Next Article in Journal
Surgical Management of Ovarian Endometrioma: Impact on Ovarian Reserve Parameters and Reproductive Outcomes
Next Article in Special Issue
Association between Obstructive Sleep Apnea and Heart Failure in Adults—A Systematic Review
Previous Article in Journal
Sex Differences in Prognosis of Heart Failure Due to Ischemic and Nonischemic Cardiomyopathy
Previous Article in Special Issue
Medical Treatment of Obstructive Sleep Apnea in Children
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
JCM
Volume 12
Issue 16
10.3390/jcm12165325
jcm-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Association of TNF-α (-308G/A) Gene Polymorphism with Changes in Circulating TNF-α Levels in Response to CPAP Treatment in Adults with Coronary Artery Disease and Obstructive Sleep Apnea

by
Yeliz Celik
1,
Yüksel Peker
1,2,3,4,5,*,
Tülay Yucel-Lindberg
6,
Tilia Thelander
7 and
Afrouz Behboudi
7
1
Department of Pulmonary Medicine, Koc University School of Medicine, and Koc University Research Center for Translational Medicine (KUTTAM), Koc University, 34010 Istanbul, Turkey
2
Department of Molecular and Clinical Medicine, Sahlgrenska Academy, University of Gothenburg, 40530 Gothenburg, Sweden
3
Department of Clinical Sciences, Respiratory Medicine and Allergology, Faculty of Medicine, Lund University, 22185 Lund, Sweden
4
Division of Pulmonary, Allergy, and Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA
5
Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, and Harvard Medical School, Boston, MA 02115, USA
6
Department of Dental Medicine, Karolinska Institute, 17177 Stockholm, Sweden
7
Division of Biomedicine, School of Heath Sciences, University of Skövde, 54128 Skövde, Sweden
*
Author to whom correspondence should be addressed.
J. Clin. Med. 2023, 12(16), 5325; https://doi.org/10.3390/jcm12165325
Submission received: 14 July 2023 / Revised: 10 August 2023 / Accepted: 11 August 2023 / Published: 16 August 2023
(This article belongs to the Special Issue Current Perspectives on the Treatment of Obstructive Sleep Apnea—Part II)
Browse Figures
Review Reports Versions Notes

Abstract

:
Rationale: We recently demonstrated that patients with coronary artery disease (CAD) and obstructive sleep apnea (OSA) carrying the tumor necrosis factor-alpha (TNF-α) A allele had increased circulating TNF-α levels compared with the ones carrying the TNF-α G allele. In the current study, we addressed the effect of TNF-α (-308G/A) gene polymorphism on circulating TNF-α levels following continuous positive airway pressure (CPAP) therapy. Methods: This study was a secondary analysis of the RICCADSA trial (NCT00519597) conducted in Sweden. CAD patients with OSA (apnea–hypopnea index) of ≥15 events/h and an Epworth Sleepiness Scale (ESS) score of <10 were randomized to CPAP or no-CPAP groups, and OSA patients with an ESS score of ≥10 were offered CPAP treatment. Blood samples were obtained at baseline and 12-month follow-up visits. TNF-α was measured by immunoassay (Luminex, R&D Systems). Genotyping of TNF-α-308G/A (single nucleotide polymorphism Rs1800629) was performed by polymerase chain reaction–restriction fragment length polymorphism. Results: In all, 239 participants (206 men and 33 women; mean age 64.9 (SD 7.7) years) with polymorphism data and circulating levels of TNF-α at baseline and 1-year follow-up visits were included. The median circulating TNF-α values fell in both groups between baseline and 12 months with no significant within- or between-group differences. In a multivariate linear regression model, a significant change in circulating TNF-α levels from baseline across the genotypes from GA to GA and GA to AA (standardized β-coefficient −0.129, 95% confidence interval (CI) −1.82; −0.12; p = 0.025) was observed in the entire cohort. The association was more pronounced among the individuals who were using the device for at least 4 h/night (n = 86; standardized β-coefficient −2.979 (95% CI −6.11; −1.21); p = 0.004)), whereas no significant association was found among the patients who were non-adherent or randomized to no-CPAP. The participants carrying the TNF-α A allele were less responsive to CPAP treatment regarding the decline in circulating TNF-α despite CPAP adherence (standardized β-coefficient −0.212, (95% CI −5.66; −1.01); p = 0.005). Conclusions: Our results suggest that TNF-α (-308G/A) gene polymorphism is associated with changes in circulating TNF-α levels in response to CPAP treatment in adults with CAD and OSA.

1. Introduction

Coronary artery disease (CAD) is associated with high mortality [1]. The traditionally recognized risk factors for CAD are age, male sex, hypertension, diabetes, and hyperlipidemia. It has also been proposed that the interaction between genetic and environmental factors influences the development of CAD [1,2,3].
Obstructive sleep apnea (OSA) is characterized by intermittent upper airway collapse during sleep, causing sleep fragmentation and intermittent hypoxia [4]. Almost 50% of CAD patients have OSA, and many of them do not report excessive daytime sleepiness (ESS), which is one of the cardinal symptoms of OSA [5]. Individuals with OSA have been reported to have an increased risk of incident CAD compared with adults without OSA [6].
Vascular inflammation plays a key role in the development of atherosclerotic plaques and CAD [7]. It has also been suggested that circulating levels of inflammatory markers can predict future cardiovascular events [8,9]. Elevated levels of high-sensitivity C-reactive protein (hs-CRP), interleukin (IL)-6, and tumor necrosis factor (TNF)-α have been reported in adults with OSA [10,11]. Treatment of OSA with continuous positive airway pressure (CPAP) has been suggested to normalize the levels of circulating inflammatory markers, supporting the link between systemic inflammation and OSA [12]. It has also been proposed that inflammation can be a predisposing factor for OSA [13,14,15], not just a consequence of OSA.
TNF-α is a pro-inflammatory cytokine that is important for the immune system and plays a notable role in the development of autoimmune and infectious diseases as well as atherosclerosis and CAD [16]. TNF-α also plays a crucial role in sleep regulation [17]. Many OSA patients have elevated levels of circulating TNF-α [18]. Existing data also suggest that genetic and environmental factors are involved in the development of OSA [19], and TNF-α has received special attention in this context [17,20]. There is an SNP (Rs1800629) in the promoter region of the TNF-α (position 308G/A); allele A at this position (TNF-α-308A) is suggested to be associated with a higher occurrence of OSA [21] as well as with the severity of this disorder [18,22,23,24,25]. There are also reports concerning the association of TNF-α-308G/A (rs1800629) polymorphism with the risk of many diseases, such as allograft rejection [26], asthma [27], chronic obstructive pulmonary disease [28], ischemic stroke [29], rheumatoid arthritis [30], and systemic lupus erythematosus [31]. An association between the TNF-α-308A allele and obesity has also been reported [17,21], whereas conflicting results have been reported regarding the relationship between TNF-α-308G/A polymorphism and CAD. One study suggested that TNF-α-308G/A polymorphism is associated with ST-elevation myocardial infarction and high plasma levels of biochemical ischemia markers [32], and a meta-analysis demonstrated a significant association between TNF-α-308G/A and the risk of acute myocardial infarction [33]. On the other hand, a recent meta-analysis showed no significant association [34].
We recently demonstrated that patients with CAD and OSA carrying the TNF-α A allele had increased circulating TNF-α levels compared with the ones carrying the TNF-α G allele [35] in the “Randomized Intervention with CPAP in CAD and OSA” (RICCADSA) cohort [36]. In the current study, we addressed the role of TNF-α (-308G/A) gene polymorphism on circulating TNF-α levels in response to 12 months of CPAP therapy.

