Abstract
Immunoglobulin E (IgE)–mediated allergic diseases, including eczema, atopic dermatitis (AD), and allergic rhinitis (AR), have increased prevalence in recent decades. Recent studies have proved that environmental pollution might have correlations with IgE-mediated allergic diseases, but existing research findings were controversial. Thus, we performed a comprehensive meta-analysis from published observational studies to evaluate the risk of long-term and short-term exposure to air pollutants on eczema, AD, and AR in the population (per 10-μg/m3 increase in PM2.5 and PM10; per 1-ppb increase in SO2, NO2, CO, and O3). PubMed, Embase, and Web of Science were searched to identify qualified literatures. The Cochran Q test was used to assess heterogeneity and quantified with the I2 statistic. Pooled effects and the 95% confidence intervals (CIs) were used to evaluate outcome effects. A total of 55 articles were included in the study. The results showed that long-term and short-term exposure to PM10 increased the risk of eczema (PM10, RRlong = 1.583, 95% CI: 1.328, 1.888; RRshort = 1.006, 95% CI: 1.003–1.008) and short-term exposure to NO2 (RRshort = 1.009, 95% CI: 1.008–1.011) was associated with eczema. Short-term exposure to SO2 (RRshort: 1.008, 95% CI: 1.001–1.015) was associated with the risk of AD. For AR, PM2.5 (RRlong = 1.058, 95% CI: 1.014–1.222) was harmful in the long term, and short-term exposure to PM10 (RRshort: 1.028, 95% CI: 1.008–1.049) and NO2 (RRshort: 1.018, 95% CI: 1.007–1.029) were risk factors. The findings indicated that exposure to air pollutants might increase the risk of IgE-mediated allergic diseases. Further studies are warranted to illustrate the potential mechanism for air pollutants and allergic diseases.
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Introduction
Allergic diseases are inflammatory disorders that involve various types of cells and factors, including allergens, immunoglobulin (Ig)E, mast cells, basophils, cytokines, and soluble mediators. The etiological mechanisms of allergic diseases are complex (Murrison et al. 2019). The incidence of allergic diseases has increased sharply with increasing industrialization and the accompanying changes to the environment and people’s lifestyles. According to one study conducted by the World Allergy Organization which involved 30 nations/region, approximately 250 million (22%) of the 1.2 billion people in those regions suffered from allergic diseases (Hu et al. 2018). Because of their high prevalence, these diseases pose a serious financial threat to affected households and consume substantial resources in socialized healthcare systems.
Allergic diseases can be divided into two categories, which are IgE mediated and non-IgE mediated. IgE-mediated allergy reactions are typically of rapid onset, and symptoms range from mild to severe. IgE, an antibody class found only in mammals, has unique properties, and plays a central role in the development of acute allergic reactions and IgE-mediated allergic diseases. IgE-mediated allergic diseases involve eczema, atopic dermatitis (AD), and allergic rhinitis (AR). The major risk factors for IgE-mediated allergic diseases studied widely were genetics and immune functions (Renz et al. 2011; Hüls et al. 2019). However, these traditional risk factors were not changed dramatically in recent decades. Therefore, traditional risk factors alone may not be sufficient to explain the massive rise in IgE-mediated allergic disease prevalence.
