Differential diagnosis between newly diagnosed asthma and COPD using exhaled breath condensate metabolomics: a pilot study
- Mauro Maniscalco1,6,
- Debora Paris2,6,
- Dominique J. Melck2,6,
- Antonio Molino3,
- Mauro Carone4,
- Paolo Ruggeri5,
- Gaetano Caramori5 and
- Andrea Motta2⇑
- 1Pulmonary Rehabilitation Unit, ICS Maugeri SpA, IRCCS, Telese Terme, Italy
- 2Institute of Biomolecular Chemistry, National Research Council, Pozzuoli, Italy
- 3Dept of Respiratory Medicine, Monaldi Hospital (Naples), Naples, Italy
- 4Pulmonary Rehabilitation Unit, ICS Maugeri SpA, IRCCS, Cassano delle Murge, Italy
- 5Unità Operativa Complessa di Pneumologia, Dipartimento di Scienze Biomediche, Odontoiatriche e delle Immagini Morfologiche e Funzionali (BIOMORF), Università di Messina, Messina, Italy
- 6M. Maniscalco, D. Paris and D.J. Melck contributed equally to this manuscript
- Andrea Motta, Institute of Biomolecular Chemistry - CNR, Comprensorio Olivetti, Edificio A, Via Campi Flegrei 34, 80078 Pozzuoli (NA), Italy. E-mail: andrea.motta{at}icb.cnr.it
Abstract
NMR profiling of EBC discriminates between asthma and COPD and this may help clinicians with differential diagnosis http://ow.ly/cNGI30hWYAE
To the Editor:
Asthma and chronic obstructive pulmonary disease (COPD) are heterogeneous diseases with high pathological burden and healthcare costs [1–3]. In outpatient clinical practice, an accurate differential diagnosis is often very difficult, particularly in adult smokers, requiring specific lung function tests [4, 5]. Since nuclear magnetic resonance (NMR)-based metabolomics of exhaled breath condensate (EBC) discriminates adults with COPD [6–8] or asthma [9] from healthy subjects, we hypothesised that it is also able to differentiate asthma and COPD patients of different severities.
After approval by the Maugeri ethics committee, we recruited prospectively patients with a new diagnosis of asthma (n=31) and COPD (n=44) according to current Global Initiative for Asthma and Global Initiative for Chronic Obstructive Lung Disease guidelines. Six patients with asthma and nine COPD patients were excluded because of relevant comorbidities that could potentially affect the analysis (nine for the presence of coronary or valvular heart disease, five for the presence of diabetes mellitus, one for hypothyroidism). In addition, the EBC samples obtained from five asthma and three COPD subjects were technically unsuitable for NMR analysis. The final 20 asthma and 32 COPD subjects were used to build the reference statistical model (table 1). Since no a priori analysis was possible, we could only evaluate the adequacy of our sample size a posteriori, estimating a sample size of 17±3 asthma and 23±3 COPD patients. A second cohort was also enrolled for an external blind validation comprising 13 asthma and 20 COPD patients.
EBC collection was achieved with a TURBO-DECCS condenser (Medivac, Pilastrello, Italy) as reported [10]. NMR spectra were recorded at 27°C in a randomised sequential order on a 600-MHz Bruker Avance-III spectrometer (Bruker BioSpin GmbH, Rheinstetten, Germany) equipped with a CryoProbe using standard experiments. Metabolites were identified by resorting to two-dimensional experiments. Within-day, between-day, and technical repeatability, and detection limit were assessed as reported [7, 9].
Proton NMR spectra were automatically data reduced to 390 integral segments (“buckets”), each of 0.02 ppm, using the Bruker AMIX 3.6 software package. Unsupervised principal component analysis (PCA) was first applied. However, to better identify clustering, we used orthogonal projections to latent structures discriminant analysis (OPLS-DA), and the obtained model showed improved predictive and interpretive abilities, and in a permutation test (n=300) revealed no overfit. The model quality was evaluated via the goodness-of-fit (R2) and the goodness-of-prediction (Q2) parameters [11]. Metabolite quantification was obtained using the corresponding normalised buckets. Metabolite statistical significance was determined by parametric (t-test) or non-parametric (Mann−Whitney U-test) tests according to the results of a normality test performed to evaluate each distribution (Shapiro−Wilk, Kolgomorov−Smirnov test). p-values <0.05 were considered as statistically significant.
All EBC classes were homogeneous, as PCA did not detect any subgroup related to the clinical characteristics reported in table 1. OPLS-DA analysis of EBC profiles differentiated asthma and COPD with strong regression (95%, p<0.0013; figure 1a) and high-quality parameters (R2=0.86 and Q2=0.86). COPD patients, compared with asthma patients, show an increase in ethanol (mean±sd 25.56±4.57 μM versus 12.15±3.12 μM; p=0.0119) and methanol (10.67±2.99 μM versus 5.01±2.02 μM; p=0.049), and significantly lower levels of formate (2.63±0.97 μM versus 6.97±1.12 μM; p=0.009) and acetone/acetoin (5.84±1.49 μM versus 12.53±3.01 μM; p=0.0004). The area under the curve (AUC) of the receiver operating characteristic (ROC) curve yielded a value of 0.99 for the model of figure 1a.
The relevance of the model was evaluated using a separate cohort (13 asthma and 20 COPD patients, table 1), which was tested blindly. Its projection on the above statistical model generated the scores of figure 1b. The OPLS-DA classifications shows a strong regression (95%, p<0.0011) and high-quality parameters (R2=0.89 and Q2=0.87), therefore confirming that the model obtained from the training set is actually valid. It correctly identified 12 of 13 asthma patients (92.3% accuracy) and 19 of 20 COPD subjects (95.0% accuracy, 5.0% false-positive results), showing a sensitivity (true positive rate) of 92.3%, and a specificity (true negative rate) of 95.0%. The positive predictive value (probability that the disease is present when the test is positive) is 92.3%, and the negative predictive value (probability that the disease is not present when the test is negative) is 95.0%. The discriminating metabolites were confirmed to be in COPD increased ethanol (26.31±5.01 μM versus 11.38±3.98 μM; p=0.0101) and methanol (11.43±3.34 μM versus 4.82±1.79 μM; p=0.039), and decreased formate (2.97±0.69 M versus 7.45±1.99 μM; p=0.003) and acetone/acetoin (6.04±1.89 μM versus 14.11±3.87 μM; p=0.0009). The AUC of the ROC for the total model of figure 1b was 0.96.
Methanol is metabolised to formaldehyde, which shows a pro-inflammatory action in cells and animal models. Interestingly, methanol concentration increases in lung cancer patients, and notably COPD is characterised by an increased risk of lung carcinoma [12]. Asthma, instead, presents a weaker risk of lung cancer in nonsmokers, and shows high levels of formate. This may be related to the in vitro antiproliferative effect on lung cancer cell lines of formate [13], which, on the contrary, decreases in the sputum of lung cancer patients [14].
Low acetone was observed in the EBC of stable COPD patients compared to healthy nonsmoking and former-smoker subjects with normal lung function [7]; on the contrary, increased level was reported in the exhaled breath of stable COPD patients compared to nonsmoking and smoking subjects with normal lung function [15]. These contrasting results may simply reflect the different sampling techniques. Ethanol and its metabolites are involved in the pathogenesis and progression of COPD [16]. In addition, the main metabolite of ethanol, acetaldehyde, may contribute to the increased risk of lung cancer in COPD [12].
The source of acetoin in our asthma or COPD patients is unknown. It may be the product of the detoxication process of acetaldehyde [17], but may also be a bacterial product produced by both pathogenic and non-pathogenic bacteria [18].
This is the first report demonstrating that the NMR metabolic profiling of EBC can be used to discriminate asthma patients from COPD patients, even in smoking adults, in excellent agreement with the use of an electronic nose [19] or urine metabolomics [20]. Furthermore, the EBC metabolic phenotype (“metabotype”) differentiates asthma and COPD better than, for example, a panel of sputum cytokines [21]. The fact that, in our validation cohort, the model correctly attributed both smoking and nonsmoking asthma patients to the “asthma area” (figure 1b), implies that smoking is not the major factor in the metabolomics differentiation of asthma and COPD. In addition, as clinically expected, significantly more atopic subjects are present in the asthma group. However, the possibility that changes in the NMR profiling of asthma EBC may simply be a fingerprinting of atopy was excluded because of the absence of a related subgroup in the found models.
A potential strength of our study is the use of a validation cohort, since the external validation is the only discriminatory evidence that a calculated model can be clinically valuable, regardless of the reported predictive indices. Additionally, several measures were taken for quality control of the data. For EBC collection we minimised the external influence and contamination, and good within-day, between-day, and technical repeatability were observed. Moreover, no influence of demographic parameters was observed. All patients were well characterised according to current international guidelines, and the differences between the groups with respect to age and lung function parameters reflect those expected from the clinical characteristics of asthma and COPD.
Our study, however, also presents some limitations. First, although the number of samples used is larger than that suggested by backward analysis, the sample size was relatively small. Nevertheless, we were able to distinguish COPD and asthma, identifying subtle differences in the metabolomic pattern.
Second, our cohort is not representative of the whole spectrum of asthma and COPD patients observed in clinical practice. We need to investigate older patients, those with more history of smoking and those with more severe asthma. Likewise, the potential influences of specific drugs (glucocorticoids, theophylline, antileukotrienes, oxygen, antioxidants and antibiotics) on the discriminating power of this approach should also be evaluated. The metabotype potential changes during uncontrolled asthma and symptomatic COPD, and during asthma and COPD exacerbations of different severities should similarly be considered.
Third, future studies should include other control groups such as smokers with normal lung function and patients with bronchiectasis of different aetiologies. We also recognise that a single biological fluid, as the EBC, may not represent the complexity of the metabolic pathways involved in the pathogenesis of these diseases [22].
For all these reasons, we are currently undertaking a comparative study on the metabolic profile of multiple biological fluids (serum, urine, EBC and saliva) obtained from a larger population of COPD and asthma patients, and incorporating the above-mentioned control groups. Notwithstanding the described limitations, we have shown that NMR profiling of EBC discriminates asthma and COPD patients with high sensitivity and specificity, and this may help the clinicians to decrease the number of incorrect diagnosis.
Footnotes
Conflict of interest: None declared.
- Received July 29, 2017.
- Accepted December 28, 2017.
- Copyright ©ERS 2018