Introduction
Asthma is one of the primary causes of chronic respiratory disorders [
1]. Over the last quarter century, a dramatic increase in the morbidity and economic burden of asthma has become a public concern [
2]. It was reported that at least 5% of the general population suffered from asthma [
3,
4]. Obesity is a well-established risk factor for asthma in adults, but the mechanism underlying the association is still far from clear [
5]. Epidemiological studies suggested that obesity and obesity related co-morbidities, including sleep-disordered breathing (SDB) and gastro-oesophageal reflux (GOR), were highly prevalent in children with asthma [
6-
8].
SDB and asthma share similar diurnal and nocturnal symptoms. Airway obstruction is involved in the pathogenesis of both diseases [
9,
10]. The term “SDB” refers to a spectrum of abnormal breathing and/or gas exchange patterns during sleep. It is characterized by habitual loud snoring and increased respiratory effort. Habitual snoring, upper airway resistance syndrome (UARS), obesity hypoventilation, obstructive sleep apnea (OSA), and central sleep apnea are the primary syndromes that fall under the rubric of SDB [
11]. The first report regarding the significant association between habitual snoring and history of exercise-induced asthma can date back to 1992 [
12]. Since then, several studies have been conducted to explore the correlation between sleep disturbance and asthma in SDB patients. [
13-
17]. However, few studies investigated the prevalence of SDB in asthma patients. To our knowledge, so far, only three, two cross-sectional and one cohort, studies examined the prevalence of SDB in children with a confirmed diagnosis of asthma [
18-
20]. Epidemiological data also demonstrated that sleep disturbance is more prevalent in school-aged children with atopic disease, such as hay fever and atopic dermatitis [
21,
22]. Based on the existing body of knowledge, we hypothesized that SDB may be associated with both the occurrence of asthma and asthma severity. The present study aimed at: I) estimating the prevalence rate of asthma; II) evaluating whether SDB was an independent risk factor for asthma among school-aged children in China; and III) further quantifying the relationship between SDB and prevalence and severity of asthma by conducting a meta-analysis quantitatively incorporating the results from recently published studies.
Materials and methods
Multicentric cross-sectional study
Study design and subjects
The study design and subjects recruitment protocol have been described in previous studies [
15,
23,
24]. The large national cross-sectional survey was performed in eight Chinese cities in November and December, 2005. A total of 22,478 school-aged children were recruited from six grades of 55 eligible primary schools in 39 districts of eight cities. Out of 22,478 children, 20,672 (91.9%), with a mean age of 9.00 years old (SD = 1.61; ranged from 5.08 to 12.00 years old), returned completed questionnaires.
The protocol of our study was approved by the Ministry of Education of People’s Republic of China and Ethics Committee of Shanghai Jiaotong University, School of Medicine.
Measures
Asthma
Asthma was assessed by addressing the following question to their guardians, “Did pediatricians or pediatric health professionals ever make a diagnosis of asthma for your child?” Answers were coded using a 2-point scale: “0” for no and “1” for yes.
SDB
The sleep behaviors of subjects were evaluated by Children’s Sleep Habits Questionnaire (CSHQ), which was designed to assess sleep characteristics of pre-school and school-aged children (4 to 12 years old) [
23,
25]. The sensitivity and reliability of CSHQ have been described in previous studies [
15,
24].
Three items are included in the subscale regarding the SDB-related signs and symptoms in the CSHQ. The test-retest reliability (ICCs) and the internal consistency (Cronbach’s alpha) of the SDB subscale are 0.76 and 0.42, respectively. The first question is “How often does your child snore loudly during a typical recent week?”. The second question is “How often does your child have snorts and gasps during sleep on a typical recent week?”. And the third one is “How often does your child have stopped or interrupted breathing during sleep on a typical recent week?”.
According to the CSHQ, the question is rated on a 3-point scale: “almost always” for 5 to 7 nights per week; “frequently” for 2 to 4 nights per week; and “occasionally/never” for 0 to 1 night per week. For this study, a SDB-affected subject was defined as an individual SDB problem occurring at least two nights per week.
Associated factors of asthma
The potential risk factors were conceptually divided into four categories: (1) socioeconomic variables, i.e. family structure, parents’ educational levels and household income; (2) demographic variables, i.e. children’s gender, age and ethnicity; (3) general chronic health problem variables, i.e. overweight/obesity status, history of food/drug allergy and gastro-oesophageal reflux; and (4) respiratory condition, i.e. history of chronic allergic rhinitis diagnosis, frequency of upper respiratory infection and history of hypertrophy of tonsils diagnosis.
