Evaluation of Serum/Plasma Levels of Interleukins (IL-6,IL-12,IL-17,IL-18,and IL-23) in Adults and Children with Obstructive Sleep Apnea: A Systematic Review,Meta-Analysis,and Trial Sequential Analysis

Abstract

Obstructive sleep apnea (OSA) is a chronic inflammatory disease characterized by partial or complete upper airway obstruction during sleep. We aimed to evaluate serum/plasma levels of several cytokines (interleukin [IL]-6, IL-12, IL-17, IL-18, and IL-23) in a systematic review meta-analysis in both adults and children with OSA compared with controls. We conducted a comprehensive search of 4 digital databases (PubMed, Web of Science, Scopus, and Cochrane Library) up until October 19, 2023, without any limitations. For our meta-analysis, we used Review Manager, version 5.3, and displayed the data as the standardized mean difference (SMD) and 95% confidence interval (CI) to assess the correlation between cytokine levels and OSA. We utilized Comprehensive Meta-Analysis version 3.0 software to conduct bias analyses, meta-regression, and sensitivity analyses. From 1881 records, 84 articles were included in the systematic review and meta-analysis. In adults, the pooled SMDs for IL-6 level were 0.79 (P value < 0.00001), for IL-17 level were 0.74 (P value = 0.14), and for IL-18 level were 0.43 (P value = 0.00002). In children, the pooled SMD for IL-6 was 1.10 (P value < 0.00001), for IL-12 was 0.47 (P value = 0.10), for IL-17 was 2.21 (a P value = 0.24), for IL-18 was 0.19 (P value = 0.07), and for IL-23 was 2.46 (P value < 0.0001). The subgroup analysis showed that the ethnicity, mean body mass index, and mean apnea–hypopnea index for IL-6 levels in adults and the ethnicity for IL-6 levels in children were effective factors in the pooled SMD. The findings of the trial sequential analysis revealed that adequate evidence has been obtained. The analysis of IL levels in adults and children with OSA compared with those without OSA revealed significant differences. In adults, IL-6 and IL-18 levels were significantly higher in the OSA group, while in children, only IL-6 and IL-23 levels were significantly elevated.

Introduction

Obstructive sleep apnea (OSA) is characterized by recurrent episodes of partial and complete airway obstructions during sleep with repetitive apneas and hypopneas as a result (Abbasi et al., 2021). The prevalence of OSA in the general population ranged from 9% to 38% and was higher in men. It increased with increasing age, and, in some elderly groups, it was as high as 90% in men and 78% in women (Senaratna et al., 2017). Studies published before 2014 reported a 3.3%–9.4% prevalence, while more recent studies published during 2016–2023 report a higher prevalence, 12.8%–20.4% among children in the general population (Magnusdottir and Hill, 2024). Polysomnography, despite limited availability and high cost, is currently recommended for diagnosis of OSA (Mulgrew et al., 2007). The main metric for diagnosing OSA is the apnea–hypopnea index (AHI) that AHI is a crucial metric derived from polysomnography (Senaratna et al., 2017). OSA is defined broadly as an AHI 5 events/h of sleep in adults (Peppard et al., 2013, Young et al., 2009) and an AHI >1 event/h of sleep in children (Hirsch et al., 2019, Jie et al., 2007).

Obesity plays a significant role in causing sleep-disordered breathing. Given the ongoing obesity epidemic, previous estimates of OSA prevalence need updating (Peppard et al., 2013). As obesity rates continue to rise, so does the incidence of OSA (Yaggi and Strohl, 2010). OSA is highly prevalent in women (Geer and Hilbert, 2021). In addition, as an individual’s age increases, OSA becomes more prevalent (Geer and Hilbert, 2021, Yaggi and Strohl, 2010). Most population-based studies on OSA prevalence have primarily focused on characterizing the disease’s occurrence in North America, Australia, and Europe (Yaggi and Strohl, 2010). Importantly, OSA is a substantial risk factor for major cardiovascular diseases, including arterial hypertension, rhythm/conduction disturbances, diabetes mellitus, heart diseases, and cerebral stroke (Shamsuzzaman et al., 2003, Shaw et al., 2008, Tietjens et al., 2019, Urbanik et al., 2020).

The association between OSA and systemic inflammation is well-established and has been the subject of a multitude of diverse studies over the past 2 decades. These studies aim to decipher the causal relationships between OSA and inflammatory pathways, as well as to identify potential biomarkers indicative of OSA or its associated health conditions (Horvath et al., 2018; Perrini et al., 2017; Bouloukaki et al., 2017; Chen et al., 2017). Cytokines, small proteins secreted by nearly every cell, play a pivotal role in regulating and influencing the immune response (Takeuchi and Akira, 2010). Recent research suggests that an immune response necessitates a simultaneous release of both pro-inflammatory and anti-inflammatory cytokines (Geginat et al., 2016). Despite their significance, the nomenclature of cytokines is somewhat inconsistent; they are referred to as interleukins (ILs), chemokines, or growth factors, among other terms (Charo and Ransohoff, 2006). While studies have reported a link between inflammatory factors and OSA (Imani et al., 2022; Imani et al., 2021; Imani et al., 2020), the underlying mechanisms remain elusive.

A meta-analysis found that serum/plasma levels of IL-12 and IL-18 were reported in both adults and children with OSA compared with controls (Janmohammadi et al., 2023). Another meta-analysis focused on serum/plasma levels of IL-6 in adults and children with OSA compared with controls (Imani et al., 2020). In addition, a separate meta-analysis examined serum levels of IL-6 specifically in adults with OSA compared with controls (Zhong et al., 2016). Another study investigated serum levels of IL-6 in adults with OSA compared with controls (Nadeem et al., 2013). We aimed to evaluate serum/plasma levels of several cytokines (IL-6, IL-12, IL-17, IL-18, and IL-23) in a systematic review meta-analysis with more studies for reported cytokines in previous meta-analysis and adding new cytokines such as IL-17 and IL-23 in both adults and children with OSA compared with controls. In addition, we added trial sequential analysis (TSA) to increase the strength of the results.

