro-inflammatory and decreased levels of certain anti-inflammatory cytokines may favor the persistence of a chronic inflammatory state with further consequences. Nocturnal hypoxemia, frequent episodes of desaturation, and increased daytime pCO₂ are factors contributing to the chronic inflammatory state in OSA patients.

Keywords:

obstructive sleep apnea; inflammatory cytokines; chemokines; hypertension; diabetes; COPD

1. Introduction

Obstructive sleep apnea (OSA) is the most important sleep-breathing disorder of clinical significance. The occurrence of OSA ranges from 10% to 17% in men and from 3% to 9% in women—more frequently in older (≥50 years) persons [1]. Recurrent episodes of sleep apneas and hypopneas cause episodes of arterial oxygen desaturation and, in consequence, lead to oxidative stress, endothelial dysfunction, neurohormonal dysregulation, sleep fragmentation, and, finally, changes in the central nervous and cardiovascular systems [2,3,4,5,6,7].

Common comorbidities associated with OSA include obesity, hypertension, diabetes mellitus, and chronic obstructive pulmonary disease (COPD) [8,9]. OSA is also a risk factor of atrial fibrillation for the recurrence of atrial fibrillation after cardioversion and/or ablation [10] and for other arrhythmias [11]. OSA favors the occurrence of left heart failure [12,13] and, in some patients, pulmonary hypertension [14]. In severe cases, the risk of ischemic heart episodes [15] and stroke [16] increases. High prevalences of depression (35%) and anxiety (44%) in OSA patients have also been noted [17].

Obesity is typical for this disorder, being one of the causes of OSA, but—taking into account the bidirectional influences of excess weight and sleep disorders [18]—is also one of its consequences.

There is a high risk of developing arterial hypertension in OSA patients, especially in cases of prolonged cumulative time of hypoxemia [19], independently from obesity and age [20]. OSA promotes arterial hypertension through chemoreceptor stimulation and vegetative stimulation and through the activation of the renin-angiotensin-aldosterone system [21]. The prevalence of hypertension increases with the severity of OSA [8].

Diabetes mellitus is considered the most common comorbidity of OSA, occurring in up to approximately one-third of patients [8]. There is also a bidirectional association between diabetes and OSA [22]. Coexisting diabetes mellitus increases macro- and microvascular complications of both disorders and is a risk factor for cardiovascular mortality [23].

COPD is not a clear predisposing factor for OSA, but in cases with obesity, predominant bronchitis, and fluid retention, it may constitute an increased risk for OSA [24]. The coexistence of OSA and COPD is called overlap syndrome. Coexisting COPD leads to more severe arterial oxygen desaturations during sleep and strongly worsens health-related quality of life [25]. Patients with overlap syndrome are at higher risk of developing cardiovascular complications [26]. Overlap syndrome also increases mortality [25].

Thus obesity, hypertension, diabetes, and COPD—all associated with the occurrence of OSA—may constitute additional risk factors for cardiac or cerebral vascular events in the course of OSA. The incidence of cardiovascular OSA complications may be influenced by the chronic inflammatory state associated with this disorder [27].

The immune system profoundly contributes to cardiovascular diseases [28]. The function of the immune system strongly depends on cytokines that influence humoral and cellular reactions to infections and inflammation and helps in the interplay between immune cells and organs [29]. Cytokines are small proteins of pleiotrophic, multifunctional, and hormone-like properties; there are pro-inflammatory cytokines, anti-inflammatory cytokines, and chemotactic cytokines (chemokines) [30]. They are produced by various cells, such as lymphocytes (B and T), macrophages, platelets, fibroblasts, and endothelial cells [31]. Chemokines constitute a large superfamily of ligands and receptors, participating in immunological and inflammatory disorders [32], as well as in neurobiological processes [33]. Both cytokines and chemokines strongly influence cardiovascular diseases [34,35].

No specific biomarker or group of biomarkers has been found to be associated with OSA and cardiovascular diseases. It can be hypothesized that disequilibrium in pro-inflammatory and anti-inflammatory cytokines and chemokines may be considered one of the possible pathways linking OSA and its cardiovascular complications. The aim of our study was to assess a panel of 45 chemokines and cytokines in a group of newly diagnosed OSA patients with special regard to their possible association with concomitant diseases and the severity of sleep hypoxemia.

2. Materials and Methods

2.1. Patients and Controls

In this cross-sectional study, 61 patients (F/M = 10/51) with OSA syndrome were investigated. Inclusion criteria were as follows: Age > 40 years, diagnosis based on in-hospital polysomnography (PSG), and no previous OSA treatment. Exclusion criteria encompassed a lack of agreement for participation in the study and the absence of any unstable or acute disease.

The age of the patients was 58.61 ± 11.09 years. There were 13 patients (21%) with mild OSA, 9 patients with moderate OSA (15%), and 39% (64%) with severe OSA. The mean apnea–hypopnea index (AHI) was. 42.13 ± 25.40/h and the oxygen desaturation index (ODI) was 38.92 ± 27.02/h. In all patients, daytime arterialized capillary blood gas studies were performed, and partial pressure of oxygen (pO₂) and partial pressure of CO₂ (pCO₂) were analyzed.

There were 42 obese (body mass index, BMI > 30 kg/m²) patients (69%) and 19 non-obese patients (31%), including 16 overweight patients and 3 normal-weight patients. There were 7 patients (11%) with overlap syndrome, 16 patients (26%) with diabetes mellitus, and 43 patients (70%) with arterial hypertension. The comparison of clinical data in the groups of patients with and without comorbidities is shown in Table 1. Hypertensive patients were older than normotensive patients, diabetic patients were more obese than non-diabetic patients, overlap patients had lower mean nocturnal SaO₂ and lower daytime pO₂ than the patients without COPD, and obese patients had higher AHI and lower daytime pO₂ than non-obese patients.

All participants in the study provided written informed consent. The study was approved by the local Ethics Committee (No 1082/2021), and all the procedures were in accordance with the ethical standards of the Helsinki Declaration, as revised in 2013.

2.2. Polysomnography

All the patients underwent in-hospital polysomnography using the Alice 6 LDe Polysomnographic Sleep System (Philips Respironics, Monroeville, PA, USA). During 8 h of nocturnal sleep, the following parameters were measured: Airflow with the use of an oronasal thermal sensor and a nasal pressure sensor, chest and abdomen movements, oxygen saturation using a finger clip sensor for respiration, and electroencephalography, electromyography, and electrooculography for sleep stages. The following parameters were analyzed: AHI, ODI, mean arterial oxygen saturation (SaO₂) during sleep, and minimal SaO₂ at the end of sleep apnea/hypopnea episodes. Apneas were defined as the complete cessation of airflow for >10 s with concomitant respiratory movements of the chest and diaphragm, and hypopneas were defined as 30–50% reduction in oronasal airflow for >10 s associated with desaturation >3% or with arousal. Manual scoring was carried out after automatic scoring, according to the American Association of Sleep Medicine criteria [36]. OSA syndrome diagnosis was based on AHI >5/h and the presence of symptoms such as excessive daytime somnolence and daytime fatigue with concomitant choking and recurrent awakenings during sleep.

2.3. Chemokine and Cytokine Serum Levels

Serum samples were collected using BD Vacutainer SSTTM II Advance tubes (Becton Dickinson, Franklin Lakes, NJ, USA) from all participants and stored at −20 °C. Samples were then thawed and screened for the simultaneous detection of 45 chemokines and cytokines with the use of a customized Human XL Cyt Disc Premixed Mag Luminex Perf Assay Kit (R&D Systems Inc., Minneapolis, MN, USA). Analyses were performed according to the manual provided by the manufacturer. Serum samples were not diluted for the experiment. For analysis purposes, the Luminex 200 instrument (Luminex Corp., Austin, TX, USA) was used.

The concentrations of the following proteins were measured: B7-H1 (PD-L1), CCL11 (Eotaxin), CCL19 (MIP-3-ß), CCL2 (MCP-1), CCL20 (MIP-3-α), CCL3 (MIP-1-α), CCL4 (MIP-1-ß), CCL5 (RANTES), CD40Ligand (TNFSF5), CX3CL1 (Fractalkine), CXCL1 (GRO-α), CXCL10 (IP-10), CXCL2 (GRO-ß), EGF, FGF-basic, Flt-3 Ligand, G-CSF, GM-CSF, Granzyme B, IFN-α, IFN-ß, IFN-γ, IL-1-α (IL-1F), IL-1-ß (IL-1F2), IL-1ra (IL-1F3), IL-2, IL-3, IL-4, IL-5, IL-6, IL-7, IL-8 (CXCL8), IL-10, IL-12p70, IL-13, IL-15, IL-17A, IL-17E/IL-25, IL-33, PDGF-AA, PDGF-AB (PDGF-BB), TGF-α, TNF-α, TRAIL, and VEGF.

A list of the cytokines and chemokines studied with official names and gene locus is presented in Table 2. A list of the cytokines/chemokines with their function related to inflammation and cardiovascular diseases [37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81] is presented in Table 3. There were no valid experimental results in the case of the following 7 cytokines/chemokines: B7-H1/PD-L1, IFN-ß, IL-3, IL-12 p70, IL-17A, IL-17E/IL-25, and IL-33, which is why these cytokines/chemokines were not analyzed in further statistical analysis.

2.4. Statistical Analysis

Results of the serum cytokine and chemokine level assessment were related to clinical data. All statistical analyses were performed using STATISTICA 13 software (StatSoft. Inc., Tulsa, OK, USA). The U Mann–Whitney test for two independent samples was used, and for correlation analyses, the Spearman’s Rho correlation test was used. In all calculations, the statistical significance was considered at p < 0.05. The effect size was measured as either the standardized mean difference (β) or Spearman’s Rho correlation coefficient (rs).

3. Results

The comparison of the concentrations of the chemokines and cytokines in the groups of patients with and without comorbidities is shown in Table 4. In obese patients, there were increased concentrations of IL-1ra and IL-13 compared with non-obese patients. In overlap patients, there were increased concentrations of CCL11 and CD40 ligands but decreased concentrations of IL-10 compared with patients without concomitant COPD.

In hypertensive patients, there were increased concentrations of CCL3 and IL-10 compared with normotensive patients. In diabetic patients, there were increased concentrations of IL-6 and TRAIL compared with non-diabetic patients.

The comparison of OSA patients without any other diseases with OSA patients with only hypertension also demonstrated increased concentrations of CCL3 and, additionally, increased concentrations of CX3CL1/Fractalkine and IL-7 (Table 5).

Correlations between chemokine/cytokine levels and BMI, daytime pO₂ and pCO₂, and parameters indicating the severity of OSA (AHI, ODI, mean SaO₂, and minimal SaO₂) are shown in Table 6. There was a positive correlation between BMI and IL-1ra, and IFN-γ was negatively correlated with BMI. There was a positive correlation between daytime pO₂ and TRAI and an inverse correlation between pCO₂ and TRAIL. Daytime pO2 also positively correlated with the concentration of CCL11 and negatively correlated with the concentration of IL-1ra; daytime pCO₂ was positively correlated with the concentration of CXCL1 and PDGF-AB. There was a positive correlation between ODI and TNF-α, as well as between mean SaO₂ during sleep and IL-15, with no influences of AHI or minimal SaO₂ at the end of sleep apneas/hypopneas.

4. Discussion

There were differences in the concentrations of some chemokines/cytokines in patients with and without obesity, COPD, hypertension, and diabetes mellitus, as well as differences associated with daytime gas exchange and sleep hypoxemia.

Obesity had some influence on the cytokine/chemokine profile as shown by higher concentrations of IL-1ra and IL-13 in obese compared to non-obese OSA patients. In addition, there was a positive correlation between IL-1RA and BMI. IL-1RA has anti-inflammatory properties [82]. In the previous studies, obese, otherwise healthy persons also had elevated IL-1RA levels [72]. In patients with rheumatoid disease, IL-1ra concentrations positively correlated with BMI [67]. In the OSA patients, IL-1ra levels were also increased, and weight loss resulted in a decrease in its expression [82]. Increased levels of IL-1RA in obese OSA patients may be regarded as a protective factor. This may also confirm the negative correlation between IFN-γ and BMI. IFN-γ belongs to pro-inflammatory cytokines [83]. In adult OSA patients, elevated levels of IFNγ were found in the group with concomitant coronary heart disease [84]. In children with OSA, IFN-γ negatively correlated with cardiac function [85].

