Over the past few decades, a great emphasis has been put into developing and researching noninvasive techniques for monitoring respiratory functions in those with airway inflammatory diseases via biomarkers. The three most promising methods currently being researched are induced sputum analysis, exhaled nitric oxide measurement, and exhaled breath condensate analysis. Clinical applications of these three methods have the potential to transform the way we diagnose, monitor, and ultimately treat those with a variety of airway inflammatory diseases, such as asthma and chronic obstructive pulmonary disease (COPD). The rise in research into noninvasive airway biomonitoring is due to several factors. The ease of administering these tests compared with more invasive procedures, such as the bronchoalveolar lavage, enables physicians to more closely and effectively monitor airway inflammation in their patients, especially in vulnerable populations (children, aging, developing countries) that are not only more likely to suffer from these chronic respiratory diseases but also prove harder to perform invasive medical procedures on. In addition, there is an increasing goal of subclassifying different molecular and inflammatory phenotypes within these diseases, leading to more personalized therapies based on the specific composition of the inflammation. We present here the current understanding of biomarkers in these three techniques regarding asthma and COPD as well as the overall importance of determining airway inflammation composition.
Noninvasive airway monitoring, airway inflammation, asthma, COPD, induced sputum, exhaled nitric oxide, exhaled breath condensate, vulnerable populations, multiple biomarker profile
Joseph Mizrahi and Elizabeth Fireman have nothing to disclose in relation to this manuscript. No funding was received in the publication of this article.
This article is published under the Creative Commons Attribution Noncommercial License, which permits any noncommercial use, distribution, adaptation, and
reproduction provided the original author(s) and source are given appropriate credit.
September 15, 2015 Accepted:
October 09, 2015
Joseph Mizrahi, Sackler School of Medicine, 17 East 62nd Street, New York, New York 10065, US. E: firstname.lastname@example.org
Airway inflammatory diseases are assessed and managed via multiple measurements, including airway caliber, airway responsiveness, and airway inflammation. Exacerbations post diagnosis is also a critical marker in terms of disease status and progression. Currently, diagnosis and monitoring of asthma1 and chronic obstructive pulmonary disease2 (COPD) is based largely on symptom reporting, pulmonary function tests, and bronchial reactivity. However, besides the inherent problems of subjective symptom reporting, it turns out that symptoms and lung function tests do not necessarily reflect the underlying airway inflammation in patients suffering from these diseases.3
While this association between asthma and COPD, and airway inflammation, has been well established, it is only recently that the actual composition of the inflammation (and not merely the presence of inflammation) has been studied in terms of determining the exact pathophysiology of these diseases, as well as to monitor disease progression and treatment.4 For example, with regard to asthma, the degree of eosinophilic airway inflammation has not only been shown to correlate with airway responsiveness, but in fact targeting airway eosinophilia specifically has consistently been associated with fewer asthmatic exacerbations, fewer hospitalizations, and fewer symptoms.5,6 Moreover, the diagnosis of eosinophilic inflammation is more closely associated with a positive response to inhaled corticosteroids (ICS) than any other clinical measure, and thus the presence and degree of airway eosinophilia is critical in assessing one’s possible response to ICS as well as altering ICS doses once treatment begins.4
1. GINA: Global Strategy for Asthma Management and Prevention (Updated 2015), Bethesda, MD: National Institutes of Health, National Heart, Lung, and Blood Institute, 2015.
2. Global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease: Revised 2015. Global Initiative for Chronic Obstructive Lung Disease (GOLD). Available at: www.goldcopd.org (accessed on September 1, 2015).
3. Vijverberg SJ, Hilvering B, Raaijmakers JA, et al., Clinical utility of asthma biomarkers: from bench to bedside, Biologics, 2013;7:199–210.
4. Pavord ID, Eosinophilic phenotypes of airway disease, Ann Am Thorac Soc, 2013;10(Suppl.):S143–9.
5. Barnes PJ, New concepts in the pathogenesis of bronchial hyperresponsiveness and asthma, J Allergy Clin Immunol, 1989;83:1013–26.
6. Fireman E, Toledano B, Buchner N, et al., Simplified detection of eosinophils in induced sputum, Inflamm Res, 2011;60:745–50.
7. Thomsen M, Ingebrigtsen TS, Marott JL, et al., Inflammatory biomarkers and exacerbations in chronic obstructive pulmonary disease, JAMA, 2013;309:2353–61.
8. Green RH, Brightling CE, Mckenna S, et al., Asthma exacerbations and sputum eosinophil counts: a randomised controlled trial, Lancet, 2002;360:1715–21.
