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André F.S. Amaral

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Journal of Allergy and Clinical Immunology

Metabolomics of asthma

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Amaral 1  Title 1 Metabolomics of asthma 2  3 Author 4 André F. S. Amaral, PhD 5  6 Affiliations 7 1. Respiratory Epidemiology, Occupational Medicine and Public Health, National Heart and Lung 8 Institute, Imperial College, London, UK 9 2. MRC-PHE Centre for Environment & Health, London, UK 10  11 Corresponding author’s details 12 André F. S. Amaral 13 Respiratory Epidemiology, Occupational Medicine and Public Health 14 National Heart and Lung Institute, Imperial College London 15 Emmanuel Kaye Building, 1B Manresa Road 16 London SW3 6LR (UK) 17 Tel: +44 (0) 207 594 7940 18 Email: a.amaral@imperial.ac.uk  19 The publisher's final edited version of this article is available at J Allergy Clin Immunol Amaral 2  To the Editor: 20 Two recent publications in this Journal provided interesting reading regarding the metabolomics of 21 asthma. Fitzpatrick et al. (1) and Loureiro et al. (2) identified metabolites and metabolic pathways 22 suggesting that oxidative stress is a factor contributing to asthma severity in children and asthma 23 exacerbation in adults, respectively. Although these findings support a role for oxidative stress in 24 asthma, these studies have some limitations. 25 It is not clear, due to the design of the studies, whether the reported and highlighted metabolites are a 26 proxy for oxidative stress contributing to severe asthma and asthma exacerbation or a consequence of 27 the disease. It is also not completely clear if the findings are driven by use of medication – this is 28 common to both studies, but particularly relevant in the study by Loureiro et al. since samples from 29 the exacerbation period were collected after the treatment provided at the emergency room. The two 30 groups of children from the study by Fitzpatrick et al. are not totally comparable, since the group with 31 severe asthma was almost exclusively non-white, unlike the control group (mild asthma), and racial 32 differences in biological predictors of severe asthma have been previously reported (3). In addition, 33 the use of non-fasting samples raises the question whether the results might have been influenced by 34 diet, since several food items have been shown to associate with specific metabolites (4), and the 35 short-term effects of diet on the metabolome is uncertain. Finally, like most of the previous studies on 36 metabolomics of asthma (table 1), these were pilot studies (small by nature) that could not adjust for 37 potential confounders.  38 Fitzpatrick et al. and Loureiro et al., together with previous studies, have shown several metabolites 39 and pointed out several pathways that could characterize asthma or asthma exacerbation. Not all 40 studies used the same methods (some used NMR, others MS) or sample types (some used serum, 41 others urine or exhaled breath condensate) nor collected fasting samples nor controlled for the use of 42 asthma- and non-asthma-related drugs or comorbidities. This should remind us of what we have seen 43 when large genome-wide association studies failed to replicate tens of SNPs that were previously 44 found associated with disease in small candidate gene studies. Nevertheless, the present findings, as 45 those from previous studies, are excellent candidate metabolites to be replicated in future and larger 46 studies. 47 Amaral 3  In summary, on the basis of the publications by Fitzpatrick et al. and Loureiro et al., some may 48 conclude that we have enough information to move to the development of treatments targeting the 49 oxidative stress-related pathways. Unfortunately, we are still far from understanding the real meaning 50 of these metabolites, and we cannot exclude that some of current findings might be false positives that 51 will not be replicated in larger and better controlled studies. Randomized trials and animal model 52 studies showing the effects of controller drugs on the metabolic profile should provide important 53 information that will help to disentangle the effect of such drugs on the metabolome. 54  55 André F. S. Amaral, PhD 56 From the Respiratory Epidemiology, Occupational Medicine and Public Health, National Heart and 57 Lung Institute, Imperial College, London, UK; and MRC-PHE Centre for Environment & Health, 58 London, UK 59  60 References 61 1. Fitzpatrick AM, Park Y, Brown LA, Jones DP. Children with severe asthma have unique oxidative 62 stress-associated metabolomic profiles. The Journal of allergy and clinical immunology. 63 2014;133:258-61 e8. 64 2. Loureiro CC, Duarte IF, Gomes J, Carrola J, Barros AS, Gil AM, et al. Urinary metabolomic 65 changes as a predictive biomarker of asthma exacerbation. The Journal of allergy and clinical 66 immunology. 2014;133:261-3 e5. 67 3. Gamble C, Talbott E, Youk A, Holguin F, Pitt B, Silveira L, et al. Racial differences in biologic 68 predictors of severe asthma: Data from the Severe Asthma Research Program. The Journal of allergy 69 and clinical immunology. 2010;126:1149-56 e1. 70 4. Floegel A, von Ruesten A, Drogan D, Schulze MB, Prehn C, Adamski J, et al. Variation of serum 71 metabolites related to habitual diet: a targeted metabolomic approach in EPIC-Potsdam. European 72 journal of clinical nutrition. 2013;67:1100-8. 73 Amaral 4  Table 1. Summary of previous publications on metabolomics of asthma and their main findings and issues. First author [year] Study population  Type of sample  Analytical method  Main findings Main issues  Children      Carraro [2007] E1 - 25 asthmatics (17 persistent asthma treated with ICS; 8 intermittent asthma with no ICS in previous month) - 11 healthy age-matched controls Exhaled breath condensate  NMR - Acetylated products (1.7-2.2 ppm spectral region) and oxidized compounds (3.2-3.4 ppm spectral region) discriminated asthmatics from non-asthmatics  - Small sample size - Not clear what metabolites have been identified - Role of ICS in the findings not clear  Saude [2011] E2 - 73 with stable atopic asthma, 67% with ICS - 20 with unstable asthma in emergency room; 50% with ICS - 42 healthy age- and sex-matched (10 confirmed non-atopic) Urine  NMR  - Several metabolites, from several pathways, discriminated asthma exacerbation from stable asthma - Small sample size - Role of ICS in the findings not clear - Treatment given at emergency not mentioned Mattarucchi [2012] E3 - 41 atopic asthmatics (14 with SABA; 16 with low conc. ICS + LABA; 11 with high conc. ICS + LABA) - 12 age-matched non-atopic non-asthmatics Urine  LC-MS  - Low levels of urocanic acid, methyl-imidazoleacetic acid, and a metabolite like isoleucyl-proline in asthmatics - Small sample size - Role of ICS in the findings not clear  Sinha [2012] E4 - 58 asthmatics - 2 healthy, non-asthmatics  Exhaled breath condensate  NMR  - Asthmatics showed lack of ammonium ions - Small sample size - No mention on the use of ICS or any other treatment  Carraro [2013] E5 - 42 atopic asthmatics [31 with non-severe asthma (17 with low-medium conc. ICS alone or combined with LABA mainly; 14 ICS naive); 11 with severe asthma with high conc. ICS (and others) + LABA - 15 healthy Exhaled breath condensate NMR  - High levels of retinoic acid- and deoxyadenosine-related metabolites in severe asthmatics - Low levels of ercalcitriol in severe asthmatics - Low levels of 20-hydroxy-PGF2a, 6-keto-prostaglandin F1a, and thromboxane B2 in severe asthmatics  - Small sample size  - Role of ICS in the findings not clear Gahleitner [2013] E6 - 25 asthmatics (17 persistent asthma treated with ICS; 8 intermittent asthma with no ICS in previous month) - 11 healthy age-matched controls Exhaled breath condensate (fasting)   GC-MS  - Eight metabolites discriminated asthmatics from healthy children: 1-(methylsulfanyl)propane, ethylbenzene, 1,4-dichlorobenzene, 4-isopropenyl-1-methylcyclohexene, - Small sample size  - Role of ICS in the findings not clear  Amaral 5  2-octenal, octadecyne, 1-isopropyl-3-methylbenzene, and 1,7-dimethylnaphtalene  Adults      Sinha [2012] E4 - 7 asthmatics (non-smokers) - 10 healthy non-asthmatics (non-smokers) Exhaled breath condensate  NMR  - Lack of ammonium ions in asthmatics - Small sample size - No mention on the use of ICS or any other treatment  Ibrahim [2013] E7 - 79 asthmatics (77% with ICS)  - 34 controls Exhaled breath condensate NMR - Five spectral regions distinguished asthmatics from controls (AUC = 0.91)  - Small sample size  - Role of ICS in findings not clear - Controls were about 14 years younger than asthmatics  Jung [2013] E8 - 39 asthmatics (77% with ICS - they stopped their medication for at least 3 days before sample collection;  44% were atopic) - 26 healthy controls Serum  NMR  - High levels of methionine, glutamine, histidine, and lower levels of formate, methanol, acetate, choline, O-phosphocholine, arginine, and glucose in asthmatics - Small sample size  - Medication stopped 3 days before sample collection, but ICS-related DNA methylation effect should not be excluded  Loureiro [2013] E9 - Woman with severe persistent allergic asthma treated with omalizumab Urine GC-MS  - Previous to treatment, main metabolites were alkanes. 12 weeks after, aldehydes were the main metabolites  - Just one case  Ried [2013] E10 - 260 asthmatics from the KORA cohort (asthmatics with three definitions: 260 ever asthmatics; 147 non-medicated current asthmatics; 104 medicated asthmatics) - 2,778 never asthmatics (controls)   Serum (fasting)  ESI-MS/MS - High levels of phosphatidylcholines and low levels of lyso-phosphatidylcholines in current asthmatics (similar results for medicated asthma)  - Changes in levels of polyunsaturated phosphatidylcholines were associated with asthma and affected by asthma risk alleles (in PSMD3 and MED24)  - Causality could not be established  ESI-MS/MS = Electrospray ionization tandem mass spectrometry; ICS = inhaled corticosteroids; LC-MS = liquid chromatography mass spectrometry; GC-MS = gas chromatography mass spectrometry; NMR = nuclear magnetic resonance; SABA = short-acting beta agonists; LABA = long-acting beta agonist. For full list of citations (E1 to E10) in this table please see this article’s Online Repository available at www.jacionline.org. Amaral 1  Online Repository material 1  2 Title 3 Metabolomics of asthma 4  5 Author 6 André F. S. Amaral, PhD 7  8 Affiliations 9 1. Respiratory Epidemiology, Occupational Medicine and Public Health, National Heart and Lung 10 Institute, Imperial College, London, UK 11 2. MRC-PHE Centre for Environment & Health, London, UK 12  13 Corresponding author’s details 14 André F. S. Amaral 15 Respiratory Epidemiology, Occupational Medicine and Public Health 16 National Heart and Lung Institute, Imperial College London 17 Emmanuel Kaye Building, 1B Manresa Road 18 London SW3 6LR (UK) 19 Tel: +44 (0) 207 594 7940 20 Email: a.amaral@imperial.ac.uk  21 Amaral 2  References in Table 1 22 E1. Carraro S, Rezzi S, Reniero F, Heberger K, Giordano G, Zanconato S, et al. Metabolomics 23 applied to exhaled breath condensate in childhood asthma. American journal of respiratory and 24 critical care medicine. 2007;175:986-90. 25 E2. Saude EJ, Skappak CD, Regush S, Cook K, Ben-Zvi A, Becker A, et al. Metabolomic profiling of 26 asthma: diagnostic utility of urine nuclear magnetic resonance spectroscopy. The Journal of allergy 27 and clinical immunology. 2011;127:757-64 e1-6. 28 E3. Mattarucchi E, Baraldi E, Guillou C. Metabolomics applied to urine samples in childhood asthma; 29 differentiation between asthma phenotypes and identification of relevant metabolites. Biomedical 30 chromatography : BMC. 2012;26:89-94. 31 E4. Sinha A, Krishnan V, Sethi T, Roy S, Ghosh B, Lodha R, et al. Metabolomic signatures in nuclear 32 magnetic resonance spectra of exhaled breath condensate identify asthma. The European respiratory 33 journal : official journal of the European Society for Clinical Respiratory Physiology. 2012;39:500-2. 34 E5. Carraro S, Giordano G, Reniero F, Carpi D, Stocchero M, Sterk PJ, et al. Asthma severity in 35 childhood and metabolomic profiling of breath condensate. Allergy. 2013;68:110-7. 36 E6. Gahleitner F, Guallar-Hoyas C, Beardsmore CS, Pandya HC, Thomas CP. Metabolomics pilot 37 study to identify volatile organic compound markers of childhood asthma in exhaled breath. 38 Bioanalysis. 2013;5:2239-47. 39 E7. Ibrahim B, Marsden P, Smith JA, Custovic A, Nilsson M, Fowler SJ. Breath metabolomic 40 profiling by nuclear magnetic resonance spectroscopy in asthma. Allergy. 2013;68:1050-6. 41 E8. Jung J, Kim SH, Lee HS, Choi GS, Jung YS, Ryu DH, et al. Serum metabolomics reveals 42 pathways and biomarkers associated with asthma pathogenesis. Clinical and experimental allergy : 43 journal of the British Society for Allergy and Clinical Immunology. 2013;43:425-33. 44 E9. Loureiro CC, Caldeira M, Rocha S, Todo-Bom A, Loureiro M. Metabolomic applied to 45 omalizumab effect in severe asthmatics–a preliminary result. Clinical and Translational Allergy. 46 2013;3:P26. 47 Amaral 3  E10. Ried JS, Baurecht H, Stuckler F, Krumsiek J, Gieger C, Heinrich J, et al. Integrative genetic and 48 metabolite profiling analysis suggests altered phosphatidylcholine metabolism in asthma. Allergy. 49 2013;68:629-36.  50 

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Metabolomics of asthma

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