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Abstract

Asthma is characterized by recurrent and reversible airflow obstruction, which is routinely monitored by history and physical examination, spirometry and home peak flow diaries. As airway inflammation is central to asthma pathogenesis, its monitoring should be part of patient management plans. Fractional exhaled nitric oxide level (FeNO) is the most extensively studied biomarker of airway inflammation, and FeNO references were higher in Chinese (Asians) than Whites. Published evidence was inconclusive as to whether FeNO is a useful management strategy for asthma. Other biomarkers include direct (histamine, methacholine) and indirect (adenosine, hypertonic saline) challenges of bronchial hyperresponsiveness (BHR), induced sputum and exhaled breath condensate (EBC). A management strategy that normalized sputum eosinophils among adult patients resulted in reductions of BHR and asthma exacerbations. However, subsequent adult and pediatric studies failed to replicate these benefits. Asthma phenotypes as defined by inflammatory cell populations in sputum were also not stable over a 12-month period. A recent meta-analysis concluded that induced sputum is not accurate enough to be applied in routine monitoring of childhood asthma. There is poor correlation between biomarkers that reflect different asthma dimensions: spirometry (airway caliber), BHR (airway reactivity) and FeNO or induced sputum (airway inflammation). Lastly, EBC is easily obtained noninvasively by cooling expired air. Many biomarkers ranging from acidity (pH), leukotrienes, aldehydes, cytokines to growth factors have been described. However, significant overlap between groups and technical difficulty in measuring low levels of inflammatory molecules are the major obstacles for EBC research. Metabolomics is an emerging analytical method for EBC biomarkers. In conclusion, both FeNO and induced sputum are useful asthma biomarkers. However, they will only form part of the clinical picture. Longitudinal studies with focused hypotheses and well-designed protocols are needed to establish the roles of these biomarkers in asthma management. The measurement of biomarkers in EBC remains a research tool.

Keywords

airway inflammation asthma biomarker exhaled breath condensate induced sputum metabolomics nitric oxide

Introduction

Asthma is characterized by recurrent and reversible airflow obstruction, which is also associated with bronchial hyperresponsiveness (BHR) to external stimuli. The etiology of asthma is multifactorial and involves a complex interaction between predisposition genes, early-life events and environmental exposures. Asthma status is thus reflected by different subjective and objective indicators as illustrated in Figure 1. Conventionally, asthma can be assessed by the frequencies of night-time awakening, daytime symptoms and exercise limitation [Global Initiative for Asthma, 2012] as well as quality of life scores [Juniper et al. 1997]. Asthma can also be assessed objectively by medication use, school or work absences, peak expiratory flow (PEF) monitoring, spirometry and acute bronchodilator response to a short-acting β₂-agonist. Other asthma biomarkers include direct (histamine, methacholine), indirect (adenosine, hypertonic saline) and specific (inhalant allergen) challenges for the presence and extent of BHR. Routine monitoring measures of asthma include history and physical examination, spirometry and home PEF diaries. The two latter indicators provide information mechanically on the extent of airflow limitation. The pathogenesis of asthma involves both inflammatory and noninflammatory components, with airway inflammation being a central feature. Treatment for asthma includes anti-inflammatory medications such as inhaled corticosteroids (ICSs). Nonetheless, monitoring of airway inflammation should therefore be part of the management plan for asthmatic patients.

Figure 1.

Assessment scheme for asthma status using both subjective and objective methods. BAL, bronchoalveolar lavage; NO, nitric oxide.