Materials and Methods

Literature search

We conducted a comprehensive search of 4 digital databases (PubMed, Web of Science, Scopus, and Cochrane Library) up until October 19, 2023, without any limitations. The keywords used for the search included various terms related to “OSA” such as “sleep apnea,” “obstructive sleep apnea,” “OSAS,” “obstructive sleep apnea syndrome,” “obstructive sleep apnea/hypopnea syndrome,” and “OSAHS.” We also used terms related to interleukins like “interleukin*,” “interleukin-6,” “IL-6,” “IL6,” “interleukin-12,” “IL-12,” “IL12,” “interleukin-17,” “IL-17,” “IL17,” “interleukin-18,” “IL-18,” “IL18,” “interleukin-23,” “IL-23,” and “IL23.” In addition, we included terms related to blood components such as “circulating,” “plasma,” “serum,” and “blood.” To ensure a thorough search, we also manually reviewed the references of the studies that met our criteria and searched Google Scholar for any potentially relevant publications.

Study selection

The PICOS framework was utilized to set the inclusion criteria as follows: Population (P) consisted of both children and adults diagnosed with OSA. The Intervention (I) involved the measurement of IL-6, IL-12, IL-17, IL-18, and IL-23 levels. The Comparison (C) was made between these levels and a control group of both children and adults without OSA. The Outcome (O) was the correlation of IL-6, IL-12, IL-17, IL-18, and IL-23 levels with OSA. The Study Design (S) was observational studies. The diagnosis of OSA patients was based on the clinical practice guideline from the American College of Physicians for adults (Epstein et al., 2009) and children (Section on Pediatric Pulmonology, 2002). The cytokine levels were determined using picograms per milliliter or converted to the corresponding unit and were measured in the morning.

Eligibility criteria

We excluded studies that met the following criteria: those that involved participants with a history or diagnosis of any systemic diseases that could overlap with OSA, such as other respiratory, cardiovascular, and endocrine diseases; patients who were receiving nutritional support or were undergoing treatment, such as medication, surgery, or continuous positive airway pressure; case reports or articles that lacked statistical data; and articles that did not include a control group. We also excluded articles that selected an adult control group with an AHI of >5 events/h or a child control group with an AHI of >1 event/h. Reviews, meta-analyses, letters to the editors, and book chapters were also excluded. In addition, we did not consider articles that did not provide any data; articles that measured the level of cytokines in saliva, urine, exhaled breath condensate, and cells; and articles that measured the overnight or evening levels of cytokines.

Data extraction

The literature was screened, and data were extracted by 2 authors (M.S. and E.S.) to maintain consistency in the screening core criteria and data collection. In case of differing opinions, they engaged in further discussions or invited an additional person (A.G.) to join the discussions until they reached a consensus. A data extraction form was designed, and the following information was collected from the eligible articles: the first author’s name, year of publication, nationality, sample size, gender, body mass index (BMI), age, AHI, type of blood sample, and cytokine levels. The quality of the studies included was assessed based on the Newcastle–Ottawa Scale (NOS) (Stang, 2010). The analyses were independently conducted by 2 authors, and all decisions were made by consensus.

Statistical analysis and data synthesis

For our meta-analysis, we used Review Manager, version 5.3, and displayed the data as the standardized mean difference (SMD) and 95% confidence interval (CI) to assess the correlation between cytokine levels and OSA. We used GetData Graph Digitizer software to depict mean ± SD on the graphs. In certain studies, the median (interquartile) or median (range) was reported, which we converted to mean ± standard deviation (SD). We transformed the standard error (SE) to SD by [ $S E = \frac{S D}{\sqrt{N}}, N = N u m b e r o f c a s e s$ ]. We extracted mean ± SD from Log (mean ± SD) in “https://brilliant.org/wiki/log-normal-distribution/.”

The diversity among the studies was evaluated using the I ² statistic, with the significance level established at P < 0.05. Considering the likely diversity of the studies, a random-effects model was used in the meta-analysis when the heterogeneity P value was <0.10 (I ² exceeding 50%) (DerSimonian and Laird, 2015). Otherwise, a fixed-effect model was used (Mantel and Haenszel, 1959). We assessed the presence of publication bias using a funnel plot and Begg’s and Egger’s tests, setting the significance level at P < 0.10. We utilized Comprehensive Meta-Analysis version 3.0 (CMA 3.0) software to conduct bias analyses, meta-regression, and sensitivity analyses.

We performed a trial sequential analysis (TSA) using the TSA software (version 0.9.5.10 beta) (Wetterslev et al., 2017). The required information size (RIS) for blood cytokine levels was determined with an alpha risk of 5% and a beta risk of 20%. The calculation of the mean difference was based on empirical assumptions. If the z-curve intersected the RIS, it indicated that the studies had a sufficient number of cases and the conclusion could be considered reliable.

Results

Study selection

Figure 1 shows a flowchart that outlines the process of selecting articles for the systematic review and meta-analysis. Records are identified through database searching (1876 records) from PubMed (417), Web of Science (527), Cochrane Library (103), and Scopus (829). In addition, 5 records are identified through other databases or electronic source searching. After removing duplicates, 1054 records are screened. Of these, 931 are excluded. A total of 123 full-text articles are assessed for eligibility, and 40 of these are excluded for various reasons. Finally, 83 articles (Abdel-Fadeil et al., 2017, Abulikemu et al., 2021, Ahsant et al., 2022, Akinnusi et al., 2013, Al-Terki et al., 2018, Arias et al., 2008, Bhatt et al., 2021, Bhatt et al., 2019, Bhatt et al., 2018, Bilal et al., 2021, Bozic et al., 2018, Bravo et al., 2007, Burioka et al., 2008, Carneiro et al., 2009, Ciftci et al., 2004, Constantinidis et al., 2008, De Santis et al., 2015, Dogan et al., 2016, Fiedorczuk et al., 2023, Fornadi et al., 2012, Gaines et al., 2016, Gaines et al., 2015, Galati et al., 2020, Gileles-Hillel et al., 2014, Goulet et al., 2023, Gozal et al., 2008, Hargens et al., 2013, Hirotsu et al., 2017, Hirsch et al., 2019, Huang et al., 2020, Huang et al., 2016, Hui et al., 2015, Huiguo et al., 2000, Imagawa et al., 2004, Ji et al., 2021, Ji et al., 2022, Jie et al., 2007, Kim et al., 2010, Ko et al., 2019, Kong et al., 2017, Kritikou et al., 2014, Kurt et al., 2013, Li et al., 2008a, Li et al., 2009, Li et al., 2022, Li et al., 2008b, Liu et al., 2011, Lu et al., 2018, Matos et al., 2013, Medeiros et al., 2012, Minoguchi et al., 2005, Motamedi et al., 2018, Nizam et al., 2016, Niżankowska-Jędrzejczyk et al., 2014, Nobili et al., 2015, Qian et al., 2012, Ryan et al., 2006, Sahlman et al., 2010, Sarinc Ulasli et al., 2015, Shu-wen et al., 2015, Smith et al., 2021, Tam et al., 2006, Tang et al., 2019, Thorn et al., 2017, Thunström et al., 2015, Tomiyama et al., 2008, Tosun et al., 2023, Toujani et al., 2017, Ünüvar Doğan et al., 2014, Vgontzas et al., 1997, Vgontzas et al., 2008, Vicente et al., 2016, Wali et al., 2021, Wang et al., 2023, Weingarten et al., 2017, Xie et al., 2020, Yamamoto et al., 2008, Yang et al., 2013, Yang and Somani, 2023, Ye et al., 2015, Ye et al., 2012, Ye et al., 2010, Yokoe et al., 2003, Zhang and Wang, 2017) are included in the systematic review and meta-analysis.