9. Fleming L, Tsartsali L, Wilson N, et al., Sputum inflammatory phenotypes are not stable in children with asthma, Thorax, 2012;67:675–81.
10. Sun XW, Gu SY, Li QY, et al., Pulmonary function parameters in high-resolution computed tomography phenotypes of chronic obstructive pulmonary disease, Am J Med Sci, 2015;349:228–33.
11. Miravitlles M, Calle M, Soler-Cataluña JJ, Fenotipos clínicos de la EPOC, Identificación, definición e implicaciones para las guías de tratamiento, Arch Bronconeumol, 2012;48:86–98.
12. Han MK, Agusti A, Calverley PM, et al., Chronic obstructive pulmonary disease phenotypes, Am J Respir Crit Care Med, 2010;182:598–604.
13. Vos T, Global, regional, and national incidence, prevalence, and years lived with disability for 301 acute and chronic diseases and injuries in 188 countries, 1990–2013: a systematic analysis for the Global Burden of Disease Study 2013, Lancet, 2015;386:743–800.
14. Bousquet J, Khaltaev NG, Cruz AA, Global Surveillance, Prevention and Control of Chronic Respiratory Diseases: A Comprehensive Approach, Geneva: World Health Organization, 2007.
15. WHO, WHO methods and data sources for global burden of disease estimates 2000–2011, Geneva: Department of Health Statistics and Information Systems, 2013. Available at: //www.who.int/healthinfo/statistics/ GlobalDALYmethods_2000_2011.pdf (accessed on September 1, 2015)
16. Bonilla S, Kehl S, Kwong KY, et al., School absenteeism in children with asthma in a Los Angeles inner city school, J Pediatr, 2005;147:802–6.
17. Amre DK, Infante-Rivard C, Gautrin D, Malo J, Socioeconomic status and utilization of health care services among asthmatic children, J Asthma, 2002;39:625–31.
18. Lodha R, Puranik M, Kattal N, Kabra SK, Social and economic impact of childhood asthma, Indian J Pediatr, 2003;40:874–9.
19. Kinsella KG, Wan H, An Aging World: 2008, Washington DC: US Dept. of Commerce, Economics and Statistics Administration US. Census Bureau; 2009. Available at: https://www.census.gov/ prod/2009pubs/p95-09-1.pdf (accessed on September 1, 2015).
20. Gibson GJ, Loddenkemper R, Lundback B, Sibille Y, Respiratory health and disease in Europe: the new European Lung White Book, Eur Respir J, 2013;42:559–63.
21. American Thoracic Society—Bronchoalveolar Lavage, n.d. Available at: //www.thoracic.org/professionals/clinicalresources/ critical-care/clinical-education/critical-careprocedures/ bronchoalveolar-lavage.php#prep (accessed on September 1, 2015)
22. Moschino L, Zanconato S, Bozzetto S, et al., Childhood asthma biomarkers: present knowledge and future steps, Paediatr Respir Rev, 2015;16:205–12.
23. Fauci AS, Harrison TR, Harrison’s Principles of Internal Medicine, New York: McGraw-Hill, Medical Publishing Division, 2008;1667.
24. Pizzichini M, Popov T, Efthimiadis A, et al., Spontaneous and induced sputum to measure indices of airway inflammation in asthma, Am J Respir Crit Care Med, 1996;154:866–9.
25. Djukanovic R, Sterk P, Fahy J, Hargreave F, Standardised methodology of sputum induction and processing, Eur Respir J, 2002;20(Suppl. 37):1s–2s.
26. Fireman E, Bliznuk D, Schwarz Y, et al., Biological monitoring of particulate matter accumulated in the lungs of urban asthmatic children in the Tel-Aviv area, Int Arch Occup Environ Health, 2015;88:443–53.
27. Macedo P, Hew M, Torrego A, et al., Inflammatory biomarkers in airways of patients with severe asthma compared with nonsevere asthma, Clin Exp Allergy, 2009;39:1668–76.
28. Lougheed M, Lemiere C, Ducharme F, Canadian Thoracic Society 2012 guideline update: diagnosis and management of asthma in preschoolers, children and adults, Can Respir J, 2012;19:127–64.
29. Shaw J, Vaughan A, Dent A, Biomarkers of progression of chronic obstructive pulmonary disease (COPD), J Thorac Dis, 2014;6:1532–47.
30. Fireman E, Lerman Y, Stark M, et al., Detection of occult lung impairment in welders by induced sputum particles and breath oxidation, Am J Ind Med, 2008;51:503–11.
31. Lerman Y, Segal B, Rochvarger M, et al., Induced-sputum particle size distribution and pulmonary function in foundry workers, Arch Environ Health, 2003;58:565–71.
32. Fireman E, Lerman Y, Ganor E, et al., Induced sputum assessment in New York City firefighters exposed to World Trade Center dust, Environ Health Perspect, 2004;112:1564–9.
33. Wright RJ, Kelly BJ, Programming of respiratory health in childhood: influence of outdoor air pollution, Curr Opin Pediatr, 2013;25:232–9.
34. Fireman E, Bliznuk D, Schwarz Y, et al., Biological monitoring of particulate matter accumulated in the lungs of urban asthmatic children in the Tel-Aviv area. Int Arch Occup Environ Health 2015;88:443–53
35. Erzurum SC, Gaston BM, Biomarkers in asthma, Clin Chest Med, 2012;33:459–71.
36. Fireman E, Toledano B, Buchner N, et al., Simplified detection of eosinophils in induced sputum, Inflamm Res, 2011;60:45–750.
37. Nathan C, Xie Q, Nitric oxide synthases: roles, tolls, and controls, Cell, 1994;78:915–8.
38. Kharitonov S, Yates D, Robbins R, et al., Increased nitric oxide in exhaled air of asthmatic patients, Lancet, 1994;343:33–5.
39. American Thoracic Society, European Respiratory Society, ATS/ ERS recommendations for standardized procedures for the online and offline measurement of exhaled lower respiratory nitric oxide and nasal nitric oxide, Am J Respir Crit Care Med, 2005;171:912–30.
40. Berry MA, Shaw DE, Green RH, et al., The use of exhaled nitric oxide concentration to identify eosinophilic airway inflammation: an observational study in adults with asthma, Clin Exp Allergy, 2005;35:1175–9.
41. Cowan D, Taylor D, Peterson L, et al., Biomarker-based asthma phenotypes of corticosteroid response, J Allergy Clin Immunol, 2015;135:877–83.
42. Van Der Valk RJ, Baraldi E, Stern G, et al., Daily exhaled nitric oxide measurements and asthma exacerbations in children, Allergy, 2011;67:265–71.
43. Angelis N, Porpodis K, Zarogoulidis P, Airway inflammation in chronic obstructive pulmonary disease, J Thorac Dis, 2014;6:167–72.
44. Gelb AF, Barnes PJ, George SC, et al., Review of exhaled nitric oxide in chronic obstructive pulmonary disease, J Breath Res, 2012;6:047101.
45. Horvath I, Hunt J, Barnes PJ, et al., Exhaled breath condensate: methodological recommendations and unresolved questions, Eur Respir J, 2005;26:523–48.
46. Koczulla R, Dragonieri S, Schot R, et al., Comparison of exhaled breath condensate pH using two commercially available devices in healthy controls, asthma and COPD patients, Respir Res, 2009;10:78.
47. Konstantinidi EM, Lappas AS, Tzortzi AS, Behrakis PK, Exhaled breath condensate: technical and diagnostic aspects, Scientific World Journal, 2015:2015:1–25.
48. Benor S, Alcalay Y, Domany K, et al., Ultrafine particle content in exhaled breath condensate in airways of asthmatic children, J Breath Res, 2015;9:026001.
49. Leung TF, Ko FW, Wong GW, Recent advances in asthma biomarker research, Ther Adv Respir Dis, 2013;7:297–308.
50. Lee JS, Shin JH, Hwang J, et al., Malondialdehyde and 3- nitrotyrosine in exhaled breath condensate in retired elderly coal miners with chronic obstructive pulmonary disease, Saf Health Work, 2014;5:91–6.
51. Loukides S, Kontogianni K, Hillas G, Horvath I, Exhaled breath condensate in asthma: from bench to bedside, Curr Med Chem, 2011;18:1432–43.
52. Jia G, Erickson RW, Choy DF, et al., Periostin is a systemic biomarker of eosinophilic airway inflammation in asthmatic patients, J Allergy Clin Immunol, 2012;130:647–54.
53. Moffatt MF, Gut IG, Demenais F, et al., GABRIEL Consortium, A large-scale, consortium-based genomewide association study of asthma, N Engl J Med, 2010;363:1211–21.
54. Adamko DJ, Sykes BD, Rowe BH, The metabolomics of asthma: novel diagnostic potential, Chest, 2012;141:1295–302.
55. Severe Asthma Research Network (SARP). Available at: // www.severeasthma.org/ (accessed on September 1, 2015).
56. Home | U-BIOPRED. Available at: //www.europeanlung.org/ en/projects-and-research/projects/u-biopred/home (accessed on September 1, 2015).