Are long-term or short-term air pollution associated with the development and prevalence of IgE-mediated allergic conditions? Numerous studies have attempted to answer this question, but no consensus has reached. For instance, Schnass et al. conducted a cohort study and concluded that traffic-related air pollution would increase the prevalence of eczema for elders (Schnass et al. 2018). However, Lopez et al. found that long-term air pollution has no adverse effect on adult eczema (Lopez et al. 2021). The same dispute can also be found in AR’s research. Huang et al. reported that the prevalence of AR in children would increase when exposed to PM2.5 (Huang et al. This review was conducted according to the PRISMA framework (Moher et al. 2009) (Table S1). We firstly posed the research question: “Does exposure to air pollutants increase the risks of IgE-mediated allergic diseases?” The included criteria were based on the population, exposure, comparator, and outcomes (PECO) framework. A framework was used to explore the association of air pollutant exposure with health outcomes. P refers to people who have IgE-mediated allergic diseases. E refers to air pollutants. C refers to incremental effect per unit increase in concentration of air pollutants for disease risk. O refers to the outcome of eczema, AR, and AD (Morgan et al. 2018; Marx et al. 2021) (Fig. S1). Embase, PubMed, and Web of Science were searched to find relevant research concerning the association between air pollutants and diseases up to May 2021. Search terms included (“allergic rhinitis” OR allergic rhinitis [MeSH Terms] OR “allergic respiratory diseases” OR allergic respiratory diseases [MeSH Terms] OR “eczema” OR eczema [MeSH Terms] OR “eczematous dermatitides” OR eczematous dermatitides [MeSH Terms] OR “atopic dermatitis” OR atopic dermatitis [MeSH Terms] OR “dermatitis atopic” OR dermatitis atopic [MeSH Terms]) AND (“carbon monoxide” OR “sulfur dioxide” OR “particulate matter” OR “nitrogen dioxide” OR “ozone” OR “PM2.5” OR “PM10”). The detailed search process is shown in supplementary Table S2. The inclusive criteria were as follows: (1) Articles should be epidemiologic studies focusing on the associations between the IgE-mediated allergic diseases with air pollutants exposure. (2) Eczema diagnosis was made according to ICD-10 code L30. ICD-10 code L20 and ICD-9 code 691 were used to classify AD. ICD-10 code J30 and ICD-9 code 477 were principles to detect AR. The classification of these three diseases could also be based on questionnaires of eczema or AD or AR. (3) Studies reported effect estimates (RR, OR, HR, PC) or data that could calculate the effect size. (4) Language was restricted to English. The studies would be excluded were as follows: (1) Animal studies, mechanism studies, reviews and meta-analyses, case reports, treatment effect evaluations, and studies without original data were excluded. (2) Studies focusing on the association between indoor air pollution and prenatal and allergic diseases were also excluded. Endnote software (X9 version) was used to screen eligible literature. All articles were evaluated by two investigators. First, duplicated studies were removed. Then, two investigators (Wang H and Li XB) independently screened remaining studies to select eligible studies. When controversy existed, a third investigator was asked to discuss and resolve the disagreement. For each included study, basic characters were extracted, including disease, first author, publication year, region, study design, sample size, number of cases, age, ICD, data sources of pollutants, term of exposure (short-term or long-term) (Ibrahim et al. 2021), mean concentration of pollutants, and impact effect estimates. Investigators extracted information based on the following principle. A single-pollutant model was used to find the effect of a pollutant, and a multi-pollutant model was utilized to explore the interactions of multiple pollutants on disease risk. If the study contained single-pollutant and multi-pollutant models, the former would be chosen (Yang et al. 2018). Due to inconsistent units of pollutant concentration in some literature, we standardized all effect sizes for every 10-µg/m3 increase in PM2.5 and PM10 and 1-ppb increase in NO2, SO2, CO, and O3 (Fan et al. 2020). The specific formulas were as follows: (1) ppm = 1000 ppb, 1 ppb = M/22.4 (µg/m3). M refers to the molecular weight of each air pollutant. Adjusted relative risk (RR), odds ratio (OR), risk ratio (HR), and percentage change (PC) were used to assess the risk of eczema, AR, and AD (Ning et al. 