FIG. 1.

Flowchart of the study selection.

Characteristics of articles

Table 1 shows a summary of characteristics of studies examining the serum and plasma levels of IL-6, IL-12, IL-17, IL-18, and IL-23 in adults and children with and without OSA. The studies were published from 1997 to 2023. Sixty-nine articles reported the cytokine levels in adults and 15 in children. Four articles (Goulet et al., 2023, Jie et al., 2007, Shu-wen et al., 2015, Vgontzas et al., 1997) had no high-quality score (score ≥7).

Table 1.

Characteristics of the Articles

Adult
					CASE			CONTROL
First author, publication year	Country	Ethnicity	Case/Control No.	Factor	AHI, events/h	Age, years	BMI, kg/m²	AHI, events/h	Age, years	BMI, kg/m²	Sample	Quality score
Abdel-Fadeil et al. (2017)	Egypt	Arab	22/22	IL-6	32.17 ± 20.59	49.92 ± 9.85	36 ± 5.16	3.72 ± 1.69	47.55 ± 2.35	36.62 ± 1.14	Serum	7
Abulikemu et al. (2021)	China	Asian	67/67	IL-6	27.34 ± 4.87	47.51 ± 9.64	24.13 ± 2.97	4.31 ± 1.05	45.93 ± 10.01	23.94 ± 2.85	Serum	8
Ahsant et al. (2022)	Iran	Asian	46/42	IL-6	36.75 ± 22.19	59.38 ± 9.37	36.05 ± 6.75	<5	52.79 ± 5.76	33 ± 4.64	Serum	8
Akinnusi et al. (2013)	USA	Mixed	29/18	IL-6	32.2 ± 13.1	54.5 ± 8.9	31.1 ± 5.7	1.9 ± 1.6	52.3 ± 9.3	29.5 ± 5.1	Serum	8
Al-Terki et al. (2018)	USA	Mixed	52/22	IL-6	22.9 ± 17.9	40.3 ± 1.6	29.5 ± 0.7	2.5 ± 1.6	39.6 ± 2.2	28.5 ± 0.9	Plasma	8
Arias et al. (2008)	Spain	Caucasian	30/15	IL-6	43.8 ± 27.0	52.0 ± 13.0	30.5 ± 4.0	3.7 ± 3.3	48.0 ± 10.0	28.7 ± 4.7	Plasma	8
Bhatt et al. (2018)	India	Asian	47/124	IL-6	≥5	44.8 ± 9.1	32.5 ± 6. 9	<5	44.2 ± 9.1	33.3 ± 7.9	Serum	7
Bhatt et al. (2019)	India	Caucasian	47/25	IL-6	13.5 ± 6.4	44.2 ± 9.1	32.5 ± 6.9	2.3 ± 1.1	28.5 ± 8.6	41 ± 8.5	Serum	8
Bilal et al. (2021)	Turkey	Caucasian	30/30	IL-6	24.65 ± 5.74	44.30 ± 10.43	30.41 ± 6.15	2.62 ± 1.34	42.53 ± 9.81	29.1 ± 4.5	Serum	8
Bozic et al. (2018)	Croatia	Caucasian	50/25	IL-6	35.0 ± 11.0	53.0 ± 11.9	28.9 ± 2.7	<5	52.5 ± 10.2	27.8 ± 2.2	Plasma	8
Bravo et al. (2007)	USA	Mixed	50/20	IL-6	51.3 ± 4.2	51.7 ± 1.9	33.2 ± 1.7	2.5 ± 0.5	47.4 ± 1.2	28.4 ± 0.6	Serum	7
Burioka et al. (2008)	Japan	Asian	8/8	IL-6	53.9 ± 17.54	45.9 ± 12.16	25.9 ± 1.7	3.8 ± 1.7	43.1 ± 13.58	24.6 ± 17	Serum	8
Carneiro et al. (2009)	Brazil	Mixed	16/13	IL-6	65.7 ± 39.6	40.1 ± 9.6	46.9 ± 8.0	3.2 ± 1.8	38.8 ± 11.9	42.8 ± 4.69	Plasma	7
Ciftci et al. (2004)	Turkey	Caucasian	43/22	IL-6	33.2 ± 3.27	49.6 ± 9.1	31.9 ± 4.1	1.55 ± 4.5	47.2 ± 10.3	31.03 ± 3.1	Serum	8
Constantinidis et al. (2008)	Greece	Caucasian	24/27	IL-6	23.3 ± 3.6	45.1 ± 8.2	≥25	3.5 ± 0.4	45.1 ± 8.2	≥25	Serum	8
De Santis et al. (2015)	Italy	Caucasian	26/24	IL-6	26.15 ± 12.1	41.8 ± 7.4	33.0 ± 5.2	1.65 ± 0.9	43.7 ± 8.2	30.8 ± 4.3	Serum	8
Dogan et al. (2016)	Turkey	Caucasian	39/12	IL-6	≥5	40.7 ± 10.75	29.26 ± 4.12	<5	40.0 ± 11.7	27.3 ± 2.9	Serum	8
Fiedorczuk et al. (2023)	Poland	Caucasian	52/28	IL-6	28.87 ± 3.55	43.68 ± 12.10	28.87 ± 3.56	2.36 ± 1.69	40.12 ± 12.68	26.69 ± 2.92	Plasma & Serum	8
Fornadi et al. (2012)	Hungary	Caucasian	25/75	IL-6	≥5	54 ± 12	29 ± 5	<5	50 ± 13	26 ± 5	Serum	8
Gaines et al. (2015)	Greece	Caucasian	82/38	IL-6	22.6 ± 15.0	54.9 ± 5.8	29.2 ± 3.3	2.0 ± 15.0	54.2 ± 5.8	27.6 ± 3.7	Plasma	7
Galati et al. (2020)	Italy	Caucasian	45/30	IL-6	≥5	53.9 ± 11.6	28 ± 2.2	<5	55 ± 5.8	26.3 ± 1.8	Serum	8
Goulet et al. (2023)	Canada	Mixed	7/18	IL-18	≥5	54.4 ± 6.4	29.4 ± 6.6	<5	22.7 ± 3.1	25.4 ± 3.5	Plasma	6
Hargens et al. (2013)	USA	Mixed	12/18	IL-6	25.4 ± 5.4	22.8 ± 0.8	32.4 ± 1.0	2.1 ± 0.3	21.9 ± 0.6	29.3 ± 0.5	Serum	8