Data analysis
Statistical descriptions including mean, percentage of categorical variables and standard deviation of continuous variables were calculated. Logistic regression was used to assess the association between asthma and symptoms of SDB (Table
1 and
2). All statistical analyses were performed by SPSS version 13.5 (SPSS Inc, Chicago, IL, USA). Two-sided
P value less than 0.05 was considered as statistical significant.
Table 1
Associated factors regarding asthma by univariate logistical regression models
Demographic and socioeconomic characteristics
| | | |
Age (years) | | | |
5-6 (2543, 12.3%) | 101 (4.0) | 1.86 (1.36-2.56) | <.001 |
7- (3802, 18.4%) | 141 (3.7) | 1.73 (1.28-2.34 | <.001 |
8- (3839, 18.6%) | 128 (3.3) | 1.55 (1.15-2.10) | 0.005 |
9- (3798, 18.4%) | 131 (3.4) | 1.61 (1.19-2.18) | 0.002 |
10- (3724, 18.0%) | 134 (3.6) | 1.68 (1.24-2.27) | 0.001 |
11- (2943, 14.3%) | 64 (2.2) | Ref. | |
Gender (%) | | | |
Boys (10227, 49.5%) | 434 (4.2) | 1.67 (1.44-1.96) | <.001 |
Girls (10445, 50.5%) | 269 (2.6) | Ref. | |
Ethnicity | | | |
Han ethnic group (19604, 94.9%) | 674 (3.4) | 1.40 (0.94-2.08) | 0.097 |
Minority ethnic group (1030, 5.1%) | 26 (2.5) | Ref. | |
Family income | | | |
<800 (3956, 19.3%) | 67 (1.7) | Ref. | |
800-2500 (11612, 56.6%) | 344 (3.0) | 1.77 (1.36-2.31) | <.001 |
≥2500 (4966, 24.2%) | 284 (5.7) | 3.52 (2.69-4.61) | <.001 |
Family structure | | | 0.067 |
Single parent family (1103, 5.3%) | 41 (3.7) | 1.17 (0.85-1.63) | 0.328 |
Large family (6565, 31.7%) | 249 (3.8) | 1.20 (1.02-1.41) | 0.024 |
Nuclear family (13014, 62.9%) | 413 (3.2) | Ref. | |
Mather’s education level | | | <.001 |
Low (5752, 28.2%) | 118 (2.1) | Ref. | |
Medium (6825, 33.4%) | 204 (3.0) | 1.47 (1.17-1.85) | 0.001 |
High (7843, 38.4%) | 370 (4.7) | 2.37 (1.92-2.92) | <.001 |
Father’s education level | | | <.001 |
Low (4940, 23.9%) | 109 (2.2) | Ref. | |
Medium (7075, 34.2%) | 215 (3.0) | 1.39 (1.10-1.76) | 0.006 |
High (8647, 41.8%) | 379 (4.4) | 2.03 (1.64-2.52) | <.001 |
General chronic health problems
| | | |
Overweight/obesity | | | |
Obesity (1323, 8.2%) | 58 (4.4) | 1.44 (1.08-1.91) | 0.012 |
Overweight (2206, 13.6%) | 91 (4.1) | 1.35 (1.07-1.71) | 0.011 |
Normal (12656, 78.2%) | 391 (3.1) | Ref. | |
Food/drug allergy | | | |
Yes (1176, 5.7%) | 139 (11.8) | 4.53 (3.72-5.51) | <.001 |
No (19588, 94.3%) | 565 (2.9) | Ref. | |
Gastro-oesophageal reflux | | | |
Yes (159, 0.8%) | 19 (11.9) | 3.97 (2.44-6.45) | <.001 |
No (20609, 99.2%) | 686 (3.3) | Ref. | |
Respiratory diseases
| | | |
History of chronic allergic rhinitis diagnosis | | | |
Yes (1993, 9.6%) | 310 (15.6) | 8.65 (7.39-10.12) | <.001 |
No (18764, 90.4%) | 392 (2.1) | Ref. | |
Upper respiratory infection | | | |
Frequently (3607 17.4%) | 350 (9.7) | 5.09 (4.37-5.93) | <.001 |
Occasionally (17164, 82.6%) | 355 (2.1) | Ref. | |
History of hypertrophy of tonsils diagnosis | | | |
Yes (2369, 11.4%) | 123 (5.2) | 1.68 (1.38-2.05) | <.001 |
No (18404, 88.6%) | 582 (3.2) | Ref. | |
Symptoms of sleep disordered breathing
| | | |
Habitual snoring | | | |
Usually/Often (2525, 12.2%) | 160 (6.3) | 2.20 (1.83-2.64) | <.001 |
Occasionally/No (18234, 87.8%) | 544 (3.0) | Ref. | |
Stops breathing | | | |
Usually/Often (282, 1.4%) | 24 (8.5) | 2.71 (1.77-4.15) | <.001 |
Occasionally/No (20419, 98.6%) | 677 (3.3) | Ref. | |
Snorts and gasps | | | |
Usually/Often (640, 3.1%) | 80 (12.5) | 4.47 (3.49-5.72) | <.001 |
Occasionally/No (20071, 96.9%) | 622 (3.1) | Ref. | |
Table 2
Associations of SDB symptoms with asthma by multivariate logistical regression models
Habitual snoring
| | | | | | |
Usually/Often | 1.97 (1.63-2.38) | <0.001 | 1.74 (1.39-2.16) | <0.001 | 1.28 (1.01-1.62) | 0.041 |