2021). During data consolidation, PC was transformed into RR. The effect estimates (RR/OR/HR) were standardized. OR and HR were roughly regarded as RR, when outcome events were popular and the effect size was small (Chen et al. 2017). All the effect sizes were pooled by the standardized increment of environmental pollutant concentration. The standardized formulae of effect sizes were as follows: The Newcastle–Ottawa Scale (NOS) and the Office of Health Assessment and Translation (OHAT) tool were used to evaluate the quality of included literature. Among them, NOS was used to assess the reported quality of cohort, case–control, and cross-sectional studies (Lin et al. 2021a). NOS has eight items, and its score ranged from 0 to 9. A study with a score higher than 7 was regarded as a high-quality study. A study with a score of 3 to 6 was intermediate quality. Otherwise, it was low-quality. To the best of our knowledge, there is no effective scale to assess the quality of time-series literature. Therefore, we adopted the quality scale used by Mustafic et al. This scale mainly evaluates three aspects: the validity of the outcome event, the assessment of air pollutant exposure, and the adjustment of confounding factors. When the evaluated document score was 3–5 points, it could be considered high-quality (Mustafic et al. 2012). In a meta-analysis, the OHAT tool was used to assess the risk of bias in each study (Zhang et al. 2021a). Cochran’s Q-test and I2 statistic were used to evaluate the heterogeneity between studies. If I2 is greater than 50%, the heterogeneity is high. Otherwise, the heterogeneity is low. If the p-value of the Q test is less than 0.05, a high heterogeneity is between the studies. Then the random effect model was chosen. The pooled RRs with 95% CIs were estimated using the fixed-effect mode, if the heterogeneity was low. Subgroup analyses were performed by age, region, and study design for each pollutant. Funnel plots were used to represent the publication bias in studies (Bai et al. 2020). In addition, the pooled effect values were tested by determining the age (< 18 years old; ≥ 18 years old; all ages), region, and study design for each pollutant and sensitivity analyses by ICD (Chevalier et al. 2015). Limited by the number of available studies, sensitivity analyses were performed for studies that could be combined in each pollutant. All data analyses were realized by R packages “metafor” and “forestplot” in version 4.0.3. A total of 2478 articles were searched. After screening the titles and abstracts, 150 articles were identified. Finally, fifty-five articles were included thoroughly reading full texts. The process of literature screening is shown in Fig. 1. In four articles, two diseases were simultaneously discussed (Kim et al. 2016; Wang et al. 2016; Min et al. 2020; To et al. 2020). Therefore, seventeen studies were on eczema (six time-series, six cohorts, and five cross-sectional studies), thirty-one studies were on AR (nine time-series, nine cohorts, one case–control, and twelve cross-sectional studies), and eleven studies were on AD (four time-series, five cohorts, and two cross-sectional studies). The information extracted from the literature is shown in Table 1. According to the NOS scale and the OHAT tool, all included studies had high qualities. Scores of articles and details of risk bias assessment were listed as shown in supplementary Table S3 (eczema), Table S4 (AD), and Table S5 (AR). Flowchart to show assessment of eligibility of identified studies As shown in Table 2 and Fig. S2, the pooled risk for eczema was 1.583 (95% CI: 1.328–1.888) with an increment of 10 μg/m3 in PM10. However, exposures to PM2.5, NO2, and SO2 were not associated with the risk of eczema. The between-study heterogeneity was low than exposure to PM10, NO2, and SO2 (I2 < 50%). The results of publication bias are shown in Fig. S3a-S3b. In subgroup analyses according to age, study design, and region, long-term exposure to PM2.5 and NO2 had no impact on eczema. Other details are shown in Fig. 2a. In addition, due to the limited number of studies on CO and O3, the analysis was not performed. Forest plot of subgroup analysis for diseases. a Forest plot of subgroup analysis for eczema. b Forest plot of subgroup analysis for AD. c Forest plot of subgroup analysis for AR. No.f: number of; ICD: International Classification of Diseases In this meta-analysis, we found that an increment of 10 μg/m3 in PM10 and 1 ppb in NO2 was associated with the risk of eczema (PM10, RR = 1.006, 95% CI: 1.003–1.008; NO2: RR = 1.009, 95% CI: 