2. Materials and Methods

2.1. Study Participants

The methodology of the main RICCADSA trial was described elsewhere [36]. In total, 511 CAD patients who underwent percutaneous coronary intervention (PCI) or coronary artery bypass grafting (CABG) in Skaraborg County of West Götaland, Sweden, were included in the RICCADSA trial between 2005 and 2010 (Figure 1). The participants with OSA, defined as an apnea–hypopnea index (AHI) of ≥15/h, on the home sleep apnea test (HSAT) at screening and an Epworth Sleepiness Scale (ESS) score of <10 were randomized to CPAP or no-CPAP groups. Patients with ESS scores of ≥10 were categorized as having excessive daytime sleepiness (EDS) and were offered CPAP treatment. The CAD patients with AHI < 5/h were categorized as no-OSA in the main protocol. For the genetic analysis, blood samples were collected at the final visit in 2012/2013 from 384 eligible participants, and 239 patients with OSA were included as the final study population for the current TNF-α-308G/A polymorphism study to evaluate the changes in circulating TNF-α levels from baseline to 12 months after CPAP treatment (Figure 1).

2.2. Study Oversight

The study protocol was approved by the Ethics Committee of the Medical Faculty of the University of Gothenburg (approval nr 207-05, 09.13.2005; amendment T744-10, 11.26.2010; amendment T512-11, 06.16.2011; additional approval for the molecular analysis, approval nr 814-17, 11.21.2017). Written informed consent was obtained from all participants. The main RICCADSA trial was registered with ClinicalTrials.gov (NCT 00519597).

2.3. Sleep Studies

The Embletta® Portable Digital System device (Embla, Broomfield, CO, USA) was used for the HSATs [36]. Apnea was defined as at least a 90% cessation of airflow, and hypopnea was defined as at least a 50% reduction in nasal pressure amplitude and/or thoracoabdominal movement for at least 10 s, following the Chicago criteria [37]. The total number of significant drops in SpO2 exceeding 4% from the immediately preceding baseline was also recorded, and the oxygen desaturation index (ODI) was determined as the number of significant desaturations per hour.

2.4. Epworth Sleepiness Scale

The ESS [38] was assessed to measure subjective daytime sleepiness. The ESS has eight items asking about the risk of dozing off under 8 different situations, and a score of at least 10 out of 24 was defined as EDS.

2.5. Comorbidities

Demographics, smoking habits, and medical history of the study cohort were obtained from the medical records. Individuals with a body mass index (BMI) of ≥30 kg/m2 were defined as obese, and abdominal obesity was defined as a waist-to-hip ratio (WHR) of ≥0.9 for men and a WHR of ≥0.8 for women [39].

2.6. TNF-α Circulating Concentration

All blood samples were collected in the morning (07:00–08.00 am) after overnight fasting using EDTA (ethylenediaminetetraacetic acid) tubes, as previously described [40]. The tubes underwent centrifugation, and the resulting plasma/serum samples were divided into aliquots and subsequently stored at −70 °C until analysis. Circulating TNF-α levels were measured in the plasma samples (undiluted) using commercially available MILLIPLEX MAP (based on Luminex technology) human adipokine assay kits according to the manufacturer’s instructions (Merck Millipore, Burlington, MA, USA). The assay sensitivities (minimum detectable levels) for TNF-α were 0.14 pg/mL, and all samples exhibited levels within the standard curve, covering a spectrum of 0 to 10,000 pg/mL. The intra-assay variability ranged from 1.4% to 7.9%, while the inter-assay variability was below 21% for the assessment of TNF-α concentrations. These values were calculated from the mean of the percentage coefficient of variability from multiple reportable results across two different concentrations of the samples in one experiment or from two results each for two different concentrations of samples across several different experiments.

2.7. TNF-α Promotor -308G/A (Rs1800629) SNA Genotyping

As previously described in detail [35], genomic DNA was isolated from whole blood samples collected in EDTA-coated tubes using the PAXgene Blood DNA Kit (PreAnalytiX; Qiagen). The quality and concentration of DNA samples were determined using a nanodrop photometer (NanoDrop 2000; Thermo Scientific, Waltham, MA, USA), and DNA samples were stored at −80 degrees. TNF-α promoter −308A/G (Rs1800629) genotyping analysis was performed by polymerase chain reaction–restriction fragment length polymorphism (PCR–RFLP), as previously described [35].

2.8. Statistical Analysis

For descriptive statistics, variables were reported as medians with interquartile ranges (IQR) for continuous variables and as percentages for categorical variables. The Shapiro–Wilk test was used to test the normality assumption of the current data for all variables. Between-group differences stratified by CPAP allocation and CPAP usage in baseline characteristics, as well as changes from baseline in circulating TNF-α levels, were tested by the Mann–Whitney test for continuous variables and the Chi-square test for categorical data. Within-group differences in changes from baseline in circulating TNF-α levels were tested by the Wilcoxon signed-rank test. A univariate linear regression analysis was performed to test the association between the change from baseline to the 12-month follow-up in circulating TNF-α levels and age, sex, ESS, BMI, WHR, AHI, ODI, OSA, and comorbidities, as well as TNF-α genotypes (coded as GG = 0, GA = 1, and AA = 2) and TNF-α alleles (coded as G = 0, and A = 1), respectively. Multivariate models included the same significant covariates as the univariate analysis as well as the variables of age, BMI, and sex in order to align with the recent guidelines [41]. All statistical tests were two-sided, odds ratios (ORs) with 95% confidence interval (CI) were reported, and a p-value of <0.05 was considered significant. Statistical analyses were performed using SPSS® 28.0 for Windows® (SPSS Inc., Chicago, IL, USA).