Although the gold standard for investigating airway inflammation in vivo is bronchoscopy with bronchoalveolar lavage (BAL) and bronchial biopsy, this invasive technique is not suitable for routine use particularly in children [Baraldi et al. 2003; Kharitonov and Barnes, 2001]. Airway inflammation can also be evaluated less invasively by cytospin and supernatant analyses of induced sputum (IS) and the entirely noninvasive collection and analyses of exhaled breath. Although bronchoscopy provides invaluable data on airway inflammation and remodeling [Lommatzsch et al. 2013], the invasive nature of bronchoscopy precludes its repeated use to monitor asthma status. Cytological enumeration of differential leukocyte counts in IS samples, measurement of fractional exhaled nitric oxide concentration (FeNO) and analysis of acidity, temperature and inflammatory mediators in exhaled breath condensate (EBC) are the more readily applicable methods for asthma monitoring. Our previous study adopting the hypothesis-free factor analysis revealed that spirometric indices, FeNO and circulating and EBC biomarkers represented separate and nonoverlapping dimensions in the assessment of childhood asthma [Leung et al. 2005b]. More recently, several research groups published exciting findings from the metabolomics analysis in EBC and urine samples for an array of metabolites that are related to airway inflammation.

A number of novel biomarkers have also been proposed to reflect different aspects of the non-inflammatory component of asthma. Such examples included matrix metalloproteinase-12 in sputum as well as desmosine and isodesmosine in urine, plasma and sputum for degradation of matrix tissue elastin [LaPan et al. 2010; Ma et al. 2011] and airway mucin in sputum for mucus hypersecretion [Jinnai et al. 2010]. Nonetheless, this review will focus predominantly on existing and emerging airway inflammatory biomarkers in IS and FeNO, for which there is some evidence for their clinical utility, as well as discuss the potential roles of EBC and metabolomics analyses in identifying novel asthma biomarkers.

Induced sputum analysis

Airway diseases are conventionally described in terms of spirometry (airway caliber), BHR (airway reactivity) and airway inflammation. The last aspect of airway inflammation is often evaluated directly by the presence of leukocytes in IS samples. International guidelines were published on the standardization of its collection, processing and analyses [Lougheed et al. 2012]. Despite this, there is generally poor correlation between these asthma biomarkers. Spirometry was shown to be normal in many children with severe asthma [Bossley et al. 2009], and the extent of airway inflammation and reactivity correlated poorly [Crimi et al. 1998; Silvestri et al. 2000]. In view of the complex nature of asthma pathogenesis, it is idle to believe that a single biomarker can provide us with all of the information that we need about airway inflammation. Thus, all of the above measures should be assessed in order to provide a complete picture of each asthmatic patient.

It is also important to distinguish between data and hypotheses in this context. Green and colleagues published the most cited study supporting the benefit of a strategy normalizing sputum eosinophils which demonstrated reductions in BHR and asthma exacerbations if sputum eosinophils were measured and treatment titrated accordingly [Green et al. 2002]. However, another adult study failed to show additional benefit from sputum monitoring in mild-to-moderate asthma [Jayaram et al. 2006]. A recent meta-analysis concluded that IS, being technically demanding and more invasive, is not accurate enough to be applied in routine monitoring of childhood asthma [Petsky et al. 2012]. Underpinning the above Leicester sputum study is the assumption that cellular phenotypes in IS are stable with time. Several adult studies reported that sputum cellular phenotype remained stable and, importantly, sputum cell counts could be used to guide the reduction of ICS dosage in noneosinophilic, symptomatic patients [Green et al. 2002; Simpson et al. 2006; van Veen et al. 2009]. However, Al-Samri and colleagues could not detect such benefit in their longitudinal study [Al-Samri et al. 2010].

For childhood asthma, Fleming and coworkers found in a prospective study that asthma inflammation as defined by inflammatory cell populations in IS samples were not stable over the 12-month follow-up period [Fleming et al. 2012]. In this prospective study, 51 children with severe and 28 with mild–moderate asthma were recruited. Fleming and colleagues’ IS samples were classified as eosinophilic (>2.5% eosinophils), neutrophilic (>54% neutrophils), mixed granulocytic (>2.5% eosinophils, >54% neutrophils) or pauci-granulocytic (≤2.5% eosinophils), neutrophilic (≤54% neutrophils) [Cai et al. 1998; Kraft et al. 1996]. The distribution of sputum counts was similar between mild–moderate and severe asthma. Two-fifths of patients were eosinophilic on one occasion and noneosinophilic on repeated samples. There was also poor correlation between biomarkers that reflect different dimensions of asthma: spirometry, BHR and FeNO or IS. Thus, asthma phenotypes at least in children are not stable over time.