1.008–1.011), and PM2.5 and SO2 were irrelevant with the risk of eczema (Table 2; Fig. S2). The between-study heterogeneity and publication bias are illustrated in Table 2 and Fig. S3c-S3f. Sensitivity analyses by ICD did not change the overall effect in PM10 and NO2 (Fig. S4). In all age groups, PM10 and NO2 increased the risk of eczema. The study design (cross-sectional) group showed no correlation on eczema in PM2.5, PM10, and NO2, and the study design (time-series) group suggested that each increment unit in PM2.5, PM10, and NO2 increased the risk of eczema. Exposures to PM2.5, PM10, and NO2 were related to the risk of eczema in region (Asia) group (Fig. 2a) Studies on pollutants CO and O3 were not enough for combining analyses at present. A total of eleven studies on AD were included in this meta-analysis. However, significant associations were not found between AD and exposure to six air pollutants (Table 2; Fig. S5). The results of heterogeneities and funnel plots are displayed in Table 2 and Fig. S6a-S6f. From Fig. 2b, ICD sensitivity analyses did not change the overall estimates (Fig. S4). The age under 18 group indicated that each increment unit in PM10 and CO was harmful to the occurrence of AD with low between-study heterogeneity (I2 < 50%), while PM2.5, NO2, SO2, and O3 were irrelevant. The study design (cohort) group was observed to have a significant association between AD and SO2 (Fig. 2b). The evidence of the detailed subgroup analyses is showed in Fig. 2b. As we can see from Table 2 and Fig. S5, SO2 increased the risk of AD by 1.008 with an increment of 1 ppb (95% CI: 1.001–1.015). PM10, NO2, and O3 had no impact on AD. The sensitivity analyses of ICD suggested that PM10, SO2, and O3 were correlated with AD in short time (Fig. S4). PM10 increased the risk of AD in all age groups. The details in subgroup analyses are shown in Fig. 2b. The results showed that only PM2.5 (an increment of 10 μg/m3) had a harmful effect to the occurrence of AR (RR = 1.058, 95% CI: 1.014–1.222), and the heterogeneity between articles was high (I2 > 50%) (Table 2; Fig. S7). PM10, NO2, SO2, CO, and O3 had no effect on the risk of AR, and the specific analyses are shown in Fig. S7. The evidence of publication bias is displayed in supplementary material Fig. S8a-S8f. Sensitivity analyses in the ICD group showed no association between AR and PM2.5 and O3 (Fig. S4). According to subgroup analyses in Fig. 2c, in the age under 18 group, the effect estimate was increased by 1.133 for an increment of 10 μg/m3 of PM2.5. The study design (cross-sectional) group found a correlation between PM10 and AR. In the region (Asia) group, PM2.5 strengthened the risk of AR, and all results were as shown in Fig. 2c. As shown in Table 2 and Fig. S7, an increment unit in PM10 and NO2 was associated with the risk of AR (PM10: RR = 1.028, 95% CI: 1.008–1.049; NO2: RR = 1.018, 95% CI: 1.007–1.029). PM2.5, SO2, CO, and O3 were not associated with the risk of AR. High heterogeneity was observed in PM2.5, PM10, NO2, SO2, and O3 (I2 > 50%). Funnel plots for publication bias are displayed in Fig. S8g-S8l. Sensitivity analyses by ICD, PM10, NO2, and SO2 were harmful factors for the risk of AR. In a subgroup analysis of age, PM10 was harmful to the population aged < 18. For all age groups, PM2.5, PM10, NO2, and SO2 increased the risk of AR. The study design (time-series) group indicated that PM10 and NO2 were potential risk factors for AR. In the region (Asia) group, PM10 and NO2 were associated with AR risk, and more details are shown in Fig. 2c. In the current systematic review and meta-analysis of 55 epidemiological studies, we performed a comprehensive evaluation of available data on ambient air pollution and IgE-mediated allergic diseases. Most included studies reported a positive association between certain air pollutants’ level and greater risk of IgE-mediated allergic diseases (Table 1). The meta-analysis results showed significant associations of long-term exposure to PM2.5 with AR and AD. Besides, long-term exposure to PM10 was found to be related to the increased risk of eczema. Although point estimates indicate higher risk of exposure to NO2, SO2, CO, and O3, the difference was not statistically significant in terms of confidence intervals. Short-term exposures to PM10 and NO2 were related with eczema and AR, and short-term exposures to SO2 and PM2.5 were associated with AD. Actually, it is difficult to assess the health effects of individual ambient pollutants, because these substances are rarely produced in isolation. For example, a study of 317,926 children