3. Results

The study population consisted of 239 participants (mean age 64.9 ± 7.7 years; male, 86%). As presented in Table 1, patients allocated to the no-CPAP group were slightly older and less sleepy, and the proportion of individuals with diabetes at baseline was lower than that among the patients allocated to CPAP treatment. The circulating levels of TNF-α at baseline did not differ significantly between the groups.
As illustrated in Figure 2A, TNF-α-GG was the most prevalent genotype in both groups, whereas TNF-α-AA in the CPAP group and TNF-α-GA in the no-CPAP group were the least frequent ones, respectively.
The median circulating levels of TNF-α decreased from 4.87 (3.43–6.99) pg/mL to 4.62 (3.59–6.59) pg/mL in patients allocated to the CPAP group (p = 0.549) and from 5.15 (3.92–6.54) pg/mL to 4.50 (3.64–7.11) pg/mL in patients allocated to the no-CPAP group (p = 0.665), with no significant between-group differences in the magnitude of change from baseline.
When analyzing the study population after stratifying by CPAP usage, the baseline characteristics did not differ significantly, except for ESS scores and the proportion of individuals with baseline EDS, which were higher among patients who used the device for at least 4 h/night during the first 12 months (Table 2).
As illustrated in Figure 3A, TNF-α-GG was the most prevalent genotype and TNF-α-AA the least frequent one in both CPAP usage groups.
As illustrated in Figure 4, the median circulating levels of TNF-α decreased from 4.84 (3.48–7.53) pg/mL to 4.72 (3.63–7.20) pg/mL in patients who used the device for at least 4 h/night (p = 0.577) and from 5.24 (3.59–6.85) pg/mL to 4.51 (3.50–6.75) pg/mL in patients allocated to the no-CPAP group or who used the device for less than 4 h/night (p = 0.199), with no significant between-group differences in the magnitude of change from baseline.
In a multivariate linear regression model, a significant decline in the change from baseline in circulating TNF-α levels across the genotypes from GG to GA and GA to AA was observed in the entire cohort (Table 3). The association was more pronounced among individuals who were using the device for at least 4 h/night, whereas no significant association was found among the patients who were non-adherent or randomized to the no-CPAP group. ESS scores at baseline tended to be inversely correlated with the change in circulating TNF-α levels from baseline to 12 months in the entire cohort (Table 3).
As shown in Table 4, the participants carrying the TNF-α A allele were less responsive to CPAP treatment regarding the decline in circulating TNF-α levels despite CPAP adherence. Baseline AHI was also inversely correlated with a decline in the change from baseline in circulating TNF-α levels among patients who were adherent to CPAP. No significant changes were observed among patients who were randomized to the no-CPAP group or those who were using the device for less than 4 h/night, except for baseline AHI, which was associated with the change in circulating TNF-α levels (Table 4).

4. Discussion

In the current revascularized CAD cohort with OSA, TNF-α-308G/A gene polymorphism was significantly correlated with the change in circulating TNF-α levels from baseline in response to 12 months of CPAP treatment, independent of age, sex, BMI, baseline AHI, ESS, and diabetes. The participants carrying the TNF-α A allele were less responsive to CPAP treatment in terms of the decline in circulating TNF-α levels despite adequate CPAP adherence levels.
To the best of our knowledge, this is the first study to address the association of TNF-α-308G/A polymorphism with change in circulating TNF-α levels in response to the alleviation of OSA with CPAP treatment in a Swedish cardiac population. Previous studies have suggested an association between the TNF-α -308A allele and OSA susceptibility in a British population [24] as well as in an obese Asian Indian population [22], whereas neutral results were reported in a Polish cohort [21] and a Turkish cohort [42]. Notwithstanding, two meta-analyses have supported the significant association between TNF-α -308G/A polymorphism and OSA [43,44].
In our entire cohort including adults without OSA as a control group, we found no significant difference between CAD patients with vs. without OSA in the frequency of TNF-α-308G/A polymorphism or TNF-α -308 alleles [35]. We found a similar -308A allele frequency in the no-OSA group, and, as interpreted in the previous report, this might be due to the confounding effect of other comorbidities, such as obesity, hypertension, and diabetes mellitus, given that individuals without OSA were not healthy controls [27]. Moreover, CAD per se is an inflammatory condition mediated by the activity of pro-inflammatory cytokines, including TNF-α [45]. The effect of TNF-α gene polymorphism on CAD pathogenesis has also been investigated previously, and TNF-α-308G/A polymorphism has been suggested to be involved in CAD development [45,46], whereas others reported no evidence for such an association [47]. Additionally, a recent meta-analysis suggested no significant relationship between TNF-α-308G/A polymorphism and the development of CAD [34].
Circulating levels of inflammatory markers predict future cardiovascular events in the general population [8] as well as in cardiac populations [9], and TNF-α levels are elevated in individuals with OSA compared with controls [25,48]. It has also been argued that inflammation can be a predisposing factor for OSA [13,14,15]; thus, this association could be bidirectional. In our first study on the effect of CPAP on inflammatory markers, including hs-CRP, IL-6, IL-8, and TNF-α, in the RICCADSA cohort, only IL-6 levels decreased after one year, both in the CPAP and no-CPAP arms [49]. This was probably indicative of a natural improvement in cardiac disease rather than the effect of CPAP treatment per se. We also demonstrated that patients with CAD and OSA carrying the TNF-α A allele had increased circulating TNF-α levels compared with the ones carrying the TNF-α G allele [35]. The TNF-α-308A allele is known to promote a two-fold increase in TNF transcription activity [50]. TNF-α is a mediator of the sleep regulatory system, and the fragmented sleep pattern associated with OSA is believed to increase circulatory levels of TNF-α [17]. Our current results clearly support a recent review of OSA heterogeneity regarding cardiovascular morbidities [1], suggesting that the response to CPAP treatment is modulated by genetic mechanisms, namely, in the current report, by TNF-α (-308G/A) gene polymorphism regarding the change in circulating levels of TNF-𝛼 from baseline. In other words, individuals with CAD and OSA carrying the TNF-α A allele seem to have an increased risk of elevated levels of circulating TNF-α, and the increased inflammatory activity is less likely to normalize despite CPAP use for at least 4 h/night. Whether or not those individuals should use the device even longer in order to reduce the levels of circulating TNF-α levels warrants further research. The clinical implications of our findings may also include that TNF-α-308G/A genotyping together with the analysis of TNF-α levels can be used for the prognostic evaluation of patients with CAD and concomitant OSA. For instance, patients carrying the A allele and higher levels of TNF-α could be included in a tighter follow-up scheme compared with those carrying the G allele.
We should acknowledge certain limitations. As also stated in the previous report [35], the power estimate for the entire RICCADSA cohort was conducted for the primary outcome and not for the secondary outcomes assessed in this study. Moreover, our results are limited to a Swedish CAD cohort and thus are not generalizable to adults with OSA in the general population or sleep clinic cohorts in other regions. Additionally, the follow-up period was relatively short in the context of the association of changes in circulating TNF-𝛼 levels at 12 months with long-term adverse outcomes. Finally, our results are limited to TNF-α (-308G/A) (rs1800629) polymorphism and not the TNF-α (-308G/A) (rs3611525) polymorphism. However, TNF-α-308G/A (rs1800629) and TNF-α-238G/A (rs361525) are located very close to each other and are hence tightly linked. In fact, in our sequencing results, we noticed that polymorphism in one 100% mirrored the other, and for the sake of simplicity, we chose to focus on and present only one of these SNPs. Accordingly, we propose that the TNF-α-308G/A (rs1800629) polymorphism analysis results can be extrapolated to TNF-α-238G/A (rs361525) SNP due to their tight linkage.