Overlap syndrome was associated with increased levels of the CCL11 and CD40 ligands, as well as decreased IL-10 levels. In COPD patients, there is a broad dysregulation of chemokines, including—as seen in our study in overlap patients—increased levels of CCL11 [86]. CCL11 has pro-inflammatory properties [87].

Increased levels of the CD40 ligand were observed in COPD patients, negatively correlating with ventilatory impairment [88]. This cytokine belongs to pro-inflammatory cytokines [89].

Decreased concentrations of Il-10 in stable COPD patients were found [90]. This is in line with the pathogenesis of COPD, as IL-10 is an anti-inflammatory cytokine [91]. In obese COPD patients, the levels of IL-10 were not decreased, indicating more severe inflammation than in non-obese COPD patients [92]. However, in some studies, COPD patients had elevated Il-10 levels [93].

In our OSA patients with concomitant arterial hypertension, there were increased concentrations of CCL3 and IL-10. In addition, the comparison of OSA patients without any other disease with OSA patients with only hypertension also demonstrated increased levels of CXCL2/GRO-ß and IL-7. In other studies, in children with primary hypertension, the serum levels of CCL3 were not different than in normotensive children [94]. Circulating levels of CCL3 (as well as CXCL10 and CD40 ligands) were associated with heart failure [50]. CCL3 has pro-inflammatory properties [95]. The role of inflammation in hypertension is still incompletely explained. There are numerous associations between changes in blood pressure and inflammatory mediators, including—as seen in our study—IL-10 and IL-7, but also interferon-γ, GM-CSF, IL-4, IL-6, IL-7, IL-8, IL-10, IL-12, IL-13, IL-17A, IL-21, IL-23, MIP-1α, and MIP-1β [96]. CXCL-2 belongs to the cytokines involved in the pathogenesis of some cardiovascular diseases as acute myocardial infarction, atherosclerosis, obesity, diabetes, and ischemic stroke [40].

Decreased levels of IL-10 were observed in OSA patients [97]. Differences in IL-10 concentrations in the OSA patients in relation to the presence of hypertension were found, with lower IL-10 concentrations in hypertensive than in normotensive patients [98]. In hypertensive patients, IL-10 was decreased [99]. IL-10 belongs to anti-inflammatory cytokines [100].

The influence of diabetes on the cytokine profile in our OSA patients was shown by increased levels of IL-6 and TRAIL. The data on IL-6 concentrations in OSA patients are contradictory. IL-6 was found to be increased in OSA patients compared to non-OSA patients, either obese or non-obese [101]. In another study, no significant differences in IL-6 were found in OSA and non-OSA patients [102]. In patients with OSA and diabetes, increased IL-6 levels were observed [71]. There is an influence of obesity on IL-6 levels, as its levels are positively correlated with BMI [103]. An increased IL-6 level was an independent predictor of type 2 diabetes and played an important role in inflammation, insulin resistance, and beta-cell dysfunction [104]. IL-6 belongs to pro-inflammatory cytokines [97].

TRAIL induces an inflammatory response, which stimulates the expression of chemokines and cytokines, including IL-6 [105]. Diabetic patients had decreased TRAIL levels compared to healthy controls [106,107]. There is a negative correlation between TRAIL levels and cardiovascular risk [80]. Previous studies indicated the importance of TRAIL in the development and progression of diabetes [108]. It is implicated in the regulation of T cell activation and suppresses the inflammatory process in many autoimmune diseases [109]. This modulation of the immune system also protects against diabetes [108]. TRIAL has pro-inflammatory properties [110].

In our study, in OSA patients, there was a positive correlation between TRAIL and daytime pO₂, as well as an inverse correlation between TRAIL and daytime pCO₂. TRAIL is a factor involved in the development of pulmonary hypertension [111,112]—a condition that may be a consequence of alveolar hypoxia and chronic hypoxemia. In OSA patients, repetitive sleep apneas and hypopneas cause alveolar hypoxia, and in cases with chronic daytime hypoxemia, pulmonary hypertension develops [113].

There was a positive correlation between pO₂ and the levels of both IL-1ra and CCL11, indicating an increase in both anti-inflammatory and pro-inflammatory actions along with improved daytime gas exchange in OSA patients.

On the other hand, however, increasing daytime pCO₂ was positively correlated with the level of CXCL1 and PDGF-AB. As both CXCL1 [114] and PDGF-AB [115] have pro-inflammatory properties, this observation indicates that the tendency to hypoventilation, as shown by increasing pCO₂, is associated with increased inflammatory status in OSA patients.

There was a positive correlation between IL-15 and mean nocturnal SaO₂. IL-15 belongs to pro-inflammatory cytokines [116], which indicates the association between sleep hypoxemia as a factor contributing to a chronic inflammatory state. This also confirms the positive correlation between ODI and TNF-α. TNF-α belongs to pro-inflammatory cytokines [117], which play an important role in OSA. Its serum levels increase with OSA severity and correlate with the frequency of apnea and hypopnea [118].

Our study has certain limitations. First, we divided the group of OSA patients into subgroups and compared the subgroups of OSA patients with and without comorbidities, i.e., obesity, hypertension, diabetes mellitus, and COPD, but did not compare these comorbidities with control groups. Second, the compared groups were relatively small. Moreover, the subgroups were not “pure”, e.g., all diabetic OSA patients also had hypertension, and obesity was diagnosed in 69% of our patients, thus some obese patients had to be included in other subgroups.

The main strength of the study is that in our OSA patients with the most common comorbidities, a significantly higher number of cytokines/chemokines were concomitantly measured. To the best of our knowledge, such an extensive study on cytokines and chemokines in OSA patients has not been performed.

In summary, the chemokine/cytokine profile in OSA patients with concomitant diseases indicates the inflammatory status in overlap syndrome, as shown by increased levels of pro-inflammatory proteins (CCL11, CD-40 ligand) and decreased anti-inflammatory protein (IL-10), and in diabetes, as shown by increased levels of pro-inflammatory cytokines (IL-6, TRIAL). There is an increase in the levels of both pro-inflammatory and anti-inflammatory cytokines in OSA patients with obesity (IF-γ and IL-1RA, respectively) or hypertension (CCL3 and IL-10, respectively). Increasing daytime pCO_2, low mean nocturnal SaO₂, and ODI are associated with increased levels of pro-inflammatory cytokines (CXCL1, PDGF-AB, IL-15, and TNF-α, respectively).

5. Conclusions

In OSA patients with concomitant diabetes and COPD, elevated levels of certain pro-inflammatory and decreased levels of certain anti-inflammatory cytokines may favor the persistence of a chronic inflammatory state with further consequences. Nocturnal hypoxemia, frequent episodes of desaturation, and increased daytime pCO₂ are the factors contributing to the chronic inflammatory state in OSA patients.

Author Contributions

Conceptualization, M.C.-M. and M.K.; methodology, M.C.-M. and K.G.; software, M.C.-M. and K.B.-K.; validation, M.C.-M., K.G. and M.K.; formal analysis, M.C.-M., M.K. and A.B.; investigation, K.G. and M.K.; resources, M.C.-M., K.B.-K., A.B. and M.K.; data curation, A.B., M.C.-M. and M.K.; writing—original draft preparation, A.B. and M.K.; writing—review and editing, M.C.-M., K.B.-K., M.K. and A.B.; visualization, K.G.; supervision, M.K.; project administration, M.C.-M. and M.K.; funding acquisition, K.B.-K., A.B. and M.K. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Wroclaw Medical University Project no 935 and SUBZ.C110.22.064.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, and approved by the Ethics Committee of Wroclaw Medical University (protocol code No 1082/2021 and date of approval 03.01.2022.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.

Conflicts of Interest

The authors declare no conflict of interest.