The Leicester group subsequently proposed the concept of ‘discordant phenotypes’ of asthma based on cellular phenotypes from sputum monitoring. Broadly speaking, they classified asthmatic patients as having polysymptomatic and pauci-inflammatory and predominantly eosinophilic phenotypes [Haldar et al. 2008]. The former group typically consisted of obese women in whom ICSs could be reduced, whereas patients with marked airway eosinophilia were prone to asthma exacerbations. This concept has implications for phenotype-specific treatment for asthma in adults. It is not helpful to escalate ICS dosage among patients who do not have any evidence of airway inflammation, whereas such an approach may also expose asthma patients unnecessarily to the side effects of anti-inflammatory treatments. Nonetheless, the validity of this approach in childhood asthma is unclear at present in view of the unstable sputum phenotypes over time.

A number of methodological limitations prevent IS from being widely applied in asthma monitoring. There has been difficulty obtaining IS especially from young children, and subjects may experience bronchospasm following inhalation of hypertonic saline. Skilled personnel are needed to process and analyze sputum samples right after the procedure. Finally, there is questionable reproducibility in sputum biomarker measurement when samples are analyzed between centers.

Exhaled nitric oxide measurement

FeNO was first demonstrated to be elevated in asthmatics when compared with healthy controls nearly two decades ago [Persson et al. 1994]. The major advantage of FeNO measurement is that results can be obtained in cooperative subjects, even in preschool children. The detection of nitric oxide can be made instantaneously by online chemiluminescence analyzer or offline through the collection of exhaled breath in Mylar bags. These measurements are simple, sensitive and noninvasive. The American Thoracic Society and European Respiratory Society jointly published a guideline in 2005 that standardizes the collection devices as well as measurement and interpretation of FeNO [American Thoracic Society and European Respiratory Society, 2005], and the clinical application of this guideline was recently revisited [Dweik et al. 2011]. A commercial device for measuring FeNO has been approved by the US Food and Drug Administration for clinical use for nearly 10 years [Silkoff et al. 2004]. FeNO is the most extensively studied asthma biomarker that serves to indicate the degree of airway inflammation. In addition to monitoring of airway inflammation, FeNO can be used to verify treatment adherence and predict asthma exacerbations. There was also early interest in adopting FeNO as a management strategy for asthma both in adults and children. Monitoring FeNO might be used to adjust anti-inflammatory treatment.

FeNO is influenced by subjects’ age, sex and dietary intakes, and subjects should avoid strenuous exercise before such measurement. Our data for both Chinese children and adults also suggested Chinese (and possibly Asians) and those with atopy to have higher FeNO [Chow et al. 2009; Ko et al. 2013; Wong et al. 2005]. Several studies of reference norms for FeNO using standardized methodology have been reported for White adults [Olin et al. 2007; Olivieri et al. 2006; Travers et al. 2007] and children [Baraldi and De Jongste, 2002; Buchvald et al. 2005; Kovesi et al. 2008]. Among Asian children, Saito and colleagues reported a mean FeNO of 25.2 ppb in 215 healthy Japanese children [Saito et al. 2004]. Our group conducted a reference study for FeNO in Chinese adolescents [Wong et al. 2005]. Boys were found to have higher eNO levels than girls (17.0 versus 10.8 ppb). These levels were higher than those reported in White children. These interethnic findings were replicated in other studies involving Asian children [Chng et al. 2005; Yao et al. 2012] and adults [Kim et al. 2010]. Whereas such discrepancy in FeNO may be explained by environmental factors such as pollutant exposures [Leung et al. 2012; Mar et al. 2005], it may also be due to different genetic constitution between Asian and White populations. Our earlier genotyping findings of nitric oxide synthase genes suggested that the frequencies of minor alleles associated with nitric oxide production were substantially lower in Chinese subjects [Leung et al. 2005a; Storm van’s Gravesande et al. 2003; Sy et al. 2012; Wechsler et al. 2000]. Therefore, ethnic-specific references must be considered when setting the cut-off values for assessing asthma status in different populations.