5. Conclusions

We conclude that TNF-α-308G/A gene polymorphism was significantly correlated with the change in the circulating TNF-α levels from baseline in response to 12 months of CPAP treatment in this revascularized Swedish CAD cohort, independent of age, sex, BMI, baseline AHI, ESS, and diabetes. The participants carrying the TNF-α A allele were less responsive to CPAP treatment in terms of the decline in circulating TNF-α levels despite adequate CPAP adherence levels. Further prospective studies in larger cohorts and different geographical locations are warranted in order to better clarify whether the combined gentrifying and protein level analysis can be used as a prognostic biomarker for improved clinical follow-up of patients with CAD and concomitant OSA.

Author Contributions

Conception and design: Y.P. and A.B. Analysis and interpretation: Y.P., Y.C., T.Y.-L., T.T. and A.B. Drafting the manuscript for important intellectual content: Y.C., Y.P. and A.B. All authors have read and agreed to the published version of the manuscript.

Funding

This study was supported by grants from the Swedish Research Council (521-2011-537 and 521-2013-3439); the Swedish Heart–Lung Foundation (20080592, 20090708, and 20100664); the “Agreement concerning research and education of doctors” of Västra Götalandsregionen (ALFGBG-11538 and ALFGBG-150801); the research fund at Skaraborg Hospital (VGSKAS-4731, VGSKAS-5908, VGSKAS-9134, VGSKAS-14781, VGSKAS-40271, and VGSKAS-116431); Skaraborg Research and Development Council (VGFOUSKB-46371); Lund University; the Heart Foundation of Kärnsjukhuset; the ResMed Foundation; and ResMed Ltd. None of the funders had any direct influence on the design of the study, the analysis of the data, data collection, the drafting of the manuscript, or the decision to publish.

Institutional Review Board Statement

The study protocol was approved by the Ethics Committee of the Medical Faculty of the University of Gothenburg (approval nr 207-05, 09.13.2005; amendment T744-10, 11.26.2010; amendment T512-11, 06.16.2011; additional approval for the molecular analysis, approval nr 814-17, 11.21.2017).

Informed Consent Statement

Written informed consent was obtained from all participants.

Data Availability Statement

Individual participant data that underlie the results reported in this article can be obtained by contacting the corresponding author, [email protected].

Conflicts of Interest

Y.C., T.Y.-L., T.T. and A.B. report no conflicts of interest. Y.P. received institutional grants from the ResMed Foundation.