References

Peppard, P.E.; Young, T.; Barnet, J.H.; Palta, M.; Hagen, E.W.; Hla, K.M. Increased Prevalence of Sleep-Disordered Breathing in Adults. Am. J. Epidemiol. 2013, 177, 1006–1014. [Google Scholar] [CrossRef] [Green Version]
Ryan, C.; Bradley, T.D. Pathogenesis of obstructive sleep apnea. J. Appl. Physiol. 2005, 99, 2440–2450. [Google Scholar] [CrossRef] [PubMed]
Dempsey, J.A.; Veasey, S.C.; Morgan, B.J.; O’Donnell, C.P. Pathophysiology of Sleep Apnea. Physiol. Rev. 2010, 90, 47–112. [Google Scholar] [CrossRef] [PubMed]
Kheirandish-Gozal, L.; Khalyfa, A.; Gozal, D.; Bhattacharjee, R.; Wang, Y. Endothelial Dysfunction in Children with Obstructive Sleep Apnea Is Associated with Epigenetic Changes in the eNOS Gene. Chest 2013, 143, 971–977. [Google Scholar] [CrossRef] [PubMed] [Green Version]
Dewan, N.A.; Nieto, F.J.; Somers, V.K. Intermittent Hypoxemia and OSA: Implications for comorbidities. Chest 2015, 147, 266–274. [Google Scholar] [CrossRef] [Green Version]
Orrù, G.; Storari, M.; Scano, A.; Piras, V.; Taibi, R.; Viscuso, D. Obstructive Sleep Apnea, oxidative stress, inflammation and endothelial dysfunction-An overview of predictive laboratory biomarkers. Eur. Rev. Med. Pharmacol. Sci. 2020, 24, 6939–6948. [Google Scholar]
Abbasi, A.; Gupta, S.S.; Sabharwal, N.; Meghrajani, V.; Sharma, S.; Kamholz, S.; Kupfer, Y. A comprehensive review of obstructive sleep apnea. Sleep Sci. 2021, 14, 142–154. [Google Scholar] [PubMed]
Al-Jahdali, H.; Ahmed, A.E.; Abdullah, A.-H.; Ayaz, K.; Ahmed, A.; Majed, A.; Sami, A.; Amirah, A.; Bassam, D. Comorbidities in Clinical and Polysomnographic Features of Obstructive Sleep Apnea: A Single Tertiary Care Center Experience. J. Epidemiol. Glob. Health 2022, 12, 486–495. [Google Scholar] [CrossRef]
del Campo, F.; Arroyo, C.A.; Zamarrón, C.; Álvarez, D. Diagnosis of Obstructive Sleep Apnea in Patients with Associated Comorbidity. Adv. Exp. Med. Biol. 2022, 1384, 43–61. [Google Scholar] [CrossRef]
Zhang, D.; Ma, Y.; Xu, J.; Yi, F. Association between obstructive sleep apnea (OSA) and atrial fibrillation (AF): A dose-response meta-analysis. Medicine 2022, 101, e29443. [Google Scholar] [CrossRef]
Vizzardi, E.; Sciatti, E.; Bonadei, I.; D’Aloia, A.; Curnis, A.; Metra, M. Obstructive sleep apnoea–hypopnoea and arrhythmias: New updates. J. Cardiovasc. Med. 2017, 18, 490–500. [Google Scholar] [CrossRef] [PubMed]
Yeghiazarians, Y.; Jneid, H.; Tietjens, J.R.; Redline, S.; Brown, D.L.; El-Sherif, N.; Mehra, R.; Bozkurt, B.; Ndumele, C.E.; Somers, V.K. Obstructive Sleep Apnea and Cardiovascular Disease: A Scientific Statement From the American Heart Association. Circulation 2021, 144, e56–e67. [Google Scholar] [CrossRef]
Javaheri, S.; Javaheri, S. Obstructive Sleep Apnea in Heart Failure: Current Knowledge and Future Directions. J. Clin. Med. 2022, 11, 3458. [Google Scholar] [CrossRef] [PubMed]
Lu, M.; Wang, Z.; Zhan, X.; Wei, Y. Obstructive sleep apnea increases the risk of cardiovascular damage: A systematic review and meta-analysis of imaging studies. Syst. Rev. 2021, 10, 212. [Google Scholar] [CrossRef] [PubMed]
Rana, D.; Torrilus, C.; Ahmad, W.; A Okam, N.; Fatima, T.; Jahan, N. Obstructive Sleep Apnea and Cardiovascular Morbidities: A Review Article. Cureus 2020, 12, e10424. [Google Scholar] [CrossRef] [PubMed]
Salari, N.; Khazaie, H.; Abolfathi, M.; Ghasemi, H.; Shabani, S.; Rasoulpoor, S.; Mohammadi, M.; Khaledi-Paveh, B. The effect of obstructive sleep apnea on the increased risk of cardiovascular disease: A systematic review and meta-analysis. Neurol. Sci. 2021, 43, 219–231. [Google Scholar] [CrossRef]
Gharsalli, H.; Harizi, C.; Zaouche, R.; Sahnoun, I.; Saffar, F.; Maalej, S.; Douik El Gharbi, L. Prevalence of depression and anxiety in obstructive sleep apnea. Tunis Med. 2022, 100, 525–533. [Google Scholar]
Rodrigues, G.D.; Fiorelli, E.M.; Furlan, L.; Montano, N.; Tobaldini, E. Obesity and sleep disturbances: The “chicken or the egg” question. Eur. J. Intern. Med. 2021, 92, 11–16. [Google Scholar] [CrossRef]
Wang, L.; Wei, D.-H.; Zhang, J.; Cao, J. Time Under 90% Oxygen Saturation and Systemic Hypertension in Patients with Obstructive Sleep Apnea Syndrome. Nat. Sci. Sleep 2022, 14, 2123–2132. [Google Scholar] [CrossRef]
Tondo, P.; Fanfulla, F.; Scioscia, G.; Sabato, R.; Salvemini, M.; De Pace, C.C.; Barbaro, M.P.F.; Lacedonia, D. The Burden of Respiratory Alterations during Sleep on Comorbidities in Obstructive Sleep Apnoea (OSA). Brain Sci. 2022, 12, 1359. [Google Scholar] [CrossRef]
Baguet, J.-P.; Barone-Rochette, G.; Pépin, J.-L. Hypertension and obstructive sleep apnoea syndrome: Current perspectives. J. Hum. Hypertens. 2009, 23, 431–443. [Google Scholar] [CrossRef] [PubMed] [Green Version]
Huang, T.; Lin, B.M.; Stampfer, M.J.; Tworoger, S.S.; Hu, F.B.; Redline, S. A Population-Based Study of the Bidirectional Association Between Obstructive Sleep Apnea and Type 2 Diabetes in Three Prospective U.S. Cohorts. Diabetes Care 2018, 41, 2111–2119. [Google Scholar] [CrossRef] [PubMed]
Paschou, S.A.; Bletsa, E.; Saltiki, K.; Kazakou, P.; Kantreva, K.; Katsaounou, P.; Rovina, N.; Trakada, G.; Bakakos, P.; Vlachopoulos, C.V.; et al. Sleep Apnea and Cardiovascular Risk in Patients with Prediabetes and Type 2 Diabetes. Nutrients 2022, 14, 4989. [Google Scholar] [CrossRef] [PubMed]
Gleeson, M.; McNicholas, W.T. Bidirectional relationships of comorbidity with obstructive sleep apnoea. Eur. Respir. Rev. 2022, 31, 210256. [Google Scholar] [CrossRef]
Adler, D.; Dupuis-Lozeron, E.; Merlet-Violet, R.; Pépin, J.-L.; Espa-Cervena, K.; Muller, H.; Janssens, J.-P.; Brochard, L. Comorbidities and Subgroups of Patients Surviving Severe Acute Hypercapnic Respiratory Failure in the Intensive Care Unit. Am. J. Respir. Crit. Care Med. 2017, 196, 200–207. [Google Scholar] [CrossRef] [PubMed]
Shah, A.J.; Quek, E.; Alqahtani, J.S.; Hurst, J.R.; Mandal, S. Cardiovascular outcomes in patients with COPD-OSA overlap syndrome: A systematic review and meta-analysis. Sleep Med. Rev. 2022, 63, 101627. [Google Scholar] [CrossRef]
Unnikrishnan, D.C.; Jun, J.; Polotsky, V. Inflammation in sleep apnea: An update. Rev. Endocr. Metab. Disord. 2014, 16, 25–34. [Google Scholar] [CrossRef] [Green Version]
Razeghian-Jahromi, I.; Akhormeh, A.K.; Razmkhah, M.; Zibaeenezhad, M.J. Immune system and atherosclerosis: Hostile or friendly relationship. Int. J. Immunopathol. Pharmacol. 2022, 36, 3946320221092188. [Google Scholar] [CrossRef]
Dinarello, C.A. Historical insights into cytokines. Eur. J. Immunol. 2007, 37 (Suppl. S1), S34–S45. [Google Scholar] [CrossRef] [Green Version]
Bhagat, A.; Shrestha, P.; Kleinerman, E.S. The Innate Immune System in Cardiovascular Diseases and Its Role in Doxorubicin-Induced Cardiotoxicity. Int. J. Mol. Sci. 2022, 23, 14649. [Google Scholar] [CrossRef]
Ogawa, M. Differentiation and proliferation of hematopoietic stem cells. Blood 1993, 81, 2844–2853. [Google Scholar] [CrossRef] [PubMed] [Green Version]
Zlotnik, A.; Yoshie, O. The Chemokine Superfamily Revisited. Immunity 2012, 36, 705–716. [Google Scholar] [CrossRef] [PubMed]
Camacho-Arroyo, I.; Flores-Ramos, M.; Mancilla-Herrera, I.; Cruz, F.M.C.; Hernández-Ruiz, J.; Diaz, G.P.; Labonne, B.F.; Meza-Rodríguez, M.D.P.; Gelman, P.L. Chemokine profile in women with moderate to severe anxiety and depression during pregnancy. BMC Pregnancy Childbirth 2021, 21, 807. [Google Scholar] [CrossRef] [PubMed]
Feng, J.; Wu, Y. Interleukin-35 ameliorates cardiovascular disease by suppressing inflammatory responses and regulating immune homeostasis. Int. Immunopharmacol. 2022, 110, 108938. [Google Scholar] [CrossRef]
Duval, V.; Alayrac, P.; Silvestre, J.-S.; Levoye, A. Emerging Roles of the Atypical Chemokine Receptor 3 (ACKR3) in Cardiovascular Diseases. Front. Endocrinol. 2022, 13, 906586. [Google Scholar] [CrossRef]
Quan, S.F.; Gillin, J.C.; Littner, M.R.; Shepard, J.W. American Academy of Sleep Medicine: Sleep-related breathing disorders in adults: Recommendations for syndrome definition and measurement techniques in clinical research. The Report of an American Academy of Sleep Medicine Task Force. Sleep 1999, 22, 667–689. [Google Scholar]
Cubillos-Zapata, C.; Almendros, I.; Diaz-Garcia, E.; Toledano, V.; Casitas, R.; Galera, R.; Lopez-Collazo, E.; Farre, R.; Gozal, D.; Garcia-Rio, F. Differential effect of intermittent hypoxia and sleep fragmentation on PD-1/PD-L1 upregulation. Sleep 2020, 43, zsz285. [Google Scholar] [CrossRef]
Roesner, C.; Goeller, M.; Raaz-Schrauder, D.; Dey, D.; Kilian, T.; Achenbach, S.; Marwan, M.; Bittner, D.O. Differences of inflammatory cytokine profile in patients with vulnerable plaque: A coronary CTA study. Atherosclerosis 2022, 350, 25–32. [Google Scholar] [CrossRef]
Caidahl, K.; Hartford, M.; Ravn-Fischer, A.; Lorentzen, E.; Yndestad, A.; Karlsson, T.; Aukrust, P.; Ueland, T. Homeostatic Chemokines and Prognosis in Patients With Acute Coronary Syndromes. J. Am. Coll. Cardiol. 2019, 74, 774–782. [Google Scholar] [CrossRef]
Guo, L.-Y.; Yang, F.; Peng, L.-J.; Li, Y.-B.; Wang, A.-P. CXCL2, a new critical factor and therapeutic target for cardiovascular diseases. Clin. Exp. Hypertens. 2020, 42, 428–437. [Google Scholar] [CrossRef]
Schutyser, E.; Struyf, S.; Van Damme, J. The CC chemokine CCL20 and its receptor CCR6. Cytokine Growth Factor Rev. 2003, 14, 409–426. [Google Scholar] [CrossRef] [PubMed]
Jones, K.L.; Maguire, J.; Davenport, A.P. Chemokine receptor CCR5: From AIDS to atherosclerosis. Br. J. Pharmacol. 2011, 162, 1453–1469. [Google Scholar] [CrossRef] [PubMed] [Green Version]
Tatara, Y.; Ohishi, M.; Yamamoto, K.; Shiota, A.; Hayashi, N.; Iwamoto, Y.; Takeda, M.; Takagi, T.; Katsuya, T.; Ogihara, T.; et al. Macrophage inflammatory protein-1β induced cell adhesion with increased intracellular reactive oxygen species. J. Mol. Cell. Cardiol. 2009, 47, 104–111. [Google Scholar] [CrossRef] [PubMed]