Among >1000 Chinese children, our recent data found the cut-off values at 25 ppb for boys and 15 ppb for girls to have respective sensitivity of 80% and 86% and specificity of 79% and 78% in identifying asthma. Other studies also claimed FeNO to be useful for the diagnosis of asthma [Dupont et al. 2003; Smith et al. 2004]. Although the above studies did not support FeNO to be a perfect diagnostic test for asthma, many existing biomarkers including FeNO are helpful to rule out such diagnosis. For example, it is unlikely for a child with suspected asthma to have normal spirometry, normal FeNO, no evidence of BHR and pauci-inflammatory sputum cytology.

A number of studies have been directed at determining the relationship between FeNO and other markers of airway inflammation [Jatakanon et al. 1998; Steerenberg, 2003; Strunk et al. 2003]. Improvement of asthma with ICS treatment was associated with a reduction in FeNO [Strunk et al. 2003]. FeNO also correlated with other inflammatory biomarkers such as circulating eosinophil count and serum level of eosinophilic cationic protein [Steerenberg, 2003; Strunk et al. 2003]. Payne and colleagues assessed the relationship between FeNO and eosinophilic inflammation in endobronchial biopsies from 31 children with difficult asthma [Payne et al. 2001]. They found significant correlation between FeNO and histological ‘eosinophil score’. Jones and colleagues reported that FeNO was useful in predicting loss of control in mild-to-moderate asthma when ICSs were withdrawn [Jones et al. 2001]. Changes in FeNO correlated significantly with asthma symptoms, lung function, sputum eosinophils and degree of BHR. Another study found that FeNO correlated with annual rate of asthma exacerbation [Biernacki et al. 2004].

Several clinical trials have provided evidence regarding the utility of FeNO measurement in clinical practice. Smith and colleagues performed a randomized controlled study with 97 asthmatic adults [Smith et al. 2005]. One group was treated conventionally with ICS treatment being adjusted according to asthma symptoms and lung function while the other group relied mainly on regular FeNO measurements. Whereas the rates of asthma exacerbation were similar between the two treatment groups, patients on FeNO-based management required lower maintenance doses of ICS to maintain asthma control. A similar study with 85 children yielded similar conclusions [Pijnenburg et al. 2005]. Nonetheless, another recent study challenged the usefulness of FeNO in routine asthma management. Szefler and colleagues recruited 780 American patients aged 12–20 years with persistent asthma in a randomized, double-blind, parallel-group trial that investigated the effectiveness of a FeNO-based treatment regimen [Szefler et al. 2008]. Compared with a standard regimen based on the guidelines from the National Asthma Education and Prevention Program, patients whose asthma treatments were tailored according to FeNO had similar asthma symptoms, lung function and disease exacerbations during the 46-week treatment period. In contrast to the above adult study [Smith et al. 2005], patients in the FeNO monitoring group received higher doses of ICS than controls. These findings suggested in children and adolescents that the addition of FeNO to conventional asthma management did not result in a clinically significant improvement in asthma control.

Biomarkers in exhaled breath condensate

The availability of EBC as a completely noninvasive technique has created new opportunities for investigating and monitoring airway inflammation. EBC is easily obtained by cooling exhaled air, which also requires minimal passive cooperation. The American Thoracic Society and European Respiratory Society have jointly published recommendations regarding the methodological issues on EBC collection and assay [Horváth et al. 2005]. EBC contains several biocompounds that are believed to reflect airway lining fluid composition. EBC research has thus been extremely active for about two decades. Table 1 summarizes the published EBC biomarkers for asthma. Many inflammatory mediators ranging from leukotrienes, cytokines to growth factors were found in measurable quantities.