found a significant positive association between traffic-related pollution and AD in both sexes. However, analysis of individual traffic-related pollutants only revealed associations of AD with NOx and CO in females (Lee et al. 2008). Therefore, synergistic effects of multiple pollutants can be missed when studying the effects of a single pollutant. Besides, between-study heterogeneity for these meta-analyses was high. These might be partly explained by varied study designs, regions, ages outcome definition, and exposure assessment of the included studies. Results of studies highlighted that early childhood exposure to air pollutants from birth to 5 years of age was associated with new onset of IgE-mediated allergic diseases throughout childhood and there was evidence to suggest that air pollutants may have an ongoing effect with a lag time of about 3 years (Bowatte et al. 2015). Therefore, the longer the observational duration from birth, the higher the likelihood of finding the relationship between air pollutants and IgE-mediated allergic diseases. When we conducted subgroup analyses based on age, we found that people age under 18 had a higher risk of IgE-mediated allergic diseases compared with adults aged above 18. Nevertheless, many of these subgroups are I2 > 50, so age might not be the only source of heterogeneity. 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This study was supported by the National Natural Science Foundation of China (81803310); Undergraduate Innovation and Entrepreneurship Training Program in Anhui Province (S201910366064); Emergency Research Project of Novel Coronavirus Infection of Anhui Medical University (YJGG202003); the Grants for Scientific Research of BSKY (XJ201619) from Anhui Medical University; and Research Fund of Anhui Institute of Translational Medicine (2021zhyx-C21). Hua Wang: performed the data analysis, writing—original draft. **an-Bao Li: performed the data analysis, writing—original draft. **u-Jie Chu: investigation, data curation. Nv-Wei Cao: formal analysis, helped revise the manuscript. Hong Wu: investigation. Rong-Gui Huang: investigation. Bao-Zhu Li (lbz88730@163.com) and Dong-Qing Ye (anhuiydq@126.com): conceptualization, project administration, funding acquisition, writing—original draft. Not applicable. Not applicable (this study does not contain any individual person’s data in any form). The authors declare that they agree with the publication of this paper in this journal. The authors declare no competing interests. Responsible Editor: Lotfi Aleya Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Below is the link to the electronic supplementary material. Fig. S1 Research method conceptual framework diagram (PNG 497 kb) Fig. S2 Forest plot of included studies of air pollutants exposure and eczema (PNG 676 kb) Fig. S3 Funnel plot of studies on the effect of air pollutants on eczema (PNG 484 kb) Fig. S4 Sensitivity analyses by ICD of IgE mediated allergic diseases (PNG 1299 kb) Fig. S5 Forest plot of included studies of air pollutants exposure and AD (PNG 826 kb) Fig. S6 Funnel plot of studies on the effect of air pollutants on AD (long-term) (PNG 492 kb) Fig. S7 Forest plot of included studies of air pollutants exposure and AR (PNG 2260 kb) Fig. S8 Funnel plot of studies on the effect of air pollutants on AR (PNG 618 kb) Wang, H., Li, XB., Chu, XJ. et al. Ambient air pollutants increase the risk of immunoglobulin E–mediated allergic diseases: a systematic review and meta-analysis.
Environ Sci Pollut Res 29, 49534–49552 (2022). https://doi.org/10.1007/s11356-022-20447-z Received: Accepted: Published: Issue Date: DOI: https://doi.org/10.1007/s11356-022-20447-zMaterial and methods
Search strategy
Inclusion and exclusion criteria
Data extraction
Data synthesis
Quality and risk bias assessment
Statistical analysis
Results
Characteristics of included studies
Relationship between air pollution and eczema
Effect of long-term air pollution exposure on eczema
Effect of short-term air pollution exposure on eczema
Relationship between air pollution and AD
Effect of long-term air pollution exposure on AD
Effect of short-term air pollution exposure on AD
Relationship between air pollution and AR
Effect of long-term air pollution exposure on AR
Effect of short-term air pollution exposure on AR
Discussion
Data availability
References
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Funding
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Ethics approval
Consent to participate
Consent for publication
Competing interests
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