Chang, T.-T.; Yang, H.-Y.; Chen, C.; Chen, J.-W. CCL4 Inhibition in Atherosclerosis: Effects on Plaque Stability, Endothelial Cell Adhesiveness, and Macrophages Activation. Int. J. Mol. Sci. 2020, 21, 6567. [Google Scholar] [CrossRef]
Mikolajczyk, T.P.; Szczepaniak, P.; Vidler, F.; Maffia, P.; Graham, G.J.; Guzik, T.J. Role of inflammatory chemokines in hypertension. Pharmacol. Ther. 2021, 223, 107799. [Google Scholar] [CrossRef]
Leonetti, S.; Tricò, D.; Nesti, L.; Baldi, S.; Kozakova, M.; Goncalves, I.; Nilsson, J.; Shore, A.; Khan, F.; Natali, A. Soluble CD40 receptor is a biomarker of the burden of carotid artery atherosclerosis in subjects at high cardiovascular risk. Atherosclerosis 2022, 343, 1–9. [Google Scholar] [CrossRef]
Ruchaya, P.; Paton, J.; Murphy, D.; Yao, S. A cardiovascular role for fractalkine and its cognate receptor, CX3CR1, in the rat nucleus of the solitary tract. Neuroscience 2012, 209, 119–127. [Google Scholar] [CrossRef]
Afsar, G.C.; Oruc, O.; Sarac, S.; Topçuoğlu, B.; Salturk, C.; Tepetam, F.M.; Bulut, I. Fractalkine in obstructive sleep apnea patients. Sleep Breath. 2017, 21, 355–359. [Google Scholar] [CrossRef]
Moreno, B.; Hueso, L.; Ortega, R.; Benito, E.; Martínez-Hervas, S.; Peiro, M.; Civera, M.; Sanz, M.-J.; Piqueras, L.; Real, J.T. Association of chemokines IP-10/CXCL10 and I-TAC/CXCL11 with insulin resistance and enhance leukocyte endothelial arrest in obesity. Microvasc. Res. 2022, 139, 104254. [Google Scholar] [CrossRef]
Altara, R.; Manca, M.; Hessel, M.H.; Gu, Y.; van Vark, L.C.; Akkerhuis, K.M.; Staessen, J.A.; Struijker-Boudier, H.A.J.; Booz, G.W.; Blankesteijn, W.M. CXCL10 Is a Circulating Inflammatory Marker in Patients with Advanced Heart Failure: A Pilot Study. J. Cardiovasc. Transl. Res. 2016, 9, 302–314. [Google Scholar] [CrossRef]
Yamamura, A.; Nayeem, J.; Sato, M. Roles of growth factors on vascular remodeling in pulmonary hypertension. Folia Pharmacol. Jpn. 2021, 156, 161–165. [Google Scholar] [CrossRef] [PubMed]
Tsapogas, P.; Mooney, C.J.; Brown, G.; Rolink, A. The Cytokine Flt3-Ligand in Normal and Malignant Hematopoiesis. Int. J. Mol. Sci. 2017, 18, 1115. [Google Scholar] [CrossRef] [Green Version]
Pourtaji, A.; Jahani, V.; Moallem, S.M.H.; Karimani, A.; Mohammadpour, A.H. Application of G-CSF in Congestive Heart Failure Treatment. Curr. Cardiol. Rev. 2019, 15, 83–90. [Google Scholar] [CrossRef] [PubMed]
Mindur, J.; Swirski, F. Growth Factors as Immunotherapeutic Targets in Cardiovascular Disease. Arter. Thromb. Vasc. Biol. 2019, 39, 1275–1287. [Google Scholar] [CrossRef] [PubMed]
Zeglinski, M.R.; Granville, D.J. Granzymes in cardiovascular injury and disease. Cell. Signal. 2020, 76, 109804. [Google Scholar] [CrossRef] [PubMed]
Farhangian, M.; Azarafrouz, F.; Chavoshinezhad, S.; Dargahi, L. Intranasal interferon-beta alleviates anxiety and depressive-like behaviors by modulating microglia polarization in an Alzheimer’s disease model. Neurosci. Lett. 2023, 792, 136968. [Google Scholar] [CrossRef]
Cavalli, G.; Colafrancesco, S.; Emmi, G.; Imazio, M.; Lopalco, G.; Maggio, M.C.; Sota, J.; Dinarello, C.A. Interleukin 1α: A comprehensive review on the role of IL-1α in the pathogenesis and treatment of autoimmune and inflammatory diseases. Autoimmun. Rev. 2021, 20, 102763. [Google Scholar] [CrossRef]
Yang, D.; Wang, L.; Jiang, P.; Kang, R.; Xie, Y. Correlation between hs-CRP, IL-6, IL-10, ET-1, and Chronic Obstructive Pulmonary Disease Combined with Pulmonary Hypertension. J. Healthc. Eng. 2022, 2022, 3247807. [Google Scholar] [CrossRef]
Fisman, E.Z.; Adler, Y.; Tenenbaum, A. Biomarkers in Cardiovascular Diabetology: Interleukins and Matrixins. Adv. Cardiol. 2008, 45, 44–64. [Google Scholar] [CrossRef]
Ye, J.; Wang, Y.; Wang, Z.; Liu, L.; Yang, Z.; Wang, M.; Xu, Y.; Ye, D.; Zhang, J.; Lin, Y.; et al. Roles and Mechanisms of Interleukin-12 Family Members in Cardiovascular Diseases: Opportunities and Challenges. Front. Pharmacol. 2020, 11, 129. [Google Scholar] [CrossRef] [Green Version]
Liu, S.; Wang, C.; Guo, J.; Yang, Y.; Huang, M.; Li, L.; Wang, Y.; Qin, Y.; Zhang, M. Serum Cytokines Predict the Severity of Coronary Artery Disease Without Acute Myocardial Infarction. Front. Cardiovasc. Med. 2022, 9, 896810. [Google Scholar] [CrossRef] [PubMed]
Nik, A.B.; Alvarez-Argote, S.; O’Meara, C.C. Interleukin 4/13 signaling in cardiac regeneration and repair. Am. J. Physiol. Circ. Physiol. 2022, 323, H833–H844. [Google Scholar] [CrossRef]
Guo, L.; Liu, M.F.; Huang, J.N.; Li, J.M.; Jiang, J.; Wang, J.A. Role of interleukin-15 in cardiovascular diseases. J. Cell. Mol. Med. 2020, 24, 7094–7101. [Google Scholar] [CrossRef] [PubMed]
Wang, Y.; Li, Y.; Wu, Y.; Jia, L.; Wang, J.; Xie, B.; Hui, M.; Du, J. 5TNF-α and IL-1β Neutralization Ameliorates Angiotensin II-Induced Cardiac Damage in Male Mice. Endocrinology 2014, 155, 2677–2687. [Google Scholar] [CrossRef]
Manoochehri, H.; Gheitasi, R.; Pourjafar, M.; Amini, R.; Yazdi, A. Investigating the relationship between the severity of coronary artery disease and inflammatory factors of MHR, PHR, NHR, and IL-25. Med J. Islam. Repub. Iran 2021, 35, 668–673. [Google Scholar] [CrossRef]
Melton, E.; Qiu, H. Interleukin-1β in Multifactorial Hypertension: Inflammation, Vascular Smooth Muscle Cell and Extracellular Matrix Remodeling, and Non-Coding RNA Regulation. Int. J. Mol. Sci. 2021, 22, 8639. [Google Scholar] [CrossRef] [PubMed]
Almeida-Santiago, C.; Quevedo-Abeledo, J.C.; Hernández-Hernández, V.; de Vera-González, A.; Gonzalez-Delgado, A.; González-Gay, M.; Ferraz-Amaro, I. Interleukin 1 receptor antagonist relation to cardiovascular disease risk in patients with rheumatoid arthritis. Sci. Rep. 2022, 12, 13698. [Google Scholar] [CrossRef]
Penna, C.; Femminò, S.; Tapparo, M.; Lopatina, T.; Fladmark, K.E.; Ravera, F.; Comità, S.; Alloatti, G.; Giusti, I.; Dolo, V.; et al. The Inflammatory Cytokine IL-3 Hampers Cardioprotection Mediated by Endothelial Cell-Derived Extracellular Vesicles Possibly via Their Protein Cargo. Cells 2020, 10, 13. [Google Scholar] [CrossRef]
Nasab, E.M.; Makoei, R.H.; Aghajani, H.; Athari, S.S. IL-33/ST2 pathway as upper-hand of inflammation in allergic asthma contributes as predictive biomarker in heart failure. ESC Heart Fail. 2022, 9, 3785–3790. [Google Scholar] [CrossRef]
Xu, J.Y.; Xiong, Y.Y.; Tang, R.J.; Jiang, W.Y.; Ning, Y.; Gong, Z.T.; Huang, P.S.; Chen, G.H.; Wu, C.X.; Hu, M.J.; et al. Interleukin-5-induced eosinophil population improves cardiac function after myocardial infarction. Cardiovasc. Res. 2022, 118, 2165–2178. [Google Scholar] [CrossRef]
Shalitin, S.; Deutsch, V.; Tauman, R. Hepcidin, soluble transferrin receptor and IL-6 levels in obese children and adolescents with and without type 2 diabetes mellitus/impaired glucose tolerance and their association with obstructive sleep apnea. J. Endocrinol. Investig. 2018, 41, 969–975. [Google Scholar] [CrossRef] [PubMed]
Elisia, I.; Lam, V.; Cho, B.; Hay, M.; Li, M.Y.; Kapeluto, J.; Elliott, T.; Harris, D.; Bu, L.; Jia, W.; et al. Exploratory examination of inflammation state, immune response and blood cell composition in a human obese cohort to identify potential markers predicting cancer risk. PLoS ONE 2020, 15, e0228633. [Google Scholar] [CrossRef] [PubMed] [Green Version]
Schmitz, T.; Harmel, E.; Heier, M.; Peters, A.; Linseisen, J.; Meisinger, C. Inflammatory plasma proteins predict short-term mortality in patients with an acute myocardial infarction. J. Transl. Med. 2022, 20, 457. [Google Scholar] [CrossRef] [PubMed]
Szelag, M.; Piaszyk-Borychowska, A.; Plens-Galaska, M.; Wesoly, J.; Bluyssen, H.A.R. Targeted inhibition of STATs and IRFs as a potential treatment strategy in cardiovascular disease. Oncotarget 2016, 7, 48788–48812. [Google Scholar] [CrossRef] [Green Version]
Benson, L.N.; Liu, Y.; Deck, K.S.; Mora, C.; Mu, S. IFN-γContributes to the Immune Mechanisms of Hypertension. Kidney360 2022, 3, 2164–2173. [Google Scholar] [CrossRef]
Venugopal, H.; Hanna, A.; Humeres, C.; Frangogiannis, N.G. Properties and Functions of Fibroblasts and Myofibroblasts in Myocardial Infarction. Cells 2022, 11, 1386. [Google Scholar] [CrossRef]
Narasimhalu, K.; Ma, L.; De Silva, D.A.; Wong, M.-C.; Chang, H.-M.; Chen, C. Elevated Platelet-Derived Growth Factor AB/BB is Associated with a Lower Risk of Recurrent Vascular Events in Stroke Patients. Int. J. Stroke 2015, 10, 85–89. [Google Scholar] [CrossRef]
Crosby, L.M.; Waters, C.M. Epithelial repair mechanisms in the lung. Am. J. Physiol. Cell. Mol. Physiol. 2010, 298, L715–L731. [Google Scholar] [CrossRef] [Green Version]
Wang, Y.; Zang, J.; Liu, C.; Yan, Z.; Shi, D. Interleukin-17 Links Inflammatory Cross-Talks Between Comorbid Psoriasis and Atherosclerosis. Front. Immunol. 2022, 13, 835671. [Google Scholar] [CrossRef]
Davenport, C.; Kenny, H.; Ashley, D.T.; O’Sullivan, E.P.; Smith, D.; O’Gorman, D.J. The effect of exercise on osteoprotegerin and TNF-related apoptosis-inducing ligand in obese patients. Eur. J. Clin. Investig. 2012, 42, 1173–1179. [Google Scholar] [CrossRef]
Melincovici, C.S.; Boşca, A.B.; Şuşman, S.; Mărginean, M.; Mihu, C.; Istrate, M.; Moldovan, I.M.; Roman, A.L.; Mihu, C.M. Vascular en-dothelial growth factor (VEGF)-key factor in normal and pathological angiogenesis. Rom. J. Morphol. Embryol. 2018, 59, 455–467. [Google Scholar] [PubMed]
Sahlman, J.; Seppä, J.; Herder, C.; Peltonen, M.; Peuhkurinen, K.; Gylling, H.; Vanninen, E.; Tukiainen, H.; Punnonen, K.; Partinen, M.; et al. Effect of weight loss on inflammation in patients with mild obstructive sleep apnea. Nutr. Metab. Cardiovasc. Dis. 2010, 22, 583–590. [Google Scholar] [CrossRef] [PubMed]