Table 1.

Summary of asthma biomarkers in exhaled breath condensate.

Biomarker	Acute asthma(effect size)	Stable asthma(effect size)	References
Acidity (pH)	↓ (2–3 log)	↓ (0.3 log)	[Antus et al. 2010; Carpagno et al. 2004; Carraro et al. 2005; Hunt et al. 2000; Leung et al. 2006]
Ammonium (NH₄⁺)	↓ (1–2 log)	↓ (40–70%)	[Carraro et al. 2005; MacGregor et al. 2005]
Leukotrienes (e.g. LTB₄, cys-LTs)	↑ (2- to 25-fold)	↑ (1.3- to 5-fold)	[Baraldi et al. 2003; Căp et al. 2004; Csoma et al. 2002; Debley et al. 2007; Leung et al. 2004a; Sachs-Olsen et al. 2010; Zanconato et al. 2004]
Eicosanoids (e.g. ratio of eoxins D₄ and E₄ to PA)	NR	↑ (1.5- to 2-fold)	[Sachs-Olsen et al. 2010]
Oxidative stress (e.g. H₂O₂, 8-isoprostane)	↑ (1.5- to 8-fold)	↑ (2- to 4-fold)	[Baraldi et al. 2003; Horváth et al. 1998; Shahid et al. 2005; Zanconato et al. 2004]
Aldehydes (e.g. malondialdehyde, acrolein, n-hexanal [C6])	↑ (1.5-fold)	↑ (2- to 5-fold)	[Corradi et al. 2003, 2004]
Anti-oxidant status (e.g. glutathione)	↓ (60%)	NR	[Corradi et al. 2003]
Nitrosative stress (e.g. nitrite, nitrate, nitrotyrosine, nitrosothiols)	NR	↑ (1.3- to 7-fold)	[Corradi et al. 2001; Hanazawa et al. 2000; Lärstad et al. 2005]
Adenosine	NR	↑ (1.5-fold)	[Huszár et al. 2002]
Cytokines (e.g. IL-4, IL-6, IL-8, IL-10)	NR	↑	[Karakoc et al. 2012; Leung et al. 2005c; Shahid et al. 2002]
Chemokines (e.g. MDC, eotaxin)	NR	↑ (1.5- to 3-fold)	[Ko et al. 2006; Leung et al. 2004b; Zietkowski et al. 2010]
Growth factors (e.g. PDGF)	NR	↑ with ↓FEV₁	[Leung et al. 2005c]
Airway remodeling (e.g. MMP-9)	↑	↑ (1.5-fold)	[Karakoc et al. 2012]
Temperature	NR	↑ (10%)	[Piacentini et al. 2004, 2007]

Cys-LTs, cysteinyl leukotrienes; FEV1, forced expiratory volume in 1 second; H2O2, hydrogen peroxide; IL, interleukin; MDC, macrophage-derived chemokine; MMP-9, matrix metalloprotease-9; NR, not reported; PA, palmitic acid; PDGF, platelet-derived growth factor.

