Predictive models

The discrimination potential offered by the data was evaluated using a widely utilised supervised pattern recognition method of partial least squares discriminant analysis (PLS-DA). A statistical model based on PLS-DA was constructed and validated for classification of asthmatic samples. The results of sample classification are presented in

Table 3-4 in terms of recognition and prediction abilities, representing the percentage of

the samples correctly classified during model training and cross-validation. The overall recognition ability during training of the model was 80% and the prediction ability for model cross-validation was 96.7%. The model therefore shows high predictive capabilities for distinguishing between moderate asthmatics and healthy controls by non- invasive sampling of saliva combined with UHPLC-MS metabolite profiling.

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The model created using moderate asthmatic samples was used to classify mild (early stage) asthmatic samples. The success rate of detection of mild asthma was low (55.6 %). However, this is not a true representation of the predictive capabilities as a model built on moderate asthmatics and healthy individuals was tested on mild asthmatics due to restricted patient numbers. Further refinement of the model is required to distinguish between healthy controls, mild and moderate asthmatics.

Table 3-4 Overview of classification results obtained by PLS-DA model.

Predicted Asthmatic Predicted Controls Accuracy (%) Model Training True Moderate Asthmatic 8 1 88.9 True Controls 5 16 76.2 Recognition ability 80.0 Model cross-validation True Moderate Asthmatic 9 0 100.0 True Controls 1 20 95.2 Recognition ability 96.7

Model testing using mild asthmatics

True Mild Asthmatic 27 15 55.6

True Controls 0 12

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3.5 CONCLUSIONS

In this pilot study, the robust methodology developed, based on protein precipitation and UHPLC-MS has been used in combination with multivariate statistical analysis for the identification of potential metabolomic biomarkers of asthma from saliva. A predictive PLS-DA model was created based on the discriminant metabolites obtained from the comparison of moderate asthmatics versus healthy control samples which clearly distinguished between the two groups. This model was evaluated for the recognition of asthma in samples obtained from mild asthmatic population.

The potential benefits of a simple, non-invasive sampling technique for asthma screening have been highlighted. The technique can be used in conjunction with or as a preliminary alternative to the existing diagnostic tests such as forced expiratory volume (FEV1%), sputum eosinophil count and methacholine challenge. The work presented here demonstrates the advantages and the potential of non-invasively obtained saliva as a biofluid to diagnose and monitor the response to treatment of asthma.

To our knowledge, this is the first study to employ a passive drool saliva test combined with mass spectrometry as a potential diagnostic tool for asthma. We found this approach could discriminate between asthma and health controls; a model derived from these 10 discriminant metabolite ions classified asthma with an accuracy of 80% and 96.7% for model training and cross-validation respectively. These figures compare favourably to current tests used for the diagnosis of asthma. Tests of airflow obstruction are widespread however suffer from limited sensitivity and specificity. One study found the sensitivities and specificities for FEV1/FVC ratio to be 61%, 60% respectively; bronchodilator response 49%, 70% and peak flow amplitude percent mean to be 43%, 75%(Hunter et al. 2002). Due in part to the limited tests available, the diagnosis and assessment of treatment response is still based on symptoms with high rates of misdiagnosis(Luks et al. 2010). More invasive tests such as bronchial challenge or induced sputum are available but are limited by their invasive nature, cost and need for expertise. They also only reflect one aspect of the asthmatic process(Grainge et al. 2011). Despite these tests a proper diagnosis remains a challenge even in specialist asthma clinics(Robinson et al. 2003).

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The benefits of a simple, non-invasive sampling technique for asthma diagnosis are obvious and a great deal of work has focussed on the development of breath sampling, including exhaled nitric oxide (Shaw et al. 2007) , measurement of volatile organic compounds(Montuschi et al. 2007; Ibrahim et al. 2011) and the e-nose (Dragonieri et al. 2009) . The discriminatory ability of these tests varies. Importantly the passive drool technique can be used in conjunction with or as a preliminary alternative to the existing diagnostic tests such as FEV1%, has good discriminatory ability, and provides metabolic information on the status of the airway. The saliva sampling kit is inexpensive and the passive drool technique can be performed in the clinic by a wide range of patients, including children. Samples can also be stored and transported with no need for immediate analysis. The use of UHPLC-MS is increasing in clinical laboratories because of the high throughput and specificity of the technique and the development of mass spectrometry-based assays for the simultaneous determination of panels of biomarkers has been shown to offer a rapid and routine approach to clinical diagnosis.

Our pilot study has similar limitations to others in the field being cross sectional with relatively few participants. We cannot fully exclude a treatment effect, however the patients with asthma were on different treatment regimens and drugs; hence the molecular features responsible for the class separation are most likely representative of the endogenous factors pertaining to asthma and not due to a drug signal. The identification, validation and the biological significance of the discriminant molecular features obtained from the samples were beyond the scope of this pilot but will form part of future work. The discriminant metabolic features identified in the study also need to be validated and quantified using a larger cohort (n>100) of moderate and/or severe asthmatics versus healthy control populations using a targeted metabolite analysis approach.

In conclusion the passive drool technique combined with UHPLC-MS is a novel non- invasive method of accurately differentiating patients with asthma from healthy controls. Further longitudinal work is required to fully assess the discriminatory ability of this test.

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