Hirotsu et al. (2017)	Brazil	Mixed	339/682	IL-6	19.3 ± 9.44	50.8 ± 13.2	29.6 ± 5.8	2.5 ± 10.4	38.2 ± 12.7	25.4 ± 3.8	Serum	8
Hui et al. (2015)	China	Asian	35/20	IL-6	≥5	47.6 ± 6.6	27.3 ± 4.4	<5	40.5 ± 4.9	25.1 ± 5.3	Serum	7
Huiguo et al. (2000)	China	Asian	20/16	IL-6	44.0 ± 21.0	47.4 ± 13.6	27.6 ± 3.3	4.29 ± 2.16	47.6 ± 14.7	23.1 ± 3	Plasma	8
Imagawa et al. (2004)	Japan	Asian	110/45	IL-6	≥5	NA	27.7 ± 4.4	<5	NA	22.9 ± 2.9	Serum	7
Ji et al. (2021)	China	Asian	67/21	IL-6	38.42 ± 11.60	44.15 ± 14.69	32.87 ± 8.47	2.69 ± 0.35	42.97 ± 1026	31.57 ± 8.85	Serum	8
Ji et al. (2022)	China	Asian	78/21	IL-6	29.46 ± 6.67	58.55 ± 10.46	25.30 ± 3.26	2.53 ± 1.38	56.1 ± 12.4	24.5 ± 3.1	Serum	8
Kim et al. (2010)	Korea	Asian	37/22	IL-6	43.39 ± 18.08	41.03 ± 11.75	17.66 ± 3.68	1.25 ± 1.25	26 ± 6.91	23.88 ± 2.30	Serum	7
Ko et al. (2019)	China	Asian	126/13	IL-6	36.09 ± 16.58	45.80 ± 13.02	27.42 ± 3.93	1.83 ± 1.34	35.92 ± 7.69	24.1 ± 2.33	Serum	7
Kong et al. (2017)	China	Asian	82/30	IL-6	≥5	45.6 ± 8.8	27.7 ± 4.6	<5	45.7 ± 6.6	27.6 ± 3.6	Serum	8
Kritikou et al. (2014)	Greece	Caucasian	38/39	IL-6	37.5 ± 21.0	55.7 ± 6.6	28.7 ± 3.3	2.3 ± 1.9	53.8 ± 6.2	27.3 ± 3.5	Plasma	7
Kurt et al. (2013)	Turkey	Caucasian	48/37	IL-6	≥5	48.3 ± 12.3	NA	<5	43.1 ± 14.1	NA	Serum	7
Li et al. (2008b)	China	Asian	28/22	IL-6	33.4 ±28.6	45.1 ± 10.2	27.7 ± 4.5	2.9 ± 1.3	43 ± 9	23.3 ±2.0	Serum	8
Li et al. (2009b)	China	Asian	68/22	IL-6	38.9 ± 26.5	45.3 ± 11.1	20.7 ± 10.1	2.9 ± 1.3	43.0 ± 93.0	23.3 ± 2.0	Serum	8
Li et al. (2022)	China	Asian	89/29	IL-6 IL-12 IL-17	18.8 ± 17.2	45.87 ± 5.17	29.05 ± 4.89	2.28 ± 0.85	45.38 ± 5.39	30.3 ± 5.3	Plasma	9
Liu et al. (2011)	China	Asian	78/20	IL-6	46.3 ± 9.2	45.2 ± 7.3	27.9 ± 3.1	<5	43.5 ± 8.3	26.1 ± 2.4	Serum	7
Lu et al. (2018)	China	Asian	35/22	IL-6	≥5	48.63 ± 1.80	28.21 ± 0.51	<5	49.04 ± 2.27	26.1 ± 0.65	Serum	7
Matos et al. (2013)	Brazil	Mixed	155/208	IL-6	≥5	51.2	29.7	<5	40.7	24.5	Plasma	7
Medeiros et al. (2012)	Brazil	Mixed	50/15	IL-6	≥5	64.29 ± 7.73	18.62 ± 4.01	<5	62.50 ± 8.4	25.81 ± 4.04	Serum	8
#66 Minoguchi et al. (2005)	Japan	Asian	36/16	IL-6 IL18	34.89 ± 17.46	48.22 ± 14.1	28.3 ± 4.44	3.3 ± 2.4	46.5 ± 15.2	28.3 ± 5.2	Serum	8
Motamedi et al. (2018)	USA	Mixed	50/24	IL-6	18.4 ± 13.1	34.9 ± 8.0	30.8 ± 4.2	2.12 ± 1.3	30.9 ± 7.77	28.6 ± 3.81	Plasma	7
Nizam et al. (2016)	Turkey	Caucasian	39/13	IL-6	45.6 ± 20.7	47.3 ± 10.4	33.2 ± 56.4	2.64 ± 1.82	43.23 ± 9.08	31.71 ± 4.56	Serum	8
Niżankowska-Jędrzejczyk et al. (2014)	Poland	Caucasian	22/16	IL-6	23.62 ± 12.32	52.50 ± 8.33	30.15 ± 2.77	1.90 ± 2.78	54.50 ± 8.33	30.15 ± 2.77	Plasma	9
Qian et al. (2012)	China	Asian	30/40 70/40	IL-6	≥5	45.8 ± 8.2	28.9 ± 2.3	<5	46.3 ± 8.1	24.1 ± 2.3	Serum	8
Ryan et al. (2006)	Ireland	Caucasian	66/30	IL-6 IL-12	35.0 ± 13.9	42.5 ± 8.5	32.5 ± 4.8	1.2 ± 1.0	41 ± 8	30.7 ± 3.1	Serum	8
Sahlman et al. (2010)	Finland	Caucasian	84/40	IL-6	9.6 ± 2.9	50.4 ± 9.3	32.5 ± 3.3	1.9 ± 1.4	45.6 ± 11.5	31.5 ± 3.5	Plasma	8
Sarinc Ulasli et al. (2015)	Turkey	Caucasian	28/20	IL-6 IL-12	30.58 ± 18.39	51.7 ± 10.2	32.4 ± 5.6	2.24 ± 0.99	45.3 ± 14	30.4 ± 8	Serum	8
Shu-wen et al. (2015)	China	Asian	66/25	IL-18	≥5	43.56 ± 7.22	24.94 ± 2.06	<5	NA	NA	Serum	6
Tang et al. (2019)	China	Asian	120/127	IL-6 IL-18	39.00 ± 18.38	48.88 ± 9.76	26.86 ± 3.12	3.31±1.09	47.37 ± 9.12	22.5 ± 3.3	Serum	8
Thorn et al. (2017)	UK	Caucasian	16/14	IL-6	30 ±18	59 ±13	32.7 ± 4.0	0 ± 0	58 ±7	30.6 ± 2.7	Serum	8
Thunström et al. (2015)	Sweden	Caucasian	234/95	IL-6	28.9 ± 13.7	65.3 ± 7.1	26.8 ± 2.1	3.1 ± 1.3	61.4 ± 9.5	25.2 ± 2.5	Serum	9