Acidity (pH) was perhaps the most easily measured and widely studied biomarker in EBC [Carpagno et al. 2004]. Acidification of airway surface lining fluid can take place through an increase in free protons due to active excretion of protons by H⁺-ATPase [Inglis et al. 2003] and generation of hypochlorous acid by the reaction between hydrogen peroxide and chloride [Kostikas et al. 2002]. Airway acidification can also be caused by decrease in alkaline-buffering capacity due to suppression of glutaminase expression and activity in the presence of pro-inflammatory cytokines such as tumor necrosis factor-α and interferon-γ that reduced ammonia production [Hunt et al. 2002; MacGregor et al. 2005]. Such airway acidification increases mucous viscosity [Holma, 1985], lowers ciliary beat frequency [Clary-Meinesz et al. 1998] and stimulates smooth muscle contraction [Ricciardolo et al. 1999]. Many demographic, personal and technical factors affect pH and other asthma biomarkers in EBC [Leung et al. 2006]. In a large dataset of 404 healthy subjects, Paget-Brown and colleagues found median and interquartile range of EBC pH to be 8.0 and 7.8–8.1, respectively. Such biomarker was not influenced by age, sex or race [Paget-Brown et al. 2006]. Despite these attractions, a British birth cohort did not find this biomarker to be useful in differentiating between children with and without parentally reported symptoms suggestive of asthma [Nicolaou et al. 2006].

Allergic asthma is characterized by increased activity of type 2 T-helper (Th2) lymphocytes. Our group was amongst the first to report significant associations between thymus and activation-regulated chemokine (TARC), chemotactic for Th2 cells, and acute and stable asthma in children Leung et al. 2002; Leung et al. 2003. Although TARC was not measurable in EBC of many patients, we found that two Th2-specific chemokines closely related to TARC were increased in EBC from asthmatics [Ko et al. 2006; Leung et al. 2004b]. Jackson and colleagues measured nitric oxide, 8-isoprostane, hydrogen peroxide, total nitrogen oxides, pH, total protein, phospholipid and keratin in EBC and compared these biomarkers with those detected in BAL [Jackson et al. 2007]. They found that 8-isoprostane, nitrogen oxides and pH were significantly higher in EBC than in BAL, whereas the other biomarkers were either similar or higher in BAL. However, there was no significant correlation between EBC and BAL for any of the biomarkers.

The technical difficulty in measuring low levels of inflammatory molecules is the major obstacle for EBC research [Ko et al. 2007]. It is also important to adjust for each EBC sample the intersubject variations in respiratory droplet dilution on the levels of EBC biomarkers. Exhaled breath temperature was an emerging surrogate marker for airway inflammation in both asthmatic adults [Paredi et al. 2002] and children [Piacentini et al. 2004, 2007], based on the hypothesis that increased mucosal blood flow altered heat loss in patients’ airways. These studies utilized measuring software that focused on either the rising part or end-expiration plateau of the exhaled breath temperature. Carbon monoxide is another exhaled breath biomarker [Zhang et al. 2010].

Despite the initial attractions, none of these EBC biomarkers has been translated into clinical utility due to the following reasons. There was substantial overlap in the levels of biocompounds in EBC between groups because of frequently small difference in the relevant mechanisms. Current evidence also failed to demonstrate that acting on a difference in individual EBC biomarkers resulted in any clinically important impact on patient outcomes. It may be that sophisticated metabolomics [Montuschi et al. 2012] may be required before EBC can come into the clinical arena. Another hurdle regarding the clinical applicability of EBC biomarkers is that their levels will need to be measured in the laboratories, and point-of-care testing for a speedy answer will not be feasible at present.

Metabolomics analysis for asthma-related metabolites

Global metabolic data represents the expression of the multiparametric metabolic response of living systems to pathophysiological stimuli. Metabolomics is the study of small molecules (<1 kDa) generated from cellular metabolic activity. Emerging analytic techniques such as nuclear magnetic resonance (NMR), the electronic nose and mass spectrometry are now more readily available for measuring novel biomarkers for asthma [Adamko et al. 2012]. The first of these methods provides a rapid but accurate metabolic picture with limited sample pretreatment, and is the most widely applicable one for metabolomics analyses of body fluids in asthma. The global metabolic data of biofluids is analyzed and interpreted by means of modern spectroscopic techniques and appropriate statistical approaches [Nicholson et al. 1999; Nicholson and Wilson, 2003; Serkova and Niemann, 2006]. Typically, the NMR spectra observed in a patient sample are matched with a database of known metabolites with referenced spectral resonant frequencies or signatures. Spectral resonant data can then be shown on coefficient-of-variation and variables-of-importance plots, and metabolites that vary significantly among patient groups identified using linear discriminant analysis. High-resolution proton NMR spectroscopy is one of the most powerful techniques for metabolite profile detection [Lindon et al. 2003]. This analytical technique provides an objective and reproducible metabolic fingerprint by characterizing a spectrum of the most represented proton-containing low-molecular-mass compounds in a biological fluid. Metabolomics is usually performed on blood and urine but can also be done on other biofluids and cell cultures.

