Background
Asthma is a chronic inflammatory respiratory disease characterized by airway obstruction due to both smooth muscle hyperresponsiveness and inflammation [
1]. Both asthma therapies and hospitalizations/doctor visits generate significant healthcare utilization [
2]. Specifically, an estimated US$62.8 billion was spent on the diagnosis and management of asthma in the U.S. in 2009 with inflation adjusted costs continuing to rise [
3]. Asthma is the leading chronic disease cause of hospitalizations and school absences in children [
4]. Individuals with frequent asthma exacerbations represent those with the greatest morbidity and economic cost due to asthma [
5,
6]. Thus, identification of children at highest risk for exacerbations could result in personalized care and substantially improve outcomes. The current guidelines that determine asthma control are related to clinical symptoms, lung function, and recent history of exacerbations [
7]. Despite ongoing efforts in asthma management and control, acute exacerbations continue to be a significant health care problem. Numerous studies have attempted to predict asthma exacerbations in children using a variety of clinical and biomarker inputs. These include clinical prediction scores and exhaled breath condensate interleukin-5 level [
8], fractional exhaled nitric oxide and inflammatory markers in exhaled breath condensate alone and combined with clinical variables [
9], recent severe asthma exacerbations as an independent predictor of future severe exacerbations [
10], and addition of single nucleotide polymorphisms (SNPs) in concert with clinical parameters to predict exacerbation outcomes [
11]. In the latter, the authors used genome-wide genotypic data (~ 550,000 SNPs) to determine children at risk for a severe asthma exacerbation. A predictive panel consisting of 417 children and a validation sample of 164 children was used; however, the addition of 160 SNPs to clinical data only increased the model Area Under the Receiver Operator Characteristic (AUROC) curve from 0.54 to 0.66 showing good, but not excellent, predictive power. Similarly, each of the other aforementioned models has had variable success with respect to prediction of asthma exacerbation [
12], and there is ongoing need for improved prediction of asthma clinical outcomes and discovery of biomarkers that achieve this goal.
MicroRNAs (miRNA) are short, single-stranded RNA molecules approximately 19–23 nucleotides that regulate gene expression with various functions described in the literature [
13]. Circulating miRNAs are attractive biomarkers for prediction of disease outcomes [
14] and are stable in the serum over long time frames even in harsh conditions [
15]. Circulating miRNA has been studied as a non-invasive biomarker in heart transplant rejection prediction [
16], prediction of metastasis in breast cancer patients with early disease [
17], and prediction of thiazolidinedione response in diabetes prevention [
18]. Prior studies of miRNA in asthma have focused mainly on asthma case-control status, including studies involving circulating miRNA expression in childhood asthmatics compared to healthy controls [
19], regulation of IL-5 expression by miRNA differential expression in serum of asthmatics and health controls [
20], and differential expression of miRNA in epithelial and airway cells [
21]. A recent study showed that a subset of circulating miRNAs is expressed uniquely between asthma and allergic rhinitis patients [
22]. However, prediction of important clinical events, such as asthma exacerbations, using circulating miRNAs has not been performed.
Our study investigated asthma exacerbation prediction in the 12 months following randomization to the inhaled corticosteroid treatment arm in 153 subjects in Childhood Asthma Management Program (CAMP) [
23]. The miRNA model was compared to a clinical score of exacerbation risk [
24]. Subsequently, we assessed the use of a combined miRNA and exacerbation clinical score model. We hypothesized that a miRNA model or a combined miRNA-clinical score model would have predictive capabilities superior to the clinical score alone. We identified a panel of 3 miRNAs with good predictive power comparable to the clinical score. One of the 3 miRNAs targets genes is well known in asthma pathobiology, while the others have been associated with asthma. The combined miRNA-clinical score model showed very good predictive power superior to either the miRNA or clinical score models alone.
Discussion
In this study, we examined baseline circulating miRNA in childhood asthmatics prior to treatment with inhaled corticosteroids (ICS) to predict exacerbations in the subsequent year. We noted that 12 miRNAs (Table
2) were significantly associated with future exacerbations, with each doubling of expression of these miRNAs associated with a 25–67% increase in risk of exacerbations. We had previously reported 3 of the 12 (miR-223-5p, miR-339-3p, miR-454-3p) to be associated with baseline FVC%, and 6 of the 12 (miR-126-3p, miR-146b-5p, miR-206, miR-342-3p, miR-409-3p, miR-454-3p) to be associated with baseline FEV1/FVC [
26]. When combined, 3 miRNAs (miR-146b-5p, miR-206 and miR-720) by themselves provided comparable predictive power to an established clinical model of exacerbations. Moreover, when these 3 miRNAs were combined with the clinical factors included in the model, there was significant increase in the ability to predict future exacerbations with AUROC 0.81.
Notably, all of the subjects within the current study were randomized to inhaled corticosteroids as part of a clinical trial cohort. Therefore, while our findings may apply broadly to asthma exacerbations as a whole, this information may also have pharmacogenomic implications via the identification of subjects who experience exacerbations despite therapy with inhaled corticosteroids. Therefore, validation of these findings may identify subjects who may benefit from alternate, or additional, therapies.