References

  1. Redline, S.; Azarbarzin, A.; Peker, Y. Obstructive sleep apnoea heterogeneity and cardiovascular disease. Nat. Rev. Cardiol. 2023, 20, 560–573. [Google Scholar] [CrossRef] [PubMed]
  2. Musunuru, K.; Kathiresan, S. Genetics of Common, Complex Coronary Artery Disease. Cell 2019, 177, 132–145. [Google Scholar] [CrossRef]
  3. Khera, A.V.; Kathiresan, S. Genetics of coronary artery disease: Discovery, biology and clinical translation. Nat. Rev. Genet. 2017, 18, 331–344. [Google Scholar] [CrossRef]
  4. Young, T.; Palta, M.; Dempsey, J.; Peppard, P.E.; Nieto, F.J.; Hla, K.M. Burden of sleep apnea: Rationale, design, and major findings of the Wisconsin Sleep Cohort study. WMJ Off. Publ. State Med. Soc. Wis. 2009, 108, 246–249. [Google Scholar]
  5. Peker, Y.; Franklin, K.; Hedner, J. Coronary artery disease and sleep apnea. In Principles and Practice of Sleep Medicine, 7th ed.; Roth, K., Goldtstein, Dement, W.T., Eds.; Elsevier Inc.: Philadelphia, PA, USA, 2022; pp. 1453–1461. ISBN 978-0-323-66189-8. [Google Scholar]
  6. Turgut Çelen, Y.; Peker, Y. Cardiovascular consequences of sleep apnea: II-Cardiovascular mechanisms. Anatol. J. Cardiol. 2010, 10, 168–175. [Google Scholar] [CrossRef] [PubMed]
  7. Hansson, G.K. Inflammation, atherosclerosis, and coronary artery disease. N. Engl. J. Med. 2005, 352, 1685–1695. [Google Scholar] [CrossRef] [PubMed]
  8. Cesari, M.; Penninx, B.W.; Newman, A.B.; Kritchevsky, S.B.; Nicklas, B.J.; Sutton-Tyrrell, K.; Pahor, M. Inflammatory markers and onset of cardiovascular events: Results from the Health ABC study. Circulation 2003, 108, 2317–2322. [Google Scholar] [CrossRef]
  9. He, L.P.; Tang, X.Y.; Ling, W.H.; Chen, W.Q.; Chen, Y.M. Early C-reactive protein in the prediction of long-term outcomes after acute coronary syndromes: A meta-analysis of longitudinal studies. Heart 2010, 96, 339–346. [Google Scholar] [CrossRef]
  10. Patt, B.T.; Jarjoura, D.; Haddad, D.N.; Sen, C.K.; Roy, S.; Flavahan, N.A.; Khayat, R.N. Endothelial dysfunction in the microcirculation of patients with obstructive sleep apnea. Am. J. Respir. Crit. Care Med. 2010, 182, 1540–1545. [Google Scholar] [CrossRef]
  11. Arnardottir, E.S.; Mackiewicz, M.; Gislason, T.; Teff, K.L.; Pack, A.I. Molecular signatures of obstructive sleep apnea in adults: A review and perspective. Sleep 2009, 32, 447–470. [Google Scholar] [CrossRef]
  12. Gabryelska, A.; Łukasik, Z.M.; Makowska, J.S.; Białasiewicz, P. Obstructive Sleep Apnea: From Intermittent Hypoxia to Cardiovascular Complications via Blood Platelets. Front. Neurol. 2018, 9, 635. [Google Scholar] [CrossRef]
  13. Reid, M.B.; Lännergren, J.; Westerblad, H. Respiratory and limb muscle weakness induced by tumor necrosis factor-alpha: Involvement of muscle myofilaments. Am. J. Respir. Crit. Care Med. 2002, 166, 479–484. [Google Scholar] [CrossRef]
  14. Boyd, J.H.; Petrof, B.J.; Hamid, Q.; Fraser, R.; Kimoff, R.J. Upper airway muscle inflammation and denervation changes in obstructive sleep apnea. Am. J. Respir. Crit. Care Med. 2004, 170, 541–546. [Google Scholar] [CrossRef] [PubMed]
  15. Huang, T.; Goodman, M.; Li, X.; Sands, S.A.; Li, J.; Stampfer, M.J.; Saxena, R.; Tworoger, S.S.; Redline, S. C-reactive Protein and Risk of OSA in Four US Cohorts. Chest 2021, 159, 2439–2448. [Google Scholar] [CrossRef] [PubMed]
  16. Nadeem, R.; Molnar, J.; Madbouly, E.M.; Nida, M.; Aggarwal, S.; Sajid, H.; Naseem, J.; Loomba, R. Serum inflammatory markers in obstructive sleep apnea: A meta-analysis. J. Clin. Sleep Med. 2013, 9, 1003–1012. [Google Scholar] [CrossRef]
  17. Kheirandish-Gozal, L.; Gozal, D. Obstructive Sleep Apnea and Inflammation: Proof of Concept Based on Two Illustrative Cytokines. Int. J. Mol. Sci. 2019, 20, 459. [Google Scholar] [CrossRef]
  18. Ming, H.; Tian, A.; Liu, B.; Hu, Y.; Liu, C.; Chen, R.; Cheng, L. Inflammatory cytokines tumor necrosis factor-α, interleukin-8 and sleep monitoring in patients with obstructive sleep apnea syndrome. Exp. Ther. Med. 2019, 17, 1766–1770. [Google Scholar] [CrossRef]
  19. Mukherjee, S.; Saxena, R.; Palmer, L.J. The genetics of obstructive sleep apnoea. Respirology 2018, 23, 18–27. [Google Scholar] [CrossRef] [PubMed]
  20. Kent, B.D.; Ryan, S.; McNicholas, W.T. The genetics of obstructive sleep apnoea. Curr. Opin. Pulm. Med. 2010, 16, 536–542. [Google Scholar] [CrossRef]
  21. Popko, K.; Gorska, E.; Potapinska, O.; Wasik, M.; Stoklosa, A.; Plywaczewski, R.; Winiarska, M.; Gorecka, D.; Sliwinski, P.; Popko, M.; et al. Frequency of distribution of inflammatory cytokines IL-1, IL-6 and TNF-alpha gene polymorphism in patients with obstructive sleep apnea. J. Physiol. Pharmacol. 2008, 59 (Suppl. S6), 607–614. [Google Scholar]
  22. Bhushan, B.; Guleria, R.; Misra, A.; Luthra, K.; Vikram, N.K. TNF-alpha gene polymorphism and TNF-alpha levels in obese Asian Indians with obstructive sleep apnea. Respir. Med. 2009, 103, 386–392. [Google Scholar] [CrossRef]
  23. Bhatt, S.P.; Guleria, R.; Vikram, N.K.; Vivekanandhan, S.; Singh, Y.; Gupta, A.K. Association of inflammatory genes in obstructive sleep apnea and non alcoholic fatty liver disease in Asian Indians residing in north India. PLoS ONE 2018, 13, e0199599. [Google Scholar] [CrossRef] [PubMed]
