Alzheimer’s disease (AD) is diagnosed based upon medical history, neuropsychiatric examination, cerebrospinal fluid analysis, extensive laboratory analyses and cerebral imaging. Diagnosis is time consuming and labour intensive. Parkinson’s disease (PD) is mainly diagnosed on clinical grounds. The primary aim of this study was to differentiate patients suffering from AD, PD and healthy controls by investigating exhaled air with the electronic nose technique. After demonstrating a difference between the three groups the secondary aim was the identification of specific substances responsible for the difference(s) using ion mobility spectroscopy. Thirdly we analysed whether amyloid beta (Aβ) in exhaled breath was causative for the observed differences between patients suffering from AD and healthy controls. We employed novel pulmonary diagnostic tools (electronic nose device/ion-mobility spectrometry) for the identification of patients with neurodegenerative diseases. Specifically, we analysed breath pattern differences in exhaled air of patients with AD, those with PD and healthy controls using the electronic nose device (eNose). Using ion mobility spectrometry (IMS), we identified the compounds responsible for the observed differences in breath patterns. We applied ELISA technique to measure Aβ in exhaled breath condensates. The eNose was able to differentiate between AD, PD and HC correctly. Using IMS, we identified markers that could be used to differentiate healthy controls from patients with AD and PD with an accuracy of 94%. In addition, patients suffering from PD were identified with sensitivity and specificity of 100%. Altogether, 3 AD patients out of 53 participants were misclassified. Although we found Aβ in exhaled breath condensate from both AD and healthy controls, no significant differences between groups were detected. These data may open a new field in the diagnosis of neurodegenerative disease such as Alzheimer’s disease and Parkinson’s disease. Further research is required to evaluate the significance of these pulmonary findings with respect to the pathophysiology of neurodegenerative disorders.

Diagnosis of obstructive sleep apnoea syndrome (OSAS) is technically demanding, cost-intensive and time-consuming. The measurement of volatile organic compounds by an electronic nose is an innovative method that determines distinct molecular patterns of exhaled breath in different patient groups. We addressed the following questions: What is the diagnostic accuracy of an electronic nose in the detection of OSAS and the ability to detect effects of standard therapy in patients with OSAS? Are these results related to changes in distinct markers of airway inflammation and extracellular remodelling?We included 40 OSAS patients and 20 healthy controls. Exhaled breath of all participants was analysed using the Cyranose 320 electronic nose. Pharyngeal washings were performed to sample the upper airway compartment. For statistical analysis linear discriminant analysis was employed.We identified a linear discriminant function separating OSAS from control (p<0.0001). The corresponding area under the receiver-operating curve was 0.85 (95% CI 0.75–0.96; sensitivity 0.93 and specificity 0.7). In pharyngeal washing fluids of OSAS patients, we observed higher levels of α₁-antitrypsin and markers of extracellular remodelling compared to controls.The electronic nose can distinguish between OSAS patients and controls with high accuracy.

Analysis of exhaled breath condensate (EBC) is a noninvasive method to access the epithelial lining fluid of the lungs. Due to standardization problems the method has not entered clinical practice. The aim of the study was to assess the comparability for two commercially available devices in healthy controls. In addition, we assessed different breathing patterns in healthy controls with protein markers to analyze the source of the EBC. EBC was collected from ten subjects using the RTube and ECoScreen Turbo in a randomized crossover design, twice with every device - once in tidal breathing and once in hyperventilation. EBC conductivity, pH, surfactant protein A, Clara cell secretory protein and total protein were assessed. Bland-Altman plots were constructed to display the influence of different devices or breathing patterns and the intra-class correlation coefficient (ICC) was calculated. The volatile organic compound profile was measured using the electronic nose Cyranose 320. For the analysis of these data, the linear discriminant analysis, the Mahalanobis distances and the cross-validation values (CVV) were calculated. Neither the device nor the breathing pattern significantly altered EBC pH or conductivity. ICCs ranged from 0.61 to 0.92 demonstrating moderate to very good agreement. Protein measurements were greatly influenced by breathing pattern, the device used, and the way in which the results were reported. The electronic nose could distinguish between different breathing patterns and devices, resulting in Mahalanobis distances greater than 2 and CVVs ranging from 64% to 87%. EBC pH and (to a lesser extent) EBC conductivity are stable parameters that are not influenced by either the device or the breathing patterns. Protein measurements remain uncertain due to problems of standardization. We conclude that the influence of the breathing maneuver translates into the necessity to keep the volume of ventilated air constant in further studies.