Tomiyama et al. (2008)	Japan	Asian	10,12,28/15	IL-6	42.7 ± 27.9	51.4 ± 13.0	26.9 ± 4.2	<5	53.0 ± 10.0	24.3 ± 2.5	Plasma	8
Tosun et al. (2023)	Turkey	Caucasian	67/25	IL-6	39.06 ± 15.39	49.24 ± 10.12	32.47 ± 4.80	2.7 ± 1.4	40.4 ± 13	28.4 ± 4.6	Serum	7
Toujani et al. (2017)	Tunisia	Arab	92/30	IL-17	55.7 ± 17.8	52.3 ± 12.7	36.2 ± 6	0 ± 0	45.7 ± 14.7	32 ± 4.2	Serum	9
Ünüvar Doğan et al. (2014)	Turkey	Caucasian	33/24	IL-6	47.2 ± 23.2	45.3 ± 8.5	31.0 ± 1.7	3.6 ± 1.8	40.5 ± 9.5	30.7 ± 1.5	Serum	8
Vgontzas et al. (1997)	USA	Mixed	12/10	IL-6	63.7 ± 10.3	40.9 ± 2.2	40.5 ± 3.2	0 ± 0	24.1 ± 0.8	24.6 ± 0.7	Plasma	6
Vgontzas et al. (2008)	Greece	Caucasian	16/28	IL-6	53.3 ± 28	48.1 ± 5.6	37.5 ± 24.8	<5	48.1 ± 10.6	30.8 ± 5.2	Plasma	7
Vicente et al. (2016)	Spain	Caucasian	89/26	IL-6	28 ± 23.7	45.33 ± 14.81	30.03 ± 5.04	1.9 ± 2.7	45 ± 11.11	28.7 ± 4.37	Plasma	8
Wali et al. (2021)	Saudi Arabia	Arab	40/24	IL-6	36.74 ± 23.60	47.5 ± 13.18	37.5 ± 11.4	2.9 ± 2	31.7 ± 11.7	30 ± 8.6	Serum	7
Weingarten et al. (2017)	USA	Mixed	11/8	IL-6	25.1 ± 17.8	38.7 ± 10.5	47.3 ± 14.8	1.8 ± 1.0	33.6 ± 5.1	25.5 ± 10.9	Serum	7
Xie et al. (2020)	China	Asian	107/34	IL-6	40.49 ± 12.69	48.22 ± 17.25	27.85 ± 3.07	2.23 ± 1.49	34.74 ± 14.02	23.80 ± 4	Serum	7
Yamamoto et al. (2008)	Japan	Asian	31/10	IL-6	≥5	49.0 ± 2.0	28.0 ± 1.0	<5	46.0 ± 6.0	27.0 ± 2.0	Serum	8
Yang et al. (2013)	China	Asian	25/25	IL-6	24 ± 17	54 ± 7	27.39 ± 2.91	3 ± 1	53 ± 7	26.22 ± 1.9	Plasma	8
Ye et al. (2010)	China	Asian	127/52	IL-6	36.5 ± 25.7	45.3 ± 11.4	26.2 ± 3.7	2.0 ± 1.4	45.0 ± 10.0	26.0 ± 3.2	Serum	8
Ye et al. (2012)	China	Asian	44/20	IL-6 IL-17	40.4 ± 20.68	46.43 ± 18.22	28.2 ± 5.34	2.26 ± 1.30	45.8 ± 23.01	26.9 ± 4.25	Plasma	8
Yokoe et al. (2003)	Japan	Asian	30/14	IL-6	36.99 ± 9.97	51.82 ± 11.45	28.84 ± 4.22	2.8 ± 0.75	48.8 ± 11.22	27.6 ± 11.7	Serum	7
Children
Bhatt et al. (2021)	India	Asian	190/57	IL-6 IL-17 IL-18 IL-23	≥1	10.71 ± 3.00	27.4 ± 4.88	<1	10.71 ± 3.00	27.1 ± 6.53	Serum	9
Gaines et al. (2016)	USA	Mixed	153/239	IL-6	13.78 ± 4.73	17.71 ± 2.19	NA	0.89 ± 4.77	16.37 ± 2.15	NA	Plasma	7
Gileles-Hillel et al. (2014)	Spain	Caucasian	75/129	IL-6 IL-18	14.2 ± 9.0	10.4 ± 2.8	28.0 ± 4.6	0.6 ± 0.6	11.0 ± 2.4	27.9 ± 4.1	Serum	8
Gozal et al. (2008)	USA	Mixed	20/20	IL-6	13.3 ± 2.8	6.5 ± 2.68	17.3 ± 2.68	0.0 ± 0.0	6.4 ± 3.13	17.1 ± 2.24	Plasma	9
Hirsch et al. (2019)	Australia	Mixed	23/15	IL-6	≥1	10.0 ± 1.7	NA	<1	10.7 ± 1.2	NA	Serum	7
Huang et al. (2016)	Taiwan	Asian	47/32	IL-6 IL-17 IL-23	9.13 ± 1.67	7.84 ± 0.56	16.95 ± 0.47	0.41 ± 0.07	7.02 ± 0.65	6.55 ± 0.58	Plasma	7
Huang et al. (2020)	Taiwan	Asian	55/32	IL-6 IL-12 IL-17 IL-23	15.71 ± 22.6	7.67 ± 2.64	16.83 ± 4.03	0.46 ± 0.28	7.02 ± 0.65	17.44 ± 3.08	Plasma	9
Jie et al. (2007)	China	Asian	100/40	IL-6	≥1	4.67 ± NA	NA	<1	NA	NA	Serum	6
Li et al. (2008a)	China	Asian	47/95	IL-6	14.1 ± 8.0	11.1 ± 1.27	NA	0.7 ± 0.6	10.7 ± 1.3	NA	Serum	7
Nobili et al. (2015)	Italy	Caucasian	52/28	IL-6	4.99 ± 3.07	11.3 ± 2.1	28.3 ± 4.9	0.58 ± 0.30	11.7 ± 1.9	26.4 ± 5.9	Serum	9
Smith et al. (2021)	USA	Mixed	43/53	IL-6	10.3 ± 9.1	9.0 ± 2.6	20.4 ± 5.3	0.8 ± 1.4	10.0 ± 2.3	20.1 ± 4.8	Serum	8
Tam et al. (2006)	Australia	Mixed	44/69	IL-6 IL-12	5.3 ± 6.5	7.3 ± 3.7	19.4 ± 5.5	0 ± 0	5.3 ± 6.5	7.6 ± 4.0	Serum	9
Wang et al. (2023)	China	Asian	83/83	IL-6	7.9 ± 8.4	7 ± 2.7	17.1 ± 3.3	0.0 ± 0.0	6.8 ± 3.5	16.9 ± 3.4	Serum	9
Ye et al. (2015)	China	Asian	25/19	IL-17	34.76 ± 15.28	6.45 ± 2.84	NA	0.68 ± 0.20	6.63 ± 2.71	NA	Serum	7
Zhang and Wang (2017)	China	Asian	50/52	IL-6	≥1	6.6	NA	<1	6.4	NA	Serum	7