  24. Riha, R.L.; Brander, P.; Vennelle, M.; Mcardle, N.; Kerr, S.M.; Anderson, N.H.; Douglas, N.J. Tumour necrosis factor-α (−308) gene polymorphism in obstructive sleep apnoea–hypopnoea syndrome. Eur. Respir. J. 2005, 26, 673–678. [Google Scholar] [CrossRef] [PubMed]
  25. Khalyfa, A.; Serpero, L.D.; Kheirandish-Gozal, L.; Capdevila, O.S.; Gozal, D. TNF-α gene polymorphisms and excessive daytime sleepiness in pediatric obstructive sleep apnea. J. Pediatr. 2011, 158, 77–82. [Google Scholar] [CrossRef] [PubMed]
  26. Pawlik, A.; Domanski, L.; Rozanski, J.; Florczak, M.; Dabrowska-Zamojcin, E.; Dutkiewicz, G.; Gawronska-Szklarz, B. IL-2 and TNF-alpha promoter polymorphisms in patients with acute kidney graft rejection. Transplant. Proc. 2005, 37, 2041–2043. [Google Scholar] [CrossRef]
  27. Aoki, T.; Hirota, T.; Tamari, M.; Ichikawa, K.; Takeda, K.; Arinami, T.; Shibasaki, M.; Noguchi, E. An association between asthma and TNF-308G/A polymorphism: Meta-analysis. J. Hum. Genet. 2006, 51, 677–685. [Google Scholar] [CrossRef]
  28. Zang, S.; Wang, C.; Xi, B.; Li, X. Association between the tumour necrosis factor-α-308G/A polymorphism and chronic obstructive pulmonary disease: An update. Respirology 2011, 16, 107–115. [Google Scholar] [CrossRef]
  29. Song, D.; Cheng, D. Associations of TNFα-308G/A and TNFα-238G/A Polymorphisms with Ischemic Stroke in East Asians and Non-East Asians: A Meta-Analysis. Genet. Test Mol. Biomarkers 2017, 21, 10–16. [Google Scholar] [CrossRef]
  30. O’Rielly, D.D.; Roslin, N.M.; Beyene, J.; Pope, A. Rahman PTNF-alpha-308G/A polymorphism responsiveness to TNF-alpha blockade therapy in moderate to severe rheumatoid arthritis: A systematic review meta-analysis. Pharmacogenom. J. 2009, 9, 161–167. [Google Scholar] [CrossRef]
  31. Guarnizo-Zuccardi, P.; Lopez, Y.; Giraldo, M.; Garcia, N.; Rodriguez, L.; Ramirez, L.; Uribe, O.; Garcia, L.; Vasquez, G. Cytokine gene polymorphisms in Colombian patients with systemic lupus erythematosus. Tissue Antigens 2007, 70, 376–382. [Google Scholar] [CrossRef]
  32. Antonicelli, R.; Olivieri, F.; Cavallone, L.; Spazzafumo, L.; Bonaf, M.; Marchegiani, F.; Cardelli, M.; Galeazzi, R.; Giovagnetti, S.; Perna, G.P.; et al. Tumor necrosis factor-alpha gene-308G>A polymorphism is associated with ST-elevation myocardial infarction and with high plasma levels of biochemical ischemia markers. Coron. Artery Dis. 2005, 16, 489–493. [Google Scholar] [CrossRef] [PubMed]
  33. Hua, X.P.; Qian, J.; Cao, C.B.; Xie, J.; Zeng, X.T.; Zhang, Z.J. Association between TNF-α rs1800629 polymorphism and the risk of myocardial infarction: A meta-analysis. Genet. Mol. Res. 2016, 13, gmr.15037292. [Google Scholar] [CrossRef]
  34. Huang, R.; Zhao, S.R.; Li, Y.; Liu, F.; Gong, Y.; Xing, J.; Xu, Z.S. Association of tumor necrosis factor-α gene polymorphisms and coronary artery disease susceptibility: A systematic review and meta-analysis. BMC Med. Genet. 2020, 21, 29. [Google Scholar] [CrossRef]
  35. Behboudi, A.; Thelander, T.; Yazici, D.; Celik, Y.; Yucel-Lindberg, T.; Thunström, E.; Peker, Y. Association of TNF-α (-308G/A) Gene Polymorphism with Circulating TNF-α Levels and Excessive Daytime Sleepiness in Adults with Coronary Artery Disease and Concomitant Obstructive Sleep Apnea. J. Clin. Med. 2021, 10, 3413. [Google Scholar] [CrossRef]
  36. Peker, Y.; Glantz, H.; Eulenburg, C.; Wegscheider, K.; Herlitz, J.; Thunstrom, E. Effect of Positive Airway Pressure on Cardiovascular Outcomes in Coronary Artery Disease Patients with Nonsleepy Obstructive Sleep Apnea. The RICCADSA Randomized Controlled Trial. Am. J. Respir. Crit. Care Med. 2016, 194, 613–620. [Google Scholar] [CrossRef]
  37. American Academy of Sleep Medicine Task Force. 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] [CrossRef]
  38. Johns, M.W. A new method for measuring daytime sleepiness: The Epworth sleepiness scale. Sleep 1991, 14, 540–545. [Google Scholar] [CrossRef] [PubMed]
  39. World Health Organ. Obesity: Preventing and Managing the Global Epidemic. Report of a WHO Consultation; World Health Organ: Geneva, Switzerland, 2000; Volume 894, pp. i–xii.
  40. Thunström, E.; Glantz, H.; Fu, M.; Yucel-Lindberg, T.; Petzold, M.; Lindberg, K.; Peker, Y. Increased inflammatory activity in nonobese patients with coronary artery disease and obstructive sleep apnea. Sleep 2015, 38, 463–471. [Google Scholar] [CrossRef] [PubMed]
  41. Lederer, D.J.; Bell, S.C.; Branson, R.; Chalmers, J.D.; Marshall, R.; Maslove, D.M.; Ost, D.E.; Punjabi, N.M.; Schatz, M.; Smyth, A.R.; et al. Control of Confounding and Reporting of Results in Causal Inference Studies. Guidance for Authors from Editors of Respiratory, Sleep, and Critical Care Journals. Ann. Am. Thorac. Soc. 2019, 16, 22–28. [Google Scholar] [CrossRef]
  42. Karkucak, M.; Ursavaş, A.; Ocakoğlu, G.; Görükmez, O.; Yakut, T.; Ercan, I.; Karadağ, M. Analysis of TNF-alpha G308A and C857T Gene Polymorphisms in Turkish Patients with Obstructive Sleep Apnea Syndrome. Turk. Klin. J. Med. Sci. 2012, 32, 1368–1373. [Google Scholar] [CrossRef]