Carraro and colleagues published the proof-of-concept study on the metabolomic analysis of EBC in relation to asthma [Carraro et al. 2007]. A total of 25 children with asthma (with or without ICS treatment) and 11 healthy controls were enrolled to perform FeNO, spirometry and EBC collection. They found that selected signals from NMR spectra, corresponding to oxidized and acetylated compounds, were more successful in identifying asthma than the combination of FeNO and forced expiratory volume in 1-second (86% versus 81%). This study and another one first reported the repeatability of measuring metabolites in biofluids by NMR spectroscopy [Carraro et al. 2007; Dumas et al. 2006]. For other biofluids, Saude and coworkers reported correlation between urine metabolites and airway dysfunction in a guinea pig model of asthma by NMR spectroscopic analysis [Saude et al. 2009]. The same group proceeded with a human study of 135 children, which showed urine metabolomic data to be 94% accurate in diagnosing asthma [Saude et al. 2011].

The electronic nose is another emerging and noninvasive method for studying metabolomics. This technology combines responses from an array of nanosensors reacting to the different fractions of the volatile organic compounds (VOCs) in breath to generate a specific fingerprint or ‘breathprint’ [Friedrich, 2009; Lewis, 2004]. Such a breath signal is then analyzed real-time by pattern recognition algorithms. Using this technology, Fens and coworkers were able to distinguish between patients with asthma and chronic obstructive pulmonary disease and controls based on the detection of VOCs in EBC [Fens et al. 2009]. Metabolomics not only allows the detection of known metabolites but also the prediction of unknown metabolites and novel biomarkers. The airway biochemical fingerprints thus obtained would facilitate the discovery of asthma biomarkers and unravel new metabolic pathways in asthma pathophysiology.

Conclusions

Both IS and FeNO play useful roles in asthma monitoring. It is highly unlikely that the former will be the be-all and end-all of asthma management. The information thus obtained will only form part of the clinical picture. Nonetheless, the gulf between ‘I can measure it in a cross-sectional study’ and ‘I can show that it is useful in a longitudinal or intervention study’ has not been bridged for most of the asthma biomarkers. Longitudinal studies with focused hypotheses and well-designed protocols are needed to establish the roles of these biomarkers in asthma management. There is no diagnostic test for asthma; but many existing biomarkers are helpful in ruling out the diagnosis. The measurement of biomarkers in EBC remains a research tool at present, and the lack of adequate sensitivity of current laboratory assays is the main limitation for biomarker discovery in EBC. Emerging evidence suggests that metabolomic profiling of EBC or urine provides more clinically useful information regarding asthma status than the quantitation of single biomarkers.

Footnotes

Funding

This work was supported by the Research Grants Council General Research Fund (grant number 470909) and the Health and Health Services Research Fund (grant number 06070261) of Hong Kong SAR Government, the Research Committee Group Research Scheme (grant numbers 3110034, 3110060 and 3110087) and the Respiratory Research Fund of the Chinese University of Hong Kong and a Hong Kong Lung Foundation Research Grant.

Conflict of interest statement

The authors have no conflicts of interest to declare.

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Recent advances in asthma biomarker research

Abstract

Keywords

Introduction

Induced sputum analysis

Exhaled nitric oxide measurement

Biomarkers in exhaled breath condensate

Metabolomics analysis for asthma-related metabolites

Conclusions

Footnotes

Funding

Conflict of interest statement

References