Our top miRNA, hsa-miR-206, was significantly associated with subsequent asthma exacerbations with OR 0.60 (95% CI: 0.42–0.83) (Table
2). Given that the distribution of expression for miR-206 is broad (spanning multiple cycle thresholds, Fig.
1, Panel A), this suggests that the reported OR for the individual miRNAs are likely conservative. Figure
1 shows the difference between the miRNA (Panel A) and asthma exacerbation clinical score (Panel B) logistic regression functions with fairly dispersed values for exacerbation status compared to a division at miR-206 cycle threshold around 26. This suggests miR-206 discriminates exacerbation status better than clinical score.
Stepwise selection of significant miRNAs resulted in a 3-miRNA model (miR-146b, miR-206 and miR-720) that was subsequently compared to and combined with an asthma exacerbation clinical score model (Table
3). The combined miR-clinical score model had very good predictive power to discriminate exacerbation from no exacerbation (AUROC 0.81). This AUROC is higher than prior clinical studies focused solely on the asthma exacerbation clinical score [
24] with an AUROC of 0.75 for a Costa Rican cohort and AUROC 0.69 for the CAMP cohort and Childhood Asthma Control Test (C-ACT) with AUROC 0.72 [
39]. The AUROC is also improved compared to other biomarkers including addition of our prior study of GWAS SNPs from the same cohort [
11]. The combined miR-clinical score model far exceeded the predictive capability for exacerbation status compared to the miRNA or clinical score model alone. These data support the hypothesis that optimal prediction models for personalized medicine are likely to come from a combination of ‘ omics and clinical variables.
The miRNAs used to build our predictive model overlaps with miRNAs relevant to asthma pathobiology. miR-146b, along with miR-146a, are negative regulators of inflammatory gene expression in lung tissue [
40]. A murine model study of acute and chronic asthma showed consistent upregulation of miR-146b, which is also expressed by leukocytes and has been shown as a negative regulator NF-κβ in human breast cancer cells [
41]. This could potentially explain why circulating miR-146b may be a viable biomarker. In eosinophilic esophagitis, miR-146b has been found both in esophageal biopsies and differentially regulated in the plasma [
42]. Altered expression in the airway wall of the other 2 miRNAs in our predictive model, miR-206 and miR-720, were noted in a mouse model of childhood allergic asthma [
43]. Moreover, miR-206 has been shown to be involved in airway smooth muscle (ASM) innervation [
44], thereby enhancing the mechanistic significance of our model. Integrative miRNA studies would be needed for direct elucidation of interactions of our miRNAs as they relate to asthma pathobiology. Pathway analysis (Additional file
1: e-Table S3) also had borderline significance for the NF-κβ pathway, which correlates to the aforementioned miR-146b regulatory role. Genes affected by the miRNA were also involved in the inactivation of GSK3 by AKT causes accumulation of b-catenin alveolar macrophages pathway (Additional file
1: e-Figure S1 and e-Table S3). Inactivation of GSK3 has been studied in a murine model and is associated with ASM hypertrophy and possibly linked to asthmatic airway remodeling [
45]. Overall, further study of these miRNA may suggest a functional approach to small RNA-directed therapies to treat or prevent asthma exacerbations.
This study has several strengths including a large number of interrogated miRNAs, large sample size of pediatric asthma patients from the CAMP cohort, biologically significant miRNAs found in modeling, and comparison to a prior validated asthma exacerbation clinical score. The CAMP cohort is clinically well-characterized notably for both identification and protocol-based treatment of asthma exacerbations, which should reduce measurement error. Replicate analysis discussed in the methods section showed high miRNA-miRNA correlations. While the primary CAMP trial recruitment occurred several years ago, circulating miRNAs have been shown to be stable over the course of many years [
46,
47]. While we do not yet have independent replication, the maintenance of high AUROCs in our cross-validation analyses supports potential generalizability.
Conclusions
From a survey of 738 baseline circulating miRNA in childhood asthmatics prior to ICS treatment, we identified 12 miRNAs that were significantly associated with exacerbations in the subsequent year, with each doubling of expression of these miRNAs associated with a 25–67% increase in risk of exacerbations. When combined, 3 miRNAs (miR-146b-5p, miR-206 and miR-720) by themselves provided comparable predictive power to an established clinical model of exacerbations. Moreover, when these 3 miRNAs were combined with the clinical factors included in the model, there was significant increase in the ability to predict future exacerbations with AUROC 0.81. To our knowledge, this is the first study to investigate prediction of asthma exacerbations with miRNAs.
Our results are promising for the translation of circulating miRNA to predict clinical outcomes related to asthma and are consistent with prior studies using circulating miRNA as biomarkers, predictors, or markers of treatment response. miRNA as potential biomarkers for a priori prediction of asthma exacerbations may be particularly salient, given the substantial morbidity and health care costs associated with exacerbations. Additionally, since this particular study restricted subjects to inhaled corticosteroid therapy, its findings may have direct pharmacogenomic relevance in the prediction of which participants respond to ICS therapy. Further study of miRNA alone or in concert with other genomic and epigenomic markers may reveal additional predictive power for asthma risk assessment and even treatment responses.