  43. Huang, J.; Liao, N.; Huang, Q.P.; Xie, Z.F. Association between tumor necrosis factor-α-308G/A polymorphism and obstructive sleep apnea: A meta-analysis. Genet. Test Mol. Biomark. 2012, 16, 246–251. [Google Scholar] [CrossRef]
  44. Wu, Y.; Cao, C.; Wu, Y.; Zhang, C.; Zhu, C.; Ying, S.; Li, W. TNF-α-308G/A Polymorphism Contributes to Obstructive Sleep Apnea Syndrome Risk: Evidence Based on 10 Case-Control Studies. PLoS ONE 2014, 9, e106183. [Google Scholar] [CrossRef]
  45. Kazemi, E.; Jamialahmadi, K.; Avan, A.; Mirhafez, S.R.; Mohiti, J.; Pirhoushiaran, M.; Hosseini, N.; Mohammadi, A.; Ferns, G.A.; Pasdar, A.; et al. Association of tumor necrosis factor-α-308G/A gene polymorphism with coronary artery diseases: An evidence-based study. J. Clin. Lan. Anal. 2018, 32, e22153. [Google Scholar] [CrossRef]
  46. Nejati, P.; Naeimipour, S.; Salehi, A.; Shahbazi, M. Association of tumor necrosis factor-alpha gene promoter polymorphism and its mRNA expression level in coronary artery disease. Meta Gene 2018, 18, 122–126. [Google Scholar] [CrossRef]
  47. Yuepeng, J.; Zhao, X.; Zhao, Y.; Li, L. Gene polymorphism associated with TNF-α (G308A) IL-6 (C174G) and susceptibility to coronary atherosclerotic heart disease: A meta-analysis. Medicine 2019, 98, e13813. [Google Scholar] [CrossRef] [PubMed]
  48. Li, Q.; Zheng, X. Tumor necrosis factor alpha is a promising circulating biomarker for the development of obstructive sleep apnea syndrome: A meta-analysis. Oncotarget 2017, 8, 27616–27626. [Google Scholar] [CrossRef] [PubMed]
  49. Thunström, E.; Glantz, H.; Yucel-Lindberg, T.; Lindberg, K.; Saygin, M.; Peker, Y. CPAP Does Not Reduce Inflammatory Biomarkers in Patients with Coronary Artery Disease and Nonsleepy Obstructive Sleep Apnea: A Randomized Controlled Trial. Sleep 2017, 40, zsx157. [Google Scholar] [CrossRef]
  50. Kroeger, K.M.; Carville, K.S.; Abraham, L.J. The-308 tumor necrosis factor-alpha promoter polymorphism effects transcription. Mol. Immunol. 1997, 34, 391–399. [Google Scholar] [CrossRef]
Jcm 12 05325 g001
Figure 1. Analytic sample of the study population. Abbreviations: AHI, apneahypopnea index; CPAP, continuous positive airway pressure; ESS, Epworth Sleepiness Scale; OSA, obstructive sleep apnea; RICCADSA, Randomized Intervention with CPAP in Coronary Artery Disease and Sleep Apnea; TNF, tumor necrosis factor.
Figure 1. Analytic sample of the study population. Abbreviations: AHI, apneahypopnea index; CPAP, continuous positive airway pressure; ESS, Epworth Sleepiness Scale; OSA, obstructive sleep apnea; RICCADSA, Randomized Intervention with CPAP in Coronary Artery Disease and Sleep Apnea; TNF, tumor necrosis factor.
Jcm 12 05325 g001
Jcm 12 05325 g002
Figure 2. (A) Genotype frequency of TNF-α-308G/A promoter polymorphism and (B) allele frequency of TNF-α-308G/A promoter polymorphism in OSA patients allocated to CPAP vs. no-CPAP groups.
Figure 2. (A) Genotype frequency of TNF-α-308G/A promoter polymorphism and (B) allele frequency of TNF-α-308G/A promoter polymorphism in OSA patients allocated to CPAP vs. no-CPAP groups.
Jcm 12 05325 g002
Jcm 12 05325 g003
Figure 3. (A) Genotype frequency of TNF-α-308G/A promoter polymorphism and (B) allele frequency of TNF-α-308G/A promoter polymorphism in OSA patients stratified by CPAP usage.
Figure 3. (A) Genotype frequency of TNF-α-308G/A promoter polymorphism and (B) allele frequency of TNF-α-308G/A promoter polymorphism in OSA patients stratified by CPAP usage.
Jcm 12 05325 g003
Jcm 12 05325 g004
Figure 4. Circulating TNF-α levels at baseline and after 12 months of CPAP therapy in OSA patients stratified by CPAP usage categories.
Figure 4. Circulating TNF-α levels at baseline and after 12 months of CPAP therapy in OSA patients stratified by CPAP usage categories.
Jcm 12 05325 g004
Table 1. Baseline demographic and clinical characteristics of the OSA patients allocated to CPAP vs. no-CPAP groups.
Table 1. Baseline demographic and clinical characteristics of the OSA patients allocated to CPAP vs. no-CPAP groups.
OSA on CPAP
n = 169		OSA no-CPAP
n = 70		p-Value
Age, yrs64.1 (59.8–69.3)67.4 (62.7–72.4)0.019
Male sex, %86.485.70.890
BMI, kg/m228.3 (25.9–31.1)(28.7 (26.2–30.0)0.548
Obesity, %32.024.30.238
WHR0.96 (0.93–1.00)0.96 (0.91–0.99)0.153
Abdominal obesity, %93.295.70.470
Current smoking, %16.015.70.960
ESS score10.0 (6.0–12.0)6.0 (4.0–7.0)<0.001
EDS (ESS score ≥ 10), %56.80.0<0.001
AHI, events/h27.7 (18.7–39.2)22.9 (17.8–35.7)0.128
ODI, events/h15.7 (9.4–24.8)12.6 (7.2–22.9)0.101
Hypertension60.954.30.341
AMI at baseline54.444.30.153
Lung disease, %5.34.30.738
Diabetes, %26.012.90.026
Stroke, %4.810.10.121
Plasma TNF-α (pg/mL)4.87 (3.43–6.99)5.15 (3.92–6.54)0.856
Continuous data are presented as median and 25–75% quartiles. Categorical data are presented as percentages. Abbreviations: AHI = Apnea–Hypopnea Index; BMI = Body Mass Index; EDS = Excessive Daytime Sleepiness (ESS score ≥ 10); ESS = Epworth Sleepiness Scale; TNF-α = tumor necrosis factor-alpha; ODI = Oxygen Desaturation Index; OSA = Obstructive Sleep Apnea; WHR = Waist–Hip Ratio.
Table 2. Baseline demographic and clinical characteristics of the OSA patients stratified by CPAP usage.