NA, not available; IL, interleukin; BMI: body mass index; AHI, apnea–hypopnea index.

Pooled analyses in adults

The forest plot analysis of IL-6 levels in adults with OSA (case) compared with those without OSA (control) is shown in Figure 2. The pooled SMD was 0.79 (95%CI: 0.50, 1.07) with a P value < 0.00001 and I ² = 96%. The result showed that levels of IL-6 in the case group were significantly higher than in the control group.

FIG. 2.

Forest plot analysis of interleukin (IL)-6 levels in adults with obstructive sleep apnea (OSA) (case) compared with those without OSA (control).

The forest plot analysis of IL-17 and IL-18 levels in the case group compared with the control group is shown in Figure 3. The pooled SMD for IL-17 level was 0.74 (95%CI:−0.24, 1.73) with a P value = 0.14 and I ² = 92% and for IL-18 level was 0.43 (95%CI: 0.20, 0.65) with a P value = 0.00002 and I ² = 0%. The result showed that just levels of IL-18 in adults with OSA were significantly higher than in adults without OSA.

FIG. 3.

Forest plot analysis of IL-17 and IL-18 levels in adults with OSA (case) compared with those without OSA (control).

Pooled analyses in children

The forest plot analysis of IL-6 levels in children with OSA (case) compared with those without OSA (control) is shown in Figure 4. The pooled SMD was 1.10 (95%CI: 0.66, 1.53) with a P value < 0.00001 and I ² = 95%. The result showed that the level of IL-6 in the case group was significantly higher than the control group.

FIG. 4.

Forest plot analysis of IL-6 levels in children with OSA (case) compared with those without OSA (control).

The forest plot analysis of IL-12, IL-17, IL-18, and IL-23 levels in children with OSA compared with the control group is shown in Figure 5. The pooled SMD for IL-12 was 0.47 (95%CI:−0.09, 1.02) with P value = 0.10 and I ² = 75%, for IL-17 was 2.21 (95%CI:−1.50, 5.92) with P value = 0.24 and I ² = 99%, for IL-18 was 0.19 (95%CI:−0.01, 0.40) with P value = 0.07 and I ² = 0%, and for IL-23 was 2.46 (95%CI: 1.25, 3.68) with P value < 0.0001 and I ² = 94%. The result showed that just level of IL-23 in children with OSA was significantly higher than in children without OSA.

FIG. 5.

Forest plot analysis of IL-12, IL-17, IL-18, and IL-23 levels in children with OSA (case) compared with those without OSA (control).

Subgroup analysis

Table 2 provides a summary of the subgroup analysis and is divided into 2 main sections: 1 for adults and 1 for children, both concerning the biomarker IL-6. In each section, the analysis is further divided by different variables such as ethnicity, type of blood sample, sample size, mean age, mean body mass index (BMI), and mean AHI in cases. The results showed that the ethnicity, mean BMI, and mean AHI for IL-6 levels in adults and the ethnicity for IL-6 levels in children were effective factors in the pooled SMD.

Table 2.

Subgroup Analysis

	Variable	Subgroup, N	SMD	95%CI		z-value	p-value	I²
	Variable	Subgroup, N	SMD	Min	Max	z-value	p-value	I²
IL-6 (adults)	Ethnicity	Asian (27)	1.70	1.15	2.24	6.05	<0.00001	97%
		Caucasian (27)	−0.11	−0.57	0.35	0.48	0.63	95%
		Mixed (11)	0.68	0.28	1.08	3.35	0.0008	86%
		Arab (2)	1.72	−0.83	4.28	1.32	0.19	96%
	Blood sample	Serum (47)	0.80	0.43	1.16	4.23	<0.0001	96%
	Blood sample	Plasma (19)	0.68	0.30	1.07	3.47	0.0005	90%
	Sample size	≥100 (16)	1.03	0.55	1.52	4.18	<0.0001	97%
	Sample size	<100 (50)	0.68	0.30	1.05	3.55	0.0004	95%
	Mean age, years	≥50 (14)	0.53	0.21	0.86	3.20	0.001	84%
	Mean age, years	<50 (41)	1.04	0.57	1.51	4.34	<0.0001	96%
	Mean BMI, kg/m² (17)	≥30 (17)	−0.15	−0.70	0.41	0.52	0.60	94%
	Mean BMI, kg/m² (17)	<30 (36)	1.43	1.04	1.81	7.27	<0.00001	96%
	Mean AHI in cases, events/h	≥30 (33)	0.98	0.47	1.48	3.77	0.0002	96%
	Mean AHI in cases, events/h	<30 (20)	0.32	−0.07	0.70	1.60	0.11	94%
IL-6 (children)	Ethnicity	Asian (7)	1.80	1.00	2.60	4.39	<0.0001	96%
		Caucasian (2)	0.14	−0.07	0.34	1.30	0.19	0%
		Mixed (5)	0.56	0.16	0.95	2.78	0.005	77%
	Blood sample	Serum (10)	1.07	0.54	1.61	3.93	<0.0001	95%
	Blood sample	Plasma (4)	1.21	0.19	2.22	2.33	0.02	95%
	Sample size	≥100 (9)	1.07	0.53	1.60	3.91	<0.0001	96%
	Sample size	<100 (5)	1.16	0.34	1.99	2.77	0.006	91%

SMD, standardized mean difference; CI, confidence interval. The bold number is statistically significant (P < 0.05). Mean age and mean BMI were calculated for both groups (case and control) together.

Meta-regression analysis

Table 3 provides a summary of a random meta-regression analysis and is divided into 2 main sections: 1 for adults and 1 for children, both concerning the biomarker IL-6. In each section, the analysis is further divided by different variables such as publication year, sample size, mean AHI in cases, mean age, and mean BMI. For adults, there is a significant negative relationship between the publication year and IL-6 levels (P = 0.0004), suggesting that IL-6 levels have been decreasing over the years. There is also a significant positive relationship between mean BMI (for BMI ≥ 30 vs. < 30) and IL-6 levels (P = 0.0049), indicating that higher BMI is associated with higher IL-6 levels. For children, there is a significant positive relationship between the publication year and IL-6 levels (P = 0.0014), suggesting that IL-6 levels have been increasing over the years. There are also significant positive relationships between mean AHI in cases, mean age in cases, mean BMI in cases, mean age in controls, and mean BMI in controls with IL-6 levels (P < 0.05), indicating that these factors are associated with higher IL-6 levels.

Table 3.

Random Meta-Regression Analysis

Variable	Outcome	Coefficient	Standard error	95% lower	95% upper	z-value	p-value
IL-6 (adults)	Publication year	−0.1537	0.0435	−0.2389	−0.0685	−3.53	0.0004
	Sample size	0.0037	0.0041	−0.0044	0.0118	0.90	0.3703
	Mean AHI in cases, events/h	−0.0457	0.0255	−0.0957	0.0044	−1.79	0.0739
	Mean age (≥50 vs. < 50), years	0.7407	0.4819	−0.2039	1.6853	1.54	0.1243
	Mean BMI (≥30 vs. < 30), kg/m²	1.3513	0.4806	0.4093	2.2933	2.81	0.0049
IL-6 (children)	Publication year	0.0004	0.0001	0.0001	0.0006	3.18	0.0014
	Sample size	0.0024	0.0017	−0.0009	0.0058	1.43	0.1532
	Mean AHI in cases, events/h	0.0708	0.0234	0.0250	0.1166	3.03	0.0025
	Mean age in cases, years	0.0806	0.0310	0.0198	0.1414	2.60	0.0094
	Mean BMI in cases, kg/m²	0.0313	0.0128	0.0061	0.0564	2.43	0.0149
	Mean age in controls, years	0.0782	0.0318	0.0159	0.1405	2.46	0.0139
	Mean BMI in controls, kg/m²	0.0291	0.0148	0.0002	0.0581	1.97	0.0486

The bold number is statistically significant (P < 0.05). Mean age and mean BMI were calculated for both groups (case and control) together.

Trial sequential analysis

Figure 6 presents the TSA graphs for IL-6 levels in both adults and children with OSA compared with those without OSA. The findings reveal that the z-curve intersects the RIS lines for both adults and children, suggesting that adequate evidence has been obtained. This implies that additional studies may not be necessary to establish the effectiveness of the intervention.

FIG. 6.

A trial sequential analysis of IL-6 levels in adults (D² = 100%) and children (D² = 99%) with OSA (case) compared with those without OSA (control).

Publication bias

The funnel plots for publication bias for IL-6, IL-17, and IL-18 in adults and IL-6, IL-17, and IL-23 in children are shown in Supplementary Figures S1–S6. The results showed that Begg’s (P = 0.004) and Egger’s (P = 0.021) tests for IL-6 in adults and Begg’s (P = 0.007) and Egger’s (P = 0.003) tests for IL-6 in children showed a publication bias.

Sensitivity analyses

Analyses that removed 1 study at a time and cumulative analyses demonstrated the robustness of the combined results for IL-6, IL-17, and IL-18 in adults and IL-6, IL-17, and IL-23 in children. This indicates that no single study had an undue influence on the results and that the findings remained stable with the inclusion of each study.

Discussion

The analysis of IL levels in adults and children with OSA compared with those without OSA showed significant differences. In adults, IL-6 and IL-18 levels were significantly higher in the OSA group, while, in children, only IL-6 and IL-23 levels were significantly higher. The subgroup analysis revealed that ethnicity, mean BMI, and mean AHI were effective factors in the pooled SMD for IL-6 levels in adults, and ethnicity was an effective factor in children. The meta-regression analysis showed a significant negative relationship between the publication year and IL-6 levels in adults, suggesting a decrease over the years, and a positive relationship in children, indicating an increase. Higher BMI was associated with higher IL-6 levels in adults. In children, mean AHI, mean age, and mean BMI in both cases and controls were associated with higher IL-6 levels. The TSA suggested that adequate evidence has been obtained for IL-6 levels in both adults and children with OSA, indicating that additional studies may not be necessary. However, a publication bias was detected in both groups.

OSA stands out as the most common form of sleep-disordered breathing (SDB) and it is marked by recurring episodes of upper airway blockage, resulting in hypoxia (low oxygen levels) and fragmented sleep patterns (Motamedi et al., 2018). The role of inflammation in OSA and its association with various health conditions has been extensively studied. Research indicates that OSA impacts patients’ immune responses in several ways. It tends to promote inflammatory and pro-Th2 responses and enhance the proliferative capacity of CD4 T cells and natural killer (NK) cells, while simultaneously reducing phagocytosis and the nicotinamide adenine dinucleotide phosphate (NADPH) oxidase activity in neutrophils (Said et al., 2017). This can show a complex mechanism between inflammation and OSA. The interplay between these 2 factors is multifaceted and not straightforward, indicating that various processes and interactions contribute to the overall impact of inflammation on OSA.

The primary cause of cardiac metabolic processes related to OSA is vascular and systemic inflammation, which is triggered by the activation of inflammatory pathways (Arnaud et al., 2011). One study provided that serum IL-23 levels were positively associated with the severity of OSA (Can et al., 2016), as well as IL-6 (Imagawa et al., 2004, Lu et al., 2018). IL-6 in OSA cases correlated positively with disease severity, age, and BMI (Fiedorczuk et al., 2023, Imagawa et al., 2004). Inflammatory markers such as IL-6 may have a role in the progression of atherosclerosis among OSA subjects (Ciccone et al., 2014). The pro-inflammatory cytokines (IL-17 and IL-23) may be potential markers helping in the diagnosis and post-treatment follow-up of pediatric OSA (Huang et al., 2016). Systemic inflammation is more pronounced in obese children with OSA (Gileles-Hillel et al., 2014). The precise process through which OSA influences IL-6 levels remains unclear. However, it is hypothesized that both lack of sleep and low oxygen levels in the blood play significant roles in this phenomenon (Ryan et al., 2006).

The findings from a meta-analysis revealed that Asians had plasma IL-6 levels approximately 11.9 times higher than those of individuals with mixed ethnicity who had OSA. Interestingly, the plasma IL-6 levels of Asians did not significantly differ from those of Caucasians (Imani et al., 2020). These results highlight the role of ethnicity in interleukin levels associated with OSA. Our meta-analysis further confirmed this effect on IL-6 levels in both adults and children with OSA.

Studies indicate that people with severe OSA tend to have higher average levels of IL-6 in their plasma/serum compared with those with mild OSA or healthy individuals (Motamedi et al., 2018, Thunström et al., 2015, Vgontzas et al., 1997, Yokoe et al., 2003). In addition, a significant correlation has been observed between elevated serum IL-6 levels and a higher AHI in people suffering from OSA (Ciftci et al., 2004). Our subgroup analysis reported that AHI was an effective factor for IL-6 levels in adults, and also, AHI had a positive correlation with IL-6 levels in children.

Understanding these terms and their implications can help in comprehending the discussion on the role of inflammation in OSA and its association with various health conditions. It also highlights the importance of managing OSA to reduce inflammation and its associated risks, especially in different demographic groups such as children, males, females, and postmenopausal women.

There are several limitations to the present meta-analysis. (1) There were a few published studies for the most ILs that we could not use subgroup and meta-regression analyses for them. (2) There are complex interactions between interleukins, BMI, AHI, ethnicity, gender, and OSA risk that we could not analyze the interleukins. (3) There was a publication bias for some analyses.

Conclusions

The analysis of IL levels in adults and children with OSA compared with those without OSA revealed significant differences. In adults, IL-6 and IL-18 levels were significantly higher in the OSA group, while in children, only IL-6 and IL-23 levels were significantly elevated. Ethnicity, mean BMI, and mean AHI were effective factors influencing IL-6 levels in adults, whereas ethnicity played a role in children.

Elevated IL-6 levels are associated with inflammation and may contribute to cardiovascular risk in OSA patients. Monitoring IL-6 levels could serve as a potential biomarker for disease severity and treatment response in both adults and children with OSA.

Further research is needed to explore the underlying mechanisms linking ethnicity, BMI, and IL-6 levels in OSA and whether interventions targeting IL-6 can improve clinical outcomes in OSA patients.

Footnotes

Informed Consent/Patient Consent

As our study is a secondary data analysis (meta-analysis), informed consent or patient consent is not required.

Ethical Statement

As our study is a secondary data analysis (meta-analysis), ethics statement is not required.

Authors’ Contributions

Conceptualization, A.G. and M.S.; Methodology, A.G. and M.S.; Software, M.S.; Validation, E.S. and M.S.; Formal Analysis, M.S.; Investigation, A.G. and M.S.; Writing—Original Draft Preparation, E.S. and M.S.; Writing—Review & Editing, A.G., E.S., and M.S.; Visualization, E.S. and M.S.; Supervision, A.G.; Project Administration, A.G. and M.S.

Author Disclosure Statement

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding Information

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Supplementary Material

Supplementary Figure S1

Supplementary Figure S2

Supplementary Figure S3

Supplementary Figure S4

Supplementary Figure S5

Supplementary Figure S6

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