Dong, J.; Ping, L.; Cao, T.; Sun, L.; Liu, D.; Wang, S.; Huo, G.; Li, B. Immunomodulatory effects of the Bifidobacterium longum BL-10 on lipopolysaccharide-induced intestinal mucosal immune injury. Front. Immunol. 2022, 13, 947755. [Google Scholar] [CrossRef]
Wen, Y.; Zhang, H.; Tang, Y.; Yan, R. Research on the Association Between Obstructive Sleep Apnea Hypopnea Syndrome Complicated With Coronary Heart Disease and Inflammatory Factors, Glycolipid Metabolism, Obesity, and Insulin Resistance. Front. Endocrinol. 2022, 13, 854142. [Google Scholar] [CrossRef]
Hirsch, D.; Evans, C.A.; Wong, M.; Machaalani, R.; Waters, K.A. Biochemical markers of cardiac dysfunction in children with obstructive sleep apnoea (OSA). Sleep Breath. 2018, 23, 95–101. [Google Scholar] [CrossRef]
Acevedo, N.; Escamilla-Gil, J.M.; Espinoza, H.; Regino, R.; Ramírez, J.; de Arco, L.F.; Dennis, R.; Torres-Duque, C.A.; Caraballo, L. Chronic Obstructive Pulmonary Disease Patients Have Increased Levels of Plasma Inflammatory Mediators Reported Upregulated in Severe COVID-19. Front. Immunol. 2021, 12, 678661. [Google Scholar] [CrossRef] [PubMed]
Grievink, H.W.; Smit, V.; Huisman, B.W.; Gal, P.; Yavuz, Y.; Klerks, C.; Binder, C.J.; Bot, I.; Kuiper, J.; Foks, A.C.; et al. Cardiovascular risk factors: The effects of ageing and smoking on the immune system, an observational clinical study. Front. Immunol. 2022, 13, 968815. [Google Scholar] [CrossRef] [PubMed]
Shigeta, A.; Tada, Y.; Wang, J.-Y.; Ishizaki, S.; Tsuyusaki, J.; Yamauchi, K.; Kasahara, Y.; Iesato, K.; Tanabe, N.; Takiguchi, Y.; et al. CD40 amplifies Fas-mediated apoptosis: A mechanism contributing to emphysema. Am. J. Physiol. Cell. Mol. Physiol. 2012, 303, L141–L151. [Google Scholar] [CrossRef] [Green Version]
Shahabi, P.; Siest, G.; Herbeth, B.; Lambert, D.; Masson, C.; Hulot, J.-S.; Bertil, S.; Gaussem, P.; Visvikis-Siest, S. Influence of Genetic Variations on Levels of Inflammatory Markers of Healthy Subjects at Baseline and One Week after Clopidogrel Therapy; Results of a Preliminary Study. Int. J. Mol. Sci. 2013, 14, 16402–16413. [Google Scholar] [CrossRef] [Green Version]
Jiang, S.; Shan, F.; Zhang, Y.; Jiang, L.; Cheng, Z. Increased serum IL-17 and decreased serum IL-10 and IL-35 levels correlate with the progression of COPD. Int. J. Chronic Obstr. Pulm. Dis. 2018, 13, 2483–2494. [Google Scholar] [CrossRef] [Green Version]
Chaudhry, A.; Samstein, R.M.; Treuting, P.; Liang, Y.; Pils, M.C.; Heinrich, J.-M.; Jack, R.S.; Wunderlich, F.T.; Brüning, J.C.; Müller, W.; et al. Interleukin-10 Signaling in Regulatory T Cells Is Required for Suppression of Th17 Cell-Mediated Inflammation. Immunity 2011, 34, 566–578. [Google Scholar] [CrossRef] [PubMed] [Green Version]
Ovsyannikov, E.S.; Avdeev, S.N.; Budnevsky, A.V. Systemic inflammation in patients with chronic obstructive pulmonary disease and obesity. Ter. Arkhiv 2020, 92, 13–18. [Google Scholar] [CrossRef] [PubMed]
Kalathil, S.G.; Lugade, A.A.; Pradhan, V.; Miller, A.; Parameswaran, G.I.; Sethi, S.; Thanavala, Y. T-Regulatory Cells and Programmed Death 1⁺ T Cells Contribute to Effector T-Cell Dysfunction in Patients with Chronic Obstructive Pulmonary Disease. Am. J. Respir. Crit. Care Med. 2014, 190, 40–50. [Google Scholar] [CrossRef] [PubMed]
Litwin, M.; Michałkiewicz, J.; Niemirska, A.; Gackowska, L.; Kubiszewska, I.; Wierzbicka-Rucinska, A.; Wawer, Z.; Janas, R. Inflammatory activation in children with primary hypertension. Pediatr. Nephrol. 2010, 25, 1711–1718. [Google Scholar] [CrossRef] [PubMed]
Bai, C.; Ye, Q.; Zhao, Y.; Liu, Y.; Wang, J. MIP-1α Level and Its Correlation with the Risk of Left Atrial Remodeling in Patients with Atrial Fibrillation. Contrast Media Mol. Imaging 2022, 2022, 1756268. [Google Scholar] [CrossRef]
Crouch, S.H.; Roux, S.B.-L.; Delles, C.; Graham, L.A.; Schutte, A.E. Inflammation and hypertension development: A longitudinal analysis of the African-PREDICT study. Int. J. Cardiol. Hypertens. 2020, 7, 100067. [Google Scholar] [CrossRef]
Yi, M.; Zhao, W.; Fei, Q.; Tan, Y.; Liu, K.; Chen, Z.; Zhang, Y. Causal analysis between altered levels of interleukins and obstructive sleep apnea. Front. Immunol. 2022, 13, 888644. [Google Scholar] [CrossRef]
Qian, X.; Yin, T.; Li, T.; Kang, C.; Guo, R.; Sun, B.; Liu, C. High Levels of Inflammation and Insulin Resistance in Obstructive Sleep Apnea Patients with Hypertension. Inflammation 2012, 35, 1507–1511. [Google Scholar] [CrossRef]
Qiu, M.; Shu, H.; Li, L.; Shen, Y.; Tian, Y.; Ji, Y.; Sun, W.; Lu, Y.; Kong, X. Interleukin 10 Attenuates Angiotensin II-Induced Aortic Remodelling by Inhibiting Oxidative Stress-Induced Activation of the Vascular p38 and NF-κB Pathways. Oxidative Med. Cell. Longev. 2022, 2022, 8244497. [Google Scholar] [CrossRef]
Ruffolo, G.; Alfano, V.; Romagnolo, A.; Zimmer, T.; Mills, J.D.; Cifelli, P.; Gaeta, A.; Morano, A.; Anink, J.; Mühlebner, A.; et al. GABAA receptor function is enhanced by Interleukin-10 in human epileptogenic gangliogliomas and its effect is counteracted by Interleukin-1β. Sci. Rep. 2022, 12, 8244497. [Google Scholar] [CrossRef]
Vgontzas, A.N.; Zoumakis, E.; Bixler, E.O.; Lin, H.-M.; Collins, B.; Basta, M.; Pejovic, S.; Chrousos, G.P. Selective effects of CPAP on sleep apnoea-associated manifestations. Eur. J. Clin. Investig. 2008, 38, 585–595. [Google Scholar] [CrossRef] [PubMed] [Green Version]
Hargens, T.A.; Guill, S.G.; Kaleth, A.S.; Nickols-Richardson, S.M.; Miller, L.; Zedalis, D.; Gregg, J.M.; Gwazdauskas, F.; Herbert, W.G. Insulin resistance and adipose-derived hormones in young men with untreated obstructive sleep apnea. Sleep Breath. 2012, 17, 403–409. [Google Scholar] [CrossRef] [PubMed]
Pang, Y.; Kartsonaki, C.; Lv, J.; Fairhurst-Hunter, Z.; Millwood, I.Y.; Yu, C.; Guo, Y.; Chen, Y.; Bian, Z.; Yang, L.; et al. Associations of Adiposity, Circulating Protein Biomarkers, and Risk of Major Vascular Diseases. JAMA Cardiol. 2021, 6, 276–286. [Google Scholar] [CrossRef] [PubMed]
Akbari, M.; Hassan-Zadeh, V. IL-6 signalling pathways and the development of type 2 diabetes. Inflammopharmacology 2018, 26, 685–698. [Google Scholar] [CrossRef]
Zoller, V.; Funcke, J.-B.; Roos, J.; Dahlhaus, M.; El Hay, M.A.; Holzmann, K.; Marienfeld, R.; Kietzmann, T.; Debatin, K.-M.; Wabitsch, M.; et al. Trail (TNF-related apoptosis-inducing ligand) induces an inflammatory response in human adipocytes. Sci. Rep. 2017, 7, 5691. [Google Scholar] [CrossRef] [Green Version]
Choi, J.; Fujii, T.; Fujii, N. Fas and Tumor Necrosis Factor-Related Apoptosis-Inducing Ligand Are Closely Linked to the Levels of Glycated and Fetal Hemoglobin in Patients with Diabetes Mellitus. Clin. Lab. 2018, 64, 767–775. [Google Scholar] [CrossRef]
Chang, W.-W.; Liang, W.; Yao, X.-M.; Zhang, L.; Zhu, L.; Yan, C.; Jin, Y.-L.; Yao, Y.-S. Tumour necrosis factor-related apoptosis-inducing ligand expression in patients with diabetic nephropathy. J. Renin-Angiotensin-Aldosterone Syst. 2018, 19, 1470320318785744. [Google Scholar] [CrossRef] [Green Version]
Bossi, F.; Bernardi, S.; Zauli, G.; Secchiero, P.; Fabris, B. TRAIL Modulates the Immune System and Protects against the Development of Diabetes. J. Immunol. Res. 2015, 2015, 680749. [Google Scholar] [CrossRef] [Green Version]
Chyuan, I.-T.; Hsu, P.-N. TRAIL regulates T cell activation and suppresses inflammation in autoimmune diseases. Cell. Mol. Immunol. 2020, 17, 1281–1283. [Google Scholar] [CrossRef]
Collison, A.M.; Li, J.; de Siqueira, A.P.; Lv, X.; Toop, H.D.; Morris, J.C.; Starkey, M.R.; Hansbro, P.M.; Zhang, J.; Mattes, J. TRAIL signals through the ubiquitin ligase MID1 to promote pulmonary fibrosis. BMC Pulm. Med. 2019, 19, 31. [Google Scholar] [CrossRef] [Green Version]
Hameed, A.; Arnold, N.D.; Chamberlain, J.; Pickworth, J.A.; Paiva, C.; Dawson, S.; Cross, S.; Long, L.; Zhao, L.; Morrell, N.; et al. Inhibition of tumor necrosis factor–related apoptosis-inducing ligand (TRAIL) reverses experimental pulmonary hypertension. J. Exp. Med. 2012, 209, 1919–1935. [Google Scholar] [CrossRef] [PubMed]
Liu, H.; Yang, E.; Lu, X.; Zuo, C.; He, Y.; Jia, D.; Zhu, Q.; Yu, Y.; Lv, A. Serum levels of tumor necrosis factor-related apoptosis-inducing ligand correlate with the severity of pulmonary hypertension. Pulm. Pharmacol. Ther. 2015, 33, 39–46. [Google Scholar] [CrossRef] [PubMed]
Yan, L.; Zhao, Z.; Zhao, Q.; Jin, Q.; Zhang, Y.; Li, X.; Duan, A.; Luo, Q.; Liu, Z. The clinical characteristics of patients with pulmonary hypertension combined with obstructive sleep apnoea. BMC Pulm. Med. 2021, 21, 378. [Google Scholar] [CrossRef] [PubMed]
Regev, D.; Etzion, S.; Haddad, H.; Gopas, J.; Goldbart, A. Obstructive Sleep Apnea Syndrome In Vitro Model: Controlled Intermittent Hypoxia Stimulation of Human Stem Cells-Derived Cardiomyocytes. Int. J. Mol. Sci. 2022, 23, 10272. [Google Scholar] [CrossRef] [PubMed]
Farooq, H.; Wessel, R.P.; Brown, K.; Slaven, J.E.; Marini, F.; Malek, S.; Natoli, R.M. Utility of Plasma Protein Biomarkers and Mid-Infrared Spectroscopy for Diagnosing Fracture-Related Infections: A Pilot Study. J. Orthop. Trauma 2022, 36, e380–e387. [Google Scholar] [CrossRef]
Kalinina, O.; Golovkin, A.; Zaikova, E.; Aquino, A.; Bezrukikh, V.; Melnik, O.; Vasilieva, E.; Karonova, T.; Kudryavtsev, I.; Shlyakhto, E. Cytokine Storm Signature in Patients with Moderate and Severe COVID-19. Int. J. Mol. Sci. 2022, 23, 8879. [Google Scholar] [CrossRef]
Fatyga, P.; Pac, A.; Fedyk-Łukasik, M.; Grodzicki, T.; Skalska, A. The relationship between malnutrition risk and inflammatory biomarkers in outpatient geriatric population. Eur. Geriatr. Med. 2020, 11, 383–391. [Google Scholar] [CrossRef] [Green Version]
Ji, L.; Liu, Y.; Liu, P.; Ji, G.; He, J.; Gan, Y.; Zhu, S.; Chen, B.; Zhang, W. Serum periostin and TNF-α levels in patients with obstructive sleep apnea-hypopnea syndrome. Sleep Breath. 2020, 25, 331–337. [Google Scholar] [CrossRef]