Table 2. Baseline demographic and clinical characteristics of the OSA patients stratified by CPAP usage.
CPAP ≥ 4 h/Night
(Adherent)
n = 86		CPAP < 4 h/Night or
no CPAP
n = 153		p-Value
Age, yrs64.4 (60.1–70.6)65.2 (59.9–70.6)0.602
Male sex, %84.986.90.660
BMI, kg/m228.2 (25.7–31.1)28.7 (26.2–30.2)0.875
Obesity, %32.628.10.470
WHR0.96 (0.92–1.02)0.97 (0.93–1.01)0.609
Abdominal obesity, %92.894.60.586
Current smoking, %12.817.60.324
ESS score10.0 (6.0–11.0)7.0 (4.0–10.0)0.007
EDS (ESS score ≥ 10), %53.532.70.002
AHI, events/h28.2 (18.3–40.1)25.3 (18.6–36.2)0.293
ODI, events/h17.2 (10.1–25.7)14.2 (7.9–23.1)0.052
Hypertension60.558.20.729
AMI at baseline54.749.70.460
Lung disease, %5.34.30.738
Diabetes, %27.919.00.110
Stroke, %5.84.60.674
Plasma TNF-α (pg/mL)4.87 (3.48–7.53)5.24 (3.59–6.85)0.631
Continuous data are presented as median and 25–75% quartiles. Categorical data are presented as percentages. Abbreviations: AHI = Apnea–Hypopnea Index; BMI = Body Mass Index; EDS= Excessive Daytime Sleepiness (ESS score ≥ 10); ESS = Epworth Sleepiness Scale; TNF-α = tumor necrosis factor-alpha; ODI = Oxygen Desaturation Index; OSA = Obstructive Sleep Apnea; WHR = Waist–Hip Ratio.
Table 3. Regression analyses of the association of the TNF-α genotypes with change in circulating TNF-α levels from baseline, adjusted for the confounding variables in CAD patients with OSA (entire cohort and subgroups based on the CPAP use).
Table 3. Regression analyses of the association of the TNF-α genotypes with change in circulating TNF-α levels from baseline, adjusted for the confounding variables in CAD patients with OSA (entire cohort and subgroups based on the CPAP use).
Standardized 95% Confidence Interval forp-Values
Coefficients βLower BoundUpper Bound
Entire CohortGenotypes *−0.129−1.82−0.120.025
Age0.018−0.050.070.762
Male sex−0.035−1.911.000.540
BMI−0.005−0.150.140.942
AHI−0.035−0.040.020.573
ESS−0.115−0.280.000.056
Diabetes0.017−1.131.530.768
CPAP use
≥4 h/night		Genotypes *−2.979−6.11−1.210.004
Age−0.057−0.250.150.607
Male sex0.128−1.616.210.246
BMI0.029−0.290.380.803
AHI−0.183−0.160.010.096
ESS−0.078−0.460.220.481
Diabetes0.006−3.033.210.955
CPAP use
<4 h/night or
no-CPAP		Genotypes *−0.019−0.930.730.816
Age0.026−0.580.790.765
Male sex−0.079−2.360.840.349
BMI−0.078−0.230.090.368
AHI0.124−0.010.070.149
ESS−0.055−0.190.100.507
Diabetes−0.055−1.820.910.509
Abbreviations: AHI = Apnea–Hypopnea Index; BMI = Body Mass Index; CAD = Coronary Artery Disease; CPAP = Continuous Positive Airway Pressure; ESS = Epworth Sleepiness Scale; OSA = Obstructive Sleep Apnea. * GG = 0, GA = 1, AA = 2.
Table 4. Regression analyses of the association of the TNF-α A allele with change in circulating TNF-α levels from baseline, adjusted for the confounding variables in CAD patients with concomitant OSA.
Table 4. Regression analyses of the association of the TNF-α A allele with change in circulating TNF-α levels from baseline, adjusted for the confounding variables in CAD patients with concomitant OSA.
Standardized 95% Confidence Interval forp-Values
Coefficients βLower BoundUpper Bound
Entire CohortTNF-α A Allele−0.098−2.08−0.080.034
Age0.020−0.040.070.676
Male sex0.027−0.881.600.568
BMI−0.009−0.130.110.858
AHI−0.060−0.050.100.202
CPAP h/night0.011−0.140.180.827
ESS−0.061−0.190.410.208
Diabetes−0.017−1.240.850.714
CPAP use
≥4 h/night		TNF-α A Allele−0.212−5.66−1.010.005
Age−0.038−0.180.110.627
Male sex0.102−0.904.580.187
BMI0.018−0.210.260.823
AHI−0.189−0.14−0.020.015
ESS−0.061−0.340.150.434
Diabetes0.012−2.032.380.876
CPAP use
<4 h/night or
no-CPAP		TNF-α A Allele−0.014−0.960.750.805
Age0.025−0.040.060.670
Male sex−0.079−1.870.350.180
BMI−0.078−0.180.040.197
AHI0.123−0.010.060.039
ESS−0.055−0.150.050.347
Diabetes−0.055−1.400.490.346
Abbreviations: AHI = Apnea–Hypopnea Index; BMI = Body Mass Index; CAD = Coronary Artery Disease; CPAP = Continuous Positive Airway Pressure; ESS = Epworth Sleepiness Scale; OSA = Obstructive Sleep Apnea.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Celik, Y.; Peker, Y.; Yucel-Lindberg, T.; Thelander, T.; Behboudi, A. Association of TNF-α (-308G/A) Gene Polymorphism with Changes in Circulating TNF-α Levels in Response to CPAP Treatment in Adults with Coronary Artery Disease and Obstructive Sleep Apnea. J. Clin. Med. 2023, 12, 5325. https://doi.org/10.3390/jcm12165325

AMA Style

Celik Y, Peker Y, Yucel-Lindberg T, Thelander T, Behboudi A. Association of TNF-α (-308G/A) Gene Polymorphism with Changes in Circulating TNF-α Levels in Response to CPAP Treatment in Adults with Coronary Artery Disease and Obstructive Sleep Apnea. Journal of Clinical Medicine. 2023; 12(16):5325. https://doi.org/10.3390/jcm12165325

Chicago/Turabian Style

Celik, Yeliz, Yüksel Peker, Tülay Yucel-Lindberg, Tilia Thelander, and Afrouz Behboudi. 2023. "Association of TNF-α (-308G/A) Gene Polymorphism with Changes in Circulating TNF-α Levels in Response to CPAP Treatment in Adults with Coronary Artery Disease and Obstructive Sleep Apnea" Journal of Clinical Medicine 12, no. 16: 5325. https://doi.org/10.3390/jcm12165325

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop