ena-STUDY-Aberystwyth University-11-05-2017-15:45:23:297-104
Clinical study of saliva metabolomics and microbiomics in respiratory diseases
Chronic and acute respiratory diseases represent major challenges for clinicians with incidence rates continuing to rise throughout the world.  It is important to develop approaches to accurately and rapidly diagnose the types and stages of respiratory disease.  Many approaches are focusing on such as serum or sputum as the basis of a screening programme. Many biomolecules found within human saliva have their origins in the circulatory system and thus, saliva offers a non-invasive diagnostic tool for a number of diseases.  In this study, we assessed how far saliva could be used to reveal key changes. In particular, we have used metabolomics whereby the chemical components of a sample (1000s of biochemicals) can be accurately quantified in a matter of minutes – to assess salvia samples from 93 samples. Saliva has also been used as a biofluid for metabolomics previously used to identify specific oral, breast and pancreatic cancer profilesThe sampled population encompassed 53 COPD patients from Prince Phillip, Gwangli and Bronglais Hospitals in the Hywel Dda Health Board Area. These consisted of 38 age-matched volunteer controls, 8 lung cancer patients and 18 with a range of respiratory diseases.  The samples consisting of 50 ?L of saliva were analysed using metabolomic approaches involving the use of Electrospray Ionisation Mass Spectrometry (FIE-MS) to produce a large dataset 169 samples x 6500 metabolites.  Application of “multivariate” statistical approaches allowed the identification of metabolites that are different in particular sample classes.   Using statistically approaches such as Principal Component Analysis (PCA) we demonstrate that saliva could distinguish between samples from each clinical classification compared to controls.  These results were validated using a cross-validated Receiver operating characteristic (ROC) curve which indicated a diagnostic accuracy of > 0.70. Individual chemicals which are the sources of variation between the sample sets have been identified. Further, we have discovered clear differences in the saliva metabolome of COPD patients at different disease severity as defined by Global Initiative for Chronic Obstructive Lung Disease (GOLD) stage classification.  Therefore, this innovative application of “big-data” based analyses has yield potential biomarkers that could be exploited to inform future clinical practice.
Chronic and acute respiratory diseases represent major challenges for clinicians with incidence rates continuing to rise throughout the world.  It is important to develop approaches to accurately and rapidly diagnose the types and stages of respiratory disease.  Many approaches are focusing on such as serum or sputum as the basis of a screening programme. Many biomolecules found within human saliva have their origins in the circulatory system and thus, saliva offers a non-invasive diagnostic tool for a number of diseases.  In this study, we assessed how far saliva could be used to reveal key changes. In particular, we have used metabolomics whereby the chemical components of a sample (1000s of biochemicals) can be accurately quantified in a matter of minutes – to assess salvia samples from 93 samples. Saliva has also been used as a biofluid for metabolomics previously used to identify specific oral, breast and pancreatic cancer profilesThe sampled population encompassed 53 COPD patients from Prince Phillip, Gwangli and Bronglais Hospitals in the Hywel Dda Health Board Area. These consisted of 38 age-matched volunteer controls, 8 lung cancer patients and 18 with a range of respiratory diseases.  The samples consisting of 50 ?L of saliva were analysed using metabolomic approaches involving the use of Electrospray Ionisation Mass Spectrometry (FIE-MS) to produce a large dataset 169 samples x 6500 metabolites.  Application of “multivariate” statistical approaches allowed the identification of metabolites that are different in particular sample classes.   Using statistically approaches such as Principal Component Analysis (PCA) we demonstrate that saliva could distinguish between samples from each clinical classification compared to controls.  These results were validated using a cross-validated Receiver operating characteristic (ROC) curve which indicated a diagnostic accuracy of > 0.70. Individual chemicals which are the sources of variation between the sample sets have been identified. Further, we have discovered clear differences in the saliva metabolome of COPD patients at different disease severity as defined by Global Initiative for Chronic Obstructive Lung Disease (GOLD) stage classification.  Therefore, this innovative application of “big-data” based analyses has yield potential biomarkers that could be exploited to inform future clinical practice.