Table 1. The comparison of clinical data in the groups of OSA patients with and without comorbidities.

Clinical Data	Obesity			COPD			Hypertension			Diabetes Mellitus
Clinical Data	YES N = 42	NO N = 19	p	YES N = 7	NO N = 54	p	YES N = 43	NO N = 18	p	YES N = 16	NO N = 45	p
Age [years]	57.68	60.50	0.280	58.57	58.61	0.303	60.74	53.50	0.037	62.13	57.36	0.087
BMI [kg/m²]	NA	NA	NA	38.99	32.98	0.100	34.27	32.25	0.317	36.23	32.77	0.050
AHI [n/h]	48.84	24.02	<0.001	53.40	40.67	0.130	42.25	41.84	0.770	38.51	43.42	0.682
Mean SaO₂ [%]	91.68	94.24	<0.001	88.80	93.00	0.004	92.15	93.38	0.097	91.76	92.79	0.075
Minimal SaO₂ [%]	73.00	83.75	0.002	71.29	77.20	0.140	76.47	76.67	0.849	77.13	76.31	0.704
pO₂ [mmHg]	69.20	71.83	0.250	59.60	71.53	0.008	69.30	72.10	0.562	71.06	69.59	0.999
pCO₂ [mmHg]	42.14	40.94	0.167	49.41	40.66	0.271	42.36	40.04	0.667	41.19	42.01	0.992

COPD—chronic obstructive pulmonary disease; BMI—body mass index; AHI—apnea hypopnea index; mean SaO₂—mean arterial oxygen saturation during sleep; minimal SaO₂—minimal arterial oxygen saturation at the end of sleep apneas and hypopneas; pO₂—daytime partial pressure of oxygen; pCO₂ daytime partial pressure of carbon dioxide; p—probability. Age, BMI, AHI, SaO₂, and pO₂ are presented as mean values across all OSA patients. Mann–Whitney U Test was used for calculations. Statistically significant results (p < 0.05) are shown in bold.

Table 2. The list of chemokines and cytokines studied.

Cytokine/Chemokine	Official Symbol	Official Name	Gene Locus
B7-H1/PD-L1	CD274	CD274 molecule	9p24.1
CCL11/eotaxin	CCL11	C-C motif chemokine ligand 11	17q12
CCL19/MIP-3-ß	CCL19	C-C motif chemokine ligand 19	9p13.3
CCL2/MCP-1	CCL2	C-C motif chemokine ligand 2	17q12
CCL20/MIP-3-α	CCL20	C-C motif chemokine ligand 20	2q36.3
CCL3/MIP-1-α	CCL3	C-C motif chemokine ligand 3	17q12
CCL4/MIP-1-ß	CCL4	C-C motif chemokine ligand 4	17q12
CCL5/RANTES	CCL5	C-C motif chemokine ligand 5	17q12
CD 40 Ligand/TNFSF5	CD40LG	CD40 ligand	Xq26.3
CX3CL1/fractalkine	CX3CL1	C-X3-C motif chemokine ligand 1	16q21
CXCL1/GRO α	CXCL1	C-X-C motif chemokine ligand 1	4q13.3
CXCL10/IP-10	CXCL10	C-X-C motif chemokine ligand 10	4q21.1
CXCL2/GRO ß	CXCL2	C-X-C motif chemokine ligand 2	4q13.3
EGF	EGF	epidermal growth factor	4q25
FGF-basic	FGF2	fibroblast growth factor 2	4q28.1
Flt-3 ligand	FLT3LG	fms related tyrosine kinase 3 ligand	19q13.33
G-CSF	CSF3	colony stimulating factor 3	17q21.1
GM-CSF	CSF2	colony stimulating factor 2	5q31.1
granzyme B	GZMB	granzyme B	14q12
IFN-ß	IFNB1	interferon beta 1	9p21.3
IL-1 α/IL-1F1	IL1A	interleukin 1 alpha	2q14.1
IL-10	IL10	interleukin 10	1q32.1
IL-12 p70	IL12A + IL12B	IL12 (p70) active heterodimer: IL-12A (p35) and IL-12B (p40)	3q25.33; 5q33.3
IL-13	IL13	interleukin 13	5q31.1
IL-15	IL15	interleukin 15	4q31.21
IL-17A	IL17A	interleukin 17A	6p12.2
IL-17E/IL-25	IL25	interleukin 25	14q11.2
IL-1-ß/IL-1F2	IL1B	interleukin 1 beta	2q14.1
IL-1ra/IL-1F3	IL1RN	interleukin 1 receptor antagonist	2q14.1
IL-2	IL2	interleukin 2	4q27
IL-3	IL3	interleukin 3	5q31.1
IL-33	IL33	interleukin 33	9p24.1
IL-4	IL4	interleukin 4	5q31.1
IL-5	IL5	interleukin 5	5q31.1
IL-6	IL6	interleukin 6	7p15.3
IL-7	IL7	interleukin 7	8q21.13
IL-8/CXCL8	CXCL8	C-X-C motif chemokine ligand 8	4q13.3
INF α	IFNA2	interferon alpha 2	9p21.3
INF γ	IFNG	interferon gamma	12q15
PDGF-AA	PDGFA	platelet derived growth factor subunit A	7p22.3
PDGF-AB/BB	PDGFB	platelet derived growth factor subunit B	22q13.1
TGF α	TGFA	transforming growth factor alpha	2p13.3
TNF-α	TNF	tumor necrosis factor	6p21.33
TRAIL	TNFSF10	TNF superfamily member 10	3q26
VEGF	VEGFA	vascular endothelial growth factor A	6p21.1

Table 3. The list of cytokines/chemokines with their function related to inflammation and cardiovascular diseases.

Cytokine/Chemokine	Function
B7-H1/PD-L1	Upregulated in the cells after intermittent hypoxia [37]
CCL11/eotaxin	Positively associated with vulnerable plaque burden [38]
CCL19/MIP-3-ß	Increases risk of heart failure in the patients with acute coronary syndrome [39]
CCL2/MCP-1	Involved in the pathogenesis of stroke and myocardial infarction [40]
CCL20/MIP-3-α	Biomarker of endothelial inflammation [41]
CCL3/MIP-1-α	Involved in the development of atherosclerosis [42]
CCL4/MIP-1-ß	Increased levels allow to predict cardiovascular and cerebrovascular complications of hypertension [43]; it’s inhibition may reduce endothelial inflammation [44]
CCL5/RANTES	Associated with immune cells activation in the patients with hypertension [45]
CD 40 Ligand/TNFSF5	Biomarker of carotid artery atherosclerosis [46]
CX3CL1/fractalkine	Microglial biomarker, induces bradycardic response and fall in blood pressure [47] Ruchaya 2012; mediator of chronic inflammation [48]
CXCL1/GRO α	Biomarker of carotid artery atherosclerosis [46]
CXCL10/IP-10	Associated with cardiovascular diseases, obesity [49] and heart failure [50]
CXCL2/GRO ß	Increased in cardiovascular diseases [40]
EGF	Involved in the development of pulmonary hypertension [51]
FGF-basic	Involved in the development of pulmonary hypertension [51]
Flt-3 ligand	Involved in the regulation of hematopoiesis [52]
G-CSF	Involved in cardiac repair after myocardial infarction and potential novel treatment in heart failure [53]
GM-CSF	May drive cardiovascular inflammation [54]
granzyme B	Increases in coronary artery disease [55]
IFN-ß	Anti-inflammatory cytokine [56]
IL-1 α/IL-1F1	Involved in the pathogenesis of cardiovascular diseases [57]
IL-10	Predictor of pulmonary hypertension [58]; protective effects in cardiovascular diseases in the course of diabetes [59]
IL-12 p70	Related to progression of cardiovascular diseases [60]; negative correlation with severity of coronary artery disease [61]
IL-13	Supports cardiac repair following myocardial infarction [62]
IL-15	May be protective in myocardial infarction [63]
IL-17A	Highly expressed in atherosclerotic plaques [64]
IL-17E/IL-25	Marker of severity of coronary artery disease [65]
IL-1-ß/IL-1F2	Contributes to regulation of arterial blood pressure [66]
IL-1ra/IL-1F3	Associated with increased cardiovascular risk; increases in obesity [67]
IL-2	Harmful in cardiovascular diseases in the course of diabetes [59]
IL-3	May impair cardioprotective mechanisms in the ischemia/reperfusion settings [68]
IL-33	Involved in pathophysiology of heart failure [69]
IL-4	Protective effects in cardiovascular diseases in the course of diabetes [59]; low levels in severe coronary artery disease [61]
IL-5	Facilitates heart repair after myocardial infarction [70]
IL-6	Increases in diabetes [71] and in obesity [72]
IL-7	Harmful effects in cardiovascular diseases in the course of diabetes [59]
IL-8/CXCL8	Inflammatory marker associated with mortality after myocardial infarction [73]
INF α	Pro-inflammatory cytokine [74]
INF γ	Contributes to hypertension [75]
PDGF-AA	Influence on cardiac fibroblasts function in myocardial infarction [76]
PDGF-AB/BB	Decreased levels associated with atherosclerotic plaque instability and higher risk of recurrent stroke [77]
TGF α	Involved in lung repair in COPD [78]
TNF-α	Increases in hypertension [79]
TRAIL	Negatively correlates with cardiovascular risk [80]
VEGF	Pro-angiogenetic, mitogenic and anti-apoptotic activity [81]

Table 4. Concentrations of chemokines/cytokines in the groups of OSA patients with and without comorbidities.

Chemokine/Cytokine [pg/mL]	Obesity			COPD			Hypertension			Diabetes Mellitus
Chemokine/Cytokine [pg/mL]	YES N = 42	NO N = 19	p	YES N = 7	NO N = 54	p	YES N = 43	NO N = 18	p	YES N = 16	NO N = 45	p
CCL11/Eotaxin	6.74	9.86	0.121 β = −0.37	12.66	7.12	0.044 β = 0.66	8.02	7.14	0.779 β = 0.10	5.90	8.42	0.624 β = −0.30
CCL19/MIP-3-ß	189.89	183.39	0.149 β = 0.04	141.65	193.73	0.453 β = −0.37	178.34	210.26	0.161 β = −0.22	168.14	194.73	0.389 β = −0.18
CCL2/MCP-1	757.41	791.98	0.834 β = −0.09	654.90	783.50	0.424 β = −0.34	716.89	892.63	0.105 β = −0.47	816.91	751.62	0.667 β = 0.17
CCL20/MIP-3-α	20.27	31.05	0.167 β = −0.26	8.21	25.83	0.149 β = −0.44	25.46	19.85	0.952 β = 0.14	27.10	22.63	0.896 β = 0.11
CCL3/MIP-1-α	28.95	45.08	0.215 β = −0.31	26.22	35.27	0.674 β = −0.17	40.63	18.96	0.008 β = 0.42	41.13	31.78	0.294 β = 0.18
CCL4/MIP-1-ß	0.29	0.28	0.904 β = 0.11	0.29	0.28	0.865 β = 0.12	0.29	0.27	0.920 β = 0.25	0.32	0.27	0.267 β = 0.62
CCL5/RANTES	241.99	169.75	0.204 β = 0.39	234.30	216.23	0.952 β = 0.09	221.14	211.54	0.968 β = 0.05	245.26	208.72	0.952 β = 0.20
CD40 Ligand/TNFSF5	6.69	7.11	0.327 β = −0.10	8.83	6.57	0.016 β = 0.54	7.12	6.13	0.379 β = 0.24	7.24	6.68	0.164 β = 0.13
CX3CL1/Fractalkine	0.04	0.04	0.190 β = 0	0.04	0.04	0.704 β = 0	0.04	0.05	0.787 β = −0.20	0.03	0.04	0.944 β = −0.20
CXCL1/GRO-α	0.05	0.03	0.542 β = 0.40	0.04	0.04	0.976 β = 0	0.04	0.04	0.968 β = 0	0.04	0.04	0.764 β = 0
CXCL10/IP-10	55.35	63.12	0.960 β = −0.18	54.31	58.36	0.849 β = −0.09	53.13	69.29	0.373 β = −0.38	67.95	54.32	0.234 β = 0.32
CXCL2/GRO-ß	265.03	209.31	0.802 β = 0.20	256.31	245.52	0.772 β = 0.04	259.95	215.25	0.696 β = 0.15	396.15	193.65	0.128 β = 0.72
EGF	240.86	270.86	0.280 β = −0.29	252.12	250.51	0.992 β = 0.01	250.90	250.21	0.674 β = 0.01	247.59	251.80	0.726 β = −0.03
FGF-basic	16.69	16.01	0.881 β = 0.02	10.27	17.27	0.704 β = −0.21	16.41	16.60	0.834 β = −0.01	12.35	17.93	0.749 β = −0.17
Flt-3 Ligand	0.11	0.10	0.363 β = 0.25	0.10	0.11	0.881 β = −0.25	0.11	0.11	0.667 β = 0	0.10	0.11	0.818 β = −0.25
G-CSF	7.32	7.06	0.960 β = 0.07	7.07	7.26	0.711 β = −0.05	7.27	7.15	0.682 β = 0.03	7.45	7.16	0.645 β = 0.08
GM-CSF	0.002	0.005	0.726 β = −0.75	0.002	0.003	0.756 β = −0.25	0.002	0.004	0.952 β = −0.50	0.002	0.003	0.667 β = −0.25
Granzyme B	2.86	9.35	0.234 β = −0.49	3.02	5.24	0.726 β = −0.17	5.69	3.31	0.779 β = 0.18	1.28	6.30	0.810 β = −0.38
IFN-α	1.03	1.01	0.379 β = 0.02	0.90	1.04	0.711 β = −0.14	0.93	1.24	0.555 β = −0.31	0.98	1.04	0.660 β = −0.06
IFN-γ	0.31	7.79	0.779 β = −0.58	0.16	3.10	0.535 β = −0.24	1.81	5.04	0.741 β = −0.26	0.35	3.62	0.928 β = −0.27
IL-1-α/IL-1F1	0.61	0.24	0.516 β = 0.29	0.27	0.51	0.889 β = −0.20	0.58	0.25	0.610 β = 0.27	0.60	0.44	0.238 β = 0.13
IL-1-ß/IL-1F2	0.33	0.28	0.603 β = 0.12	0.55	0.28	0.303 β = 0.67	0.38	0.15	0.197 β = 0.57	0.33	0.30	0.631 β = 0.07
IL-1ra/IL-1F3	589.99	380.29	0.007 β = 0.57	497.34	524.34	0.674 β = −0.07	541.60	472.59	0.548 β = 0.18	628.38	483.14	0.509 β = 0.39
IL-2	1.07	1.26	0.610 β = −0.13	1.30	1.11	0.603 β = 0.13	1.03	1.38	0.569 β = −0.25	0.90	1.21	0.936 β = −0.22
IL-4	0.09	0.10	0.267 β = −0.05	0.04	0.10	0.873 β = −0.30	0.08	0.11	0.952 β = −0.15	0.07	0.10	0.516 β = −0.15
IL-6	3.13	1.97	0.603 β = 0.36	2.97	2.72	0.603 β = 0.08	2.87	2.46	0.535 β = 0.13	4.02	2.29	0.009 β = 0.55
IL-7	5.74	4.53	0.180 β = 0.39	5.00	5.39	0.478 β = −0.12	5.05	6.05	0.180 β = −0,32	4.55	5.63	0.128 β = −0.36
IL-8/CXCL8	46.07	58.39	0.936 β = −0.13	52.93	49.75	0.653 β = 0.03	53.94	40.98	0.726 β = 0.14	46.90	51.25	0.810 β = −0.04
IL-10	0.12	0.01	0.795 β = 0.21	0.004	0.09	0.024 β = −0.17	0.113	0.019	0.036 β = 0.18	0.267	0.020	0.889 β = 0.49
IL-13	0.02	0.01	0.043 β = 1.00	0.02	0.02	0.478 β = 0	0.01	0.02	0.289 β = 0.66	0.02	0.02	0.575 β = 0
IL-15	0.15	0.18	0.327 β = −0.10	0.22	0.16	0.542 β = 0.20	0.14	0.22	0.849 β = −0.26	0.15	0.17	0.496 β = −0.06
PDGF-AA	9144.1	11,312.6	0.131 β = −0.44	9753.3	9868.2	0.992 β = −0.02	9473.9	10,765.5	0.298 β = −0.26	8995.5	10,160.7	0.529 β = −0.23
PDGF-AB/BB	9366.5	19,478.7	0.704 β = −0.35	38781.2	9298.8	0.928 β = 1.02	14,395.4	8589.0	0.756 β = 0,20	9290.1	13,888.0	0.912 β = −0.16
TGF-α	2.27	2.79	0.610 β = −0.17	1.66	2.54	0.944 β = −0.23	2.44	2.46	0.889 β = −0.01	2.44	2.44	0.873 β = 0
TNF-α	1.88	1.72	0.912 β = 0.05	2.18	1.78	0.936 β = 0.14	1.79	1.92	0.756 β = −0.04	2.23	1.68	0.764 β = 0.19
TRAIL	0.02	0.02	0.667 β = 0	0.01	0.02	0.226 β = −0.55	0.02	0.02	0.516 β = 0	0.03	0.01	0.007 β = 2
VEGF	145.73	112.69	0.928 β = 0.16	117.90	137.10	0.810 β = −0.10	139.15	124.72	0.719 β = 0.07	113.36	142.55	0.667 β = −0.14

COPD—Chronic Obstructive Pulmonary Disease. The values are presented as mean across all OSA patients. Mann–Whitney U Test was used for calculations. β—standardized mean difference. Statistically significant results (p < 0.05) are shown in bold.

Table 5. Associations between cytokine levels in OSA patients without any other diseases vs. in OSA patients with concomitant only disorders: Obesity defined by BMI > 30 kg/m² or only with hypertension. Levels of cytokines in patients with/without given disorder are presented as mean values. Mann–Whitney U Test was used for calculations. β—standardized mean difference. Statistically significant results (p ≤ 0.05) are shown in bold.

Cytokine	1	2	3
	OSA without Comorbidities	OSA + Obesity	OSA + Hypertension	1 vs. 2	1 vs. 3
	N = 7	N = 11	N = 9	p	p
CCL11/Eotaxin	12.63	3.65	6.19	0.779 β = 1.07	0.064 β = 0.75
CCL19/MIP-3-beta	175.40	232.44	213.74	0.015 β= −0.34	0.596 β = −0.33
CCL2/MCP-1	814.74	942.19	618.90	0.023 β= −0.40	0.342 β = 0.62
CCL20/MIP-3-alpha	30.99	12.76	38.90	0.107 β = 0.56	0.674 β = −0.17
CCL3/MIP-1-alpha	13.32	22.54	71.76	0.063 β = −0.34	0.030 β= −0.76
CCL4/MIP-1-beta	0.28	0.27	0.28	0.976 β = 0.20	0.833 β = 0
CCL5/RANTES	146.60	252.86	152.13	0.012 β= −0.59	0.912 β = −0.08
CD40 Ligand/TNFSF5	6.95	5.60	6.61	0.147 β = 0.42	0.913 β = 0.1
CX3CL1/Fractalkine	0.02	0.07	0.06	0.976 β = −0.71	0.044 β= −0.88
CXCL1/GRO-alpha	0.02	0.05	0.03	0.976 β = −0.75	0.749 β = −0.5
CXCL10/IP-10	79.36	62.88	43.96	0.056 β= 0.28	0.342 β = 0.68
CXCL2/GRO-beta	139.60	263.39	154.86	0.007 β= −0.62	0.674 β = −0.15
EGF	260.27	243.81	281.53	0.044 β= 0.16	0.748 β = −0.25
FGF-basic	26.54	10.27	10.36	0.976 β = 0.62	0.912 β = 0.57
Flt-3 Ligand	0.11	0.11	0.11	0.976 β = 0	0.834 β = 0
G-CSF	8.13	6.53	5.99	0.298 β = 0.41	0.342 β = 0.64
GM-CSF	0.02	0.02	0.02	0.976 β = 0	0.459 β = 0
Granzyme B	6.69	1.16	12.95	0.322 β = 0.58	0.562 β = −0.32
IFN-alpha	1.06	1.36	1.12	0.107 β = −0.18	0.091 β = −0.05
IFN-gamma	12.22	0.42	7.68	0.779 β = 0.59	0.912 β = 0.18
IL-1-alpha/IL-1F1	0.18	0.31	0.26	0.976 β = −0.59	0.749 β = −0.47
IL-1-beta/IL-1F2	0.12	0.15	0.38	0.987 β = −0.21	0.596 β = −0.59
IL-1ra/IL-1F3	386.32	527.49	356.16	0.005 β= −0.54	0.834 β = 0.16
IL-2	1.09	1.55	1.14	0.230 β = −0.24	0.873 β = −0.03
IL-3	0.02	0.001	0.01	0.989 β = 0.95	0.912 β = 0.33
IL-4	0.02	0.17	0.20	0.987 β = −0.71	0.749 β = −0.60
IL-6	2.29	2.56	1.71	0.271 β = −0.15	0.222 β = 0.48
IL-7	3.36	7.76	5.72	<0.001 β= −1.41	0.044 β= −0.94
IL-8/CXCL8	24.24	51.63	89.63	0.026 β= −0.40	0.167 β = −0.55
IL-10	0.01	0.02	0.01	0.976 β = −0.50	0.167 β = 0
IL-12 p70	0.001	0.002	0.001	0.976 β = 1	0.748 β = 0
IL-13	0.02	0.02	0.01	0.987 β = 0	0.395 β = 0.5
IL-15	0.47	0.04	0.01	0.978 β = 1.07	0.167 β = 1.06
IL-33	0.003	0.001	0.002	0.976 β = 0.66	0.912 β = 0.33
PDGF-AA	10,438.84	10,973.37	12,565.74	0.070 β = −0.10	0.674 β = −0.40
PDGF-AB/BB	10,774.76	7197.99	7242.01	0.342 β = 0.62	0.395 β = 0.57
TGF-alpha	3.59	1.74	3.20	0.211 β = 0.46	0.711 β = 0.08
TNF-alpha	1.67	2.07	1.83	0.177 β = −0.16	1.000 β = −0.05
TRAIL	0.02	0.01	0.01	0.976 β = 0.76	0.749 β = 0.76
VEGF	80.92	152.58	122.17	0.003 β= −0.78	0.204 β = −0.56

Table 6. Correlations of chemokine/cytokine levels with clinical data.

Chemokine/Cytokine	BMI	pO₂	pCO₂	AHI	Mean SaO₂	Minimal SaO₂	ODI
CCL11/Eotaxin	p = 0.553 r_s = −0.103	p = 0.019 r_s = 0.419	p = 0.331 r_s = −0.180	p = 0.103 r_s = −0.280	p = 0.714 r_s = 0.064	p = 0.636 r_s = 0.062	p = 0.350 r_s = −0.162
CCL19/MIP-3-ß	p = 0.054 r_s = 0.247	p = 0.876 r_s = −0.021	p = 0.978 r_s = 0.003	p = 0.346 r_s = 0.123	p = 0.428 r_s = 0.103	p = 0.612 r_s = 0.066	p = 0.340 r_s = 0.124
CCL2/MCP-1	p = 0.745 r_s = −0.042	p = 0.066 r_s = 0.249	p = 0.089 r_s = −0.230	p = 0.470 r_s = 0.094	p = 0.697 r_s = −0.050	p = 0.524 r_s = 0.083	p = 0.242 r_s = 0.152
CCL20/MIP-3-α	p = 0.167 r_s = −0.182	p = 0.950 r_s = 0.008	p = 0.098 r_s = −0.229	p = 0.726 r_s = −0.046	p = 0.122 r_s = 0.203	p = 0.143 r_s = 0.189	p = 0.483 r_s = −0.092
CCL3/MIP-1-α	p = 0.333 r_s = −0.144	p = 0.711 r_s = 0.057	p = 0.641 r_s = −0.072	p = 0.795 r_s = −0.039	p = 0.310 r_s = −0.151	p = 0.969 r_s = 0.005	p = 0.921 r_s = 0.014
CCL4/MIP-1-ß	p = 0.788 r_s = 0.034	p = 0.786 r_s = −0.037	p = 0.462 r_s = −0.101	p = 0.466 r_s = −0.095	p = 0.719 r_s = −0.046	p = 0.991 r_s = −0.001	p = 0.789 r_s = −0.034
CCL5/RANTES	p = 0.548 r_s = 0.083	p = 0.487 r_s = 0.100	p = 0.650 r_s = −0.109	p = 0.930 r_s = 0.012	p = 0.910 r_s = −0.015	p = 0.842 r_s = 0.026	p = 0.952 r_s = −0.008
CD40 Ligand/TNFSF5	p = 0.885 r_s = −0.018	p = 0.352 r_s = −0.127	p = 0.383 r_s = −0.119	p = 0.664 r_s = −0.057	p = 0.697 r_s = 0.050	p = 0.522 r_s = 0.083	p = 0.447 r_s = −0.099
CX3CL1/Fractalkine	p = 0.294 r_s = 0.165	p = 0.082 r_s = −0.285	p = 0.808 r_s = 0.040	p = 0.507 r_s = 0.105	p = 0.497 r_s = −0.107	p = 0.985 r_s = 0.002	p = 0.738 r_s = 0.053
CXCL1/GRO-α	p = 0.259 r_s = 0.393	p = 0.149 r_s = −0.490	p = 0.048 r_s = 0.636	p = 0.579 r_s = 0.200	p = 0.683 r_s = −0.147	p = 0.078 r_s = −0.227	p = 0.579 r_s = −0.200
CXCL10/IP-10	p = 0.471 r_s = 0.093	p = 0.164 r_s = −0.190	p = 0.808 r_s = 0.033	p = 0.320 r_s = 0.129	p = 0.471 r_s = −0.093	p = 0.586 r_s = −0.071	p = 0.334 r_s = 0.125
CXCL2/GRO-ß	p = 0.908 r_s = 0.015	p = 0.981 r_s = 0.003	p = 0.709 r_s = 0.051	p = 0.661 r_s = 0.057	p = 0.351 r_s = −0.121	p = 0.628 r_s = −0.063	p = 0.567 r_s = 0.074
EGF	p = 0.810 r_s = 0.031	p = 0.608 r_s = −0.070	p = 0.160 r_s = −0.191	p = 0.508 r_s = −0.086	p = 0.229 r_s = 0.156	p = 0.657 r_s = 0.058	p = 0.479 r_s = −0.092
FGF-basic	p = 0.284 r_s = 0.600	p = 0.800 r_s = −0.200	p = 0.051 r_s = 0.948	p = 0.747 r_s = 0.200	p = 0.218 r_s = 0.666	p = 0.722 r_s = −0.046	p = 0.747 r_s = 0.200
Flt-3 Ligand	p = 0.455 r_s = 0.097	p = 0.354 r_s = −0.127	p = 0.457 r_s = −0.102	p = 0.454 r_s = 0.097	p = 0.800 r_s = −0.032	p = 0.284 r_s = −0.139	p = 0.329 r_s = 0.126
G-CSF	p = 0.604 r_s = 0.067	p = 0.421 r_s = −0.110	p = 0.930 r_s = 0.011	p = 0.583 r_s = −0.071	p = 0.190 r_s = 0.169	p = 0.050 r_s = 0.252	p = 0.136 r_s = −0.192
GM-CSF	p = 0.262 r_s = −0.737	p = 1.000 r_s = 0.000	p = 0.666 r_s = 0.500	p = 0.051 r_s = −0.949	-	p = 0.826 r_s = 0.029	p = 0.262 r_s = −0.737
Granzyme B	p = 0.104 r_s = −0.355	p = 0.550 r_s = −0.134	p = 0.582 r_s = −0.123	p = 0.261 r_s = −0.250	p = 0.144 r_s = 0.321	p = 0.314 r_s = 0.131	p = 0.300 r_s = −0.231
IFN-α	p = 0.355 r_s = 0.120	p = 0.731 r_s = −0.041	p = 0.385 r_s = 0.119	p = 0.639 r_s = 0.051	p = 0.502 r_s = −0.087	p = 0.135 r_s = −0.192	p = 0.578 r_s = 0.072
IFN-γ	p = 0.025 r_s = −0.771	p = 0.380 r_s = −0.441	p = 0.505 r_s = −0.343	p = 0.113 r_s = −0.602	p = 0.863 r_s = 0.073	p = 0.891 r_s = −0.018	p = 0.153 r_s = −0.554
IL-1-α/IL-1F1	p = 0.086 r_s = 0.0568	p = 0.943 r_s = −0.027	p = 0.887 r_s = 0.055	p = 0.748 r_s = −0.116	p = 0.237 r_s = 0.411	p = 0.955 r_s = 0.007	p = 0.529 r_s = −0.226
IL-1-ß/IL-1F2	p = 0.567 r_s = 0.108	p = 0.453 r_s = −0.134	p = 0.840 r_s = 0.039	p = 0.319 r_s = 0.188	p = 0.486 r_s = −0.132	p = 0.186 r_s = −0.172	p = 0.615 r_s = 0.095
IL-1ra/IL-1F3	p = 0.002 r_s = 0.396	p = 0.035 r_s = −0.285	p = 0.130 r_s = 0.206	p = 0.078 r_s = 0.227	p = 0.548 r_s = −0.078	p = 0.483 r_s = −0.091	p = 0.077 r_s = 0.227
IL-2	p = 0.811 r_s = 0.033	p = 0.291 r_s = 0.153	p = 0.812 r_s = 0.034	p = 0.827 r_s = 0.030	p = 0.864 r_s = −0.023	p = 0.225 r_s = −0.158	p = 0.726 r_s = 0.048
IL-3	p = 0.600 r_s = −0.400	- -	p = 1.000 r_s = 0.000	p = 0.200 r_s = −0.800	p = 0.200 r_s = 0.800	- -	p = 0.600 r_s = −0.400
IL-4	p = 0.178 r_s = −0.342	p = 0.217 r_s = 0.326	p = 0.883 r_s = 0.038	p = 0.138 r_s = 0.374	p = 0.355 r_s = −0.239	p = 0.392 r_s = −0.111	p = 0.241 r_s = 0.300
IL-6	p = 0.320 r_s = 0.135	p = 0.056 r_s = −0.265	p = 0.796 r_s = −0.036	p = 0.723 r_s = −0.048	p = 0.233 r_s = −0.161	p = 0.439 r_s = −0.101	p = 0.629 r_s = 0.065
IL-7	p = 0.069 r_s = 0.236	p = 0.946 r_s = 0.009	p = 0.672 r_s = −0.058	p = 0.113 r_s = 0.206	p = 0.368 r_s = −0.118	p = 0.184 r_s = −0.172	p = 0.057 r_s = 0.246
IL-8/CXCL8	p = 0.468 r_s = −0.097	p = 0.767 r_s = 0.042	p = 0.722 r_s = −0.050	p = 0.513 r_s = 0.088	p = 0.498 r_s = −0.091	p = 0.738 r_s = −0.044	p = 0.416 r_s = 0.109
IL-10	p = 0.721 r_s = −0.049	p = 0.608 r_s = 0.074	p = 0.816 r_s = −0.033	p = 0.20 r_s = 0.174	p = 0.362 r_s = 0.125	p = 0.749 r_s = 0.042	p = 0.276 r_s = 0.149
IL-13	p = 0.786 r_s = 0.040	p = 0.750 r_s = 0.049	p = 0.239 r_s = −0.181	p = 0.339 r_s = −0.144	p = 0.124 r_s = 0.229	p = 0.309 r_s = −0.132	p = 0.402 r_s = −0.126
IL-15	p = 0.702 r_s = −0.090	p = 0.498 r_s = −0.170	p = 0.691 r_s = −0.100	p = 0.938 r_s = −0.018	p = 0.045 r_{s =} 0.452	p = 0.982 r_s = −0.003	p = 0.705 r_s = −0.090
IL-33	- -	p = 0.666 r_s = −0.500	p = 0.666 r_s = −0.500	p = 0.666 r_s = −0.500	p = 0.666 r_s = 0.500	- -	p = 0.666 r_s = −0.500
PDGF-AA	p = 0.600 r_s = −0.068	p = 0.902 r_s = 0.016	p = 0.738 r_s = 0.046	p = 0.183 r_s = −0.172	p = 0.117 r_s = 0.202	p = 0.691 r_s = 0.052	p = 0.120 r_s = −0.200
PDGF-AB/BB	p = 0.934 r_s = −0.011	p = 0.055 r_s = −0.290	p = 0.007 r_s = 0.398	p = 0.542 r_s = 0.089	p = 0.235 r_s = −0.172	p = 0.577 r_s = −0.073	p = 0.626 r_s = 0.071
TGF-α	p = 0.243 r_s = 0.215	p = 0.504 r_s = −0.131	p = 0.844 r_s = 0.038	p = 0.653 r_s = 0.083	p = 0.581 r_s = −0.102	p = 0.199 r_s = −0.166	p = 0.496 r_s = 0.126
TNF-α	p = 0.377 r_s = 0.170	p = 0.692 r_s = 0.083	p = 0.149 r_s = 0.296	p = 0.110 r_s = 0.303	p = 0.387 r_s = −0.166	p = 0.462 r_s = 0.096	p = 0.023 r_{s =} 0.422
TRAIL	p = 0.828 r_s = −0.031	p = 0.002 r_s = 0.438	p = 0.040 r_s = −0.303	p = 0.192 r_s = −0.187	p = 0.539 r_s = 0.088	p = 0.285 r_s = 0.139	p = 0.343 r_s = −0.136
VEGF	p = 0.548 r_s = 0.078	p = 0.789 r_s = −0.036	p = 0.695 r_s = 0.054	p = 0.587 r_s = 0.070	p = 0.889 r_s = 0.018	p = 0.659 r_s = −0.057	p = 0.429 r_s = 0.103

Spearman’s Rho correlation test was used for calculations. Statistically significant results (p < 0.05) are shown in bold, r_s—Spearman’s Rho correlation coefficient, BMI—body mass index, AHI—apnea–hypopnea index, pO₂—partial pressure of oxygen, pCO₂—partial pressure of carbon dioxide, Mean SaO₂—mean saturation during sleep, Minimal SaO₂—minimal saturation at the end of apneas/hypopneas, ODI—oxygen desaturation index.

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Chaszczewska-Markowska, M.; Górna, K.; Bogunia-Kubik, K.; Brzecka, A.; Kosacka, M. The Influence of Comorbidities on Chemokine and Cytokine Profile in Obstructive Sleep Apnea Patients: Preliminary Results. J. Clin. Med. 2023, 12, 801. https://doi.org/10.3390/jcm12030801

AMA Style

Chaszczewska-Markowska M, Górna K, Bogunia-Kubik K, Brzecka A, Kosacka M. The Influence of Comorbidities on Chemokine and Cytokine Profile in Obstructive Sleep Apnea Patients: Preliminary Results. Journal of Clinical Medicine. 2023; 12(3):801. https://doi.org/10.3390/jcm12030801

Chicago/Turabian Style

Chaszczewska-Markowska, Monika, Katarzyna Górna, Katarzyna Bogunia-Kubik, Anna Brzecka, and Monika Kosacka. 2023. "The Influence of Comorbidities on Chemokine and Cytokine Profile in Obstructive Sleep Apnea Patients: Preliminary Results" Journal of Clinical Medicine 12, no. 3: 801. https://doi.org/10.3390/jcm12030801

APA Style

Chaszczewska-Markowska, M., Górna, K., Bogunia-Kubik, K., Brzecka, A., & Kosacka, M. (2023). The Influence of Comorbidities on Chemokine and Cytokine Profile in Obstructive Sleep Apnea Patients: Preliminary Results. Journal of Clinical Medicine, 12(3), 801. https://doi.org/10.3390/jcm12030801

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The Influence of Comorbidities on Chemokine and Cytokine Profile in Obstructive Sleep Apnea Patients: Preliminary Results

Abstract

1. Introduction

2. Materials and Methods

2.1. Patients and Controls

2.2. Polysomnography

2.3. Chemokine and Cytokine Serum Levels

2.4. Statistical Analysis

3. Results

4. Discussion

5. Conclusions

Author Contributions

Funding

Institutional Review Board Statement

Informed Consent Statement

Data Availability Statement

Conflicts of Interest

References

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