Paul Brinkman

Pediatric post-COVID condition (PPCC) presents as a heterogeneous disease with a broad spectrum of symptoms. This study aimed to identify distinct phenotypes of PPCC through an unbiased cluster analysis of exhaled metabolites, with the goal of identifying biomarkers to stratify patients. Exhaled breath samples were collected from children with physician-diagnosed PPCC. An unsupervised clustering approach was applied to the exhaled breath metabolites, and the resulting clusters were compared with clinical variables. Sparse partial least squares-discriminant analysis (sPLS-DA) was applied to find most discriminative metabolites between clusters. A total of 54 children were included and categorized into two clusters. Compared to Cluster 1 ( n = 38), Cluster 2 ( n = 16) consisted predominantly of older girls (69%) with a median age of 16 years and exhibited more severe PPCC-related outcomes, including higher PROMIS fatigue scores. Six volatile organic compounds (VOCs) were identified as biomarkers that effectively differentiated the two clusters. These VOCs, previously reported in the literature, highlight metabolic and inflammatory disruptions and demonstrated high discriminatory performance (area under the receiver operating characteristic curve (AUROCC) = 1). This study found two distinct phenotypes of PPCC, and identified