Occasionally/No | Ref. | | Ref. | | Ref. | |
Snorts and gasps
| | | | | | |
Usually/Often | 4.45 (3.41-5.82) | <0.001 | 3.17 (2.26-4.23) | <0.001 | 1.92 (1.34-2.76) | <0.001 |
Occasionally/No | Ref. | | Ref. | | Ref. | |
Literature search
We also conducted a meta-analysis of all published cross-sectional and cohort studies on the correlation between SDB and asthma. Relevant studies published up to June 15, 2014 were retrieved in the MEDLINE, EMBASE, and Chinese National Knowledge Infrastructure (CNKI), without any language restriction, using the following terms: (‘asthma’ or ‘bronchial asthma’) and (‘sleep disorders’ or ‘sleep apnea syndromes’ or ‘sleep-disordered breathing’ or ‘SDB’ or ‘snoring’ or ‘habitual snoring’ or ‘sleep apnea, obstructive’ or ‘OSA’) and (‘child’ or ‘infant’ or ‘child, preschool’ or ‘adolescent’ or ‘adolescent’ or ‘middle aged’ or ‘young adult’ or ‘aged’). Bibliographies of retrieved articles were also reviewed to identify additional eligible articles.
For each study, we documented information on the first author’s last name, year of publication, country of subject, ethnicities, study design, questionnaire response rate, follow-up period if available, total number of subjects recruited, mean age (or range), symptoms of SDB, and study-specific odd ratios (95% CIs). Ethnicity was categorized as Caucasian, African, Asian, or mixed for studies including subjects of more than one ethnicity. In addition, basic information of each study including country, time and authorship were screened to exclude the duplicated publication.. All data from eligible studies were recorded independently by two authors with a piloted data standardized form and compared afterwards. In cases of conflicting evaluations, minor discrepancies were resolved by a third investigator’s careful full-text reexamination.
Statistical analysis
Odd ratios (ORs) with corresponding 95% CIs were applied to assess the strength of association of SDB and asthma. Heterogeneity was checked by a Cochran’s
Q-statistic, a
P-value less than 0.01 was considered as statistically significant [
26]. The
I
2
test was also used to quantify heterogeneity in terms of percentage (ranging from 0 to 100%) [
27].
P < 0.01 for
Q-test or
I
2
> 50% indicated the existence of heterogeneity between studies. Fixed-effect model (
Mantel-Haenszel method) was used to pool the data in the existence of between-study heterogeneity; otherwise, a random-effect model (
DerSimonian-Laird method) should be applied. To evaluate the robustness of the results, a one-way sensitivity analysis was conducted to evaluate the impact of individual study on the pooled results by omitting each study in turn. Egger’s linear regression test and Funnel plot were applied to assess whether the validity of the estimate might be affected by publication bias [
28]. The statistical significance of the pooled data was assessed by
Z-test. All the statistical analyses were conducted using STATA version 12.0 (Stata Corp, College Station, TX, USA).
Discussion
Up to now, this is the largest epidemiological study exploring the relationship between SDB and childhood asthma (n = 20,672). The reported asthma cases accounted for 3.4% of our study population, which was higher prevalent than that previously reported in Jinan districts, China (sample aged 0–14 years, 0.7%) [
38]. In addition, this study identified a relatively high prevalence of habitual snoring as well (12.2%), compared to 10.9% in Hong Kong children aged 6–12 years [
28], 10.0% in French children aged 5 to 6 years [
12], and 11.4% in British children aged 4 to 7 years [
39].
In this context, we have assembled the first national school-based survey in 5–12 years children by parent-completed questionnaire to assess separately the influence of habitual snoring and OSA on asthma. In the current study, both OSA and habitual snoring were independently associated with onset of asthma after adjusting for traditional asthma risk factors. Although previous studies of stable nocturnal asthma had concluded that impaired quality of sleep, with disturbed sleep during the night, early morning awakenings and daytime sleepiness is common in asthmatic patients, these studies only focused on the effect of asthma on sleep behaviors [
40,
41]. In recent years, an increased prevalence of sleep-related disorders, such as snoring, self-reported apnea, difficulty in inducing and maintaining sleep, and daytime sleepiness in asthmatic subjects has been reported [
18,
30,
42]. In addition, other studies also have reported patients with SDB are more vulnerable to asthma [
19,
20]. All these studies, together with our cross-sectional study and meta-analysis, support the hypothesis that OSA and habitual snoring are independent risk factors for asthma.
Asthma is a diffuse airway inflammation involving small and large airways and habitual snoring. It is presumably associated with inflammation of upper airways, which could also result in atopy. It is not surprising that they may coexist in the same individual. Although the clear mechanisms underlying these associations remain to be explored, there are several mechanisms how SDB might be linked with asthma. SDB is associated with elevation of pro-inflammatory cytokines, excessive daytime sleepiness (EDS), increased leptin levels, and reduced adiponectin levels [
43]. The two adipokines, produced by the adipose tissue that has important pro-inflammatory properties, are correlated with the occurrence of asthma [
44]. Moreover, it is possible that the correlation between asthma and SDB might partially be due to the fact that the presence of upper airway inflammation is common in asthma patients.
As discussed above, SDB was also shown to be associated with the onset of asthma. In this meta-analysis, we further examined the potential interaction between SDB and asthma severity. A clear relationship between reported snoring and severe asthma was identified even when likely confounding factors were adjusted, suggesting that patients who have difficulty achieving adequate asthma control should be screened for SDB. The mechanisms through which SDB worsens asthma still remain largely unclear. Our finding has biological plausibility since Irwin
et al. observed that tumor necrosis factor (TNF-α), one of the pro-inflammatory cytokines, participate in sleep control and elevate in sleep-deprived adults [
45]. TNF-α has been reported as a marker of 'systemic' inflammation in adults with severe asthmatics [
46]; neutrophilic airway inflammation has been observed in children with OSA [
47]. In addition, there is evidence that the treatment of snoring and OSA with nasal-continuous positive airway pressure (nCPAP) can lead to improved control of asthma both during the night and while awake [
48].
Several limitations of our study should be addressed. Firstly, this study is questionnaire-based. No objective measurement, such as attended polysomnography, was performed to diagnose SDB. It may cause measurement bias. Meanwhile, it is impossible to determine whether the subjects reporting asthma actually were asthmatics due to the lack of a gold standard. Another drawback was the direction of the cause-effect relationship between SDB and asthma or whether there is a true cause-effect relationship, due to the limitation of cross-sectional data in drawing causality conclusion. The statistical pooling of independent studies also has some disadvantages. Last, the relationship between SDB and asthma severity was not evaluated in our cross-sectional study due to the limited information obtained from the questionnaire. Firstly, meta-analysis cannot correct the limitations of primary research. Secondly, most of included studies for meta-analysis were performed in Caucasians, which restricts the findings to be generalized to other populations in different ethnicities. Finally, although these estimations were based on individual adjusted ORs, and we adjusted our analysis for a broad range of potential confounding factors, there is still a chance of unmeasured or residual confounding. Despite the limitations described above, our study had a large sample size that may increase the validity of results. Further, the meta-analysis summarized the previous studies and our study, which could provide insight into the association between SDB and asthma stratified by ethnicity and age. Up to now, this is the first national study to investigate whether SDB was an independent risk factor for asthma in school-aged children.
In conclusion, our large epidemiological study, for the first time, examined the association of asthma with habitual snoring and OSA among Chinese children. Our findings provide new evidence to support current asthma guidelines. The pooled data from this large cross-sectional study and meta-analysis will enrich our knowledge on the relationship between SDB (habitual snoring and OSA) and asthma. Further studies should focus on elucidating the underlying mechanism, such as how SDB is associated with asthma.
Competing interests
The authors declare that they have no competing interests.
Authors’ contributions
Conception and design: SX and LS; analysis and interpretation: LL, XZ, LS, SX, JX, YC, and FJ; drafting of the manuscript for important intellectual content: LL; revising the manuscript for important intellectual content: LS, XZ, TS; administrative, techanical, and material support: LS, SX, JX, YC, and FJ; and final approval of the manuscript: SX, LS, LL, XZ, LS, SX, JX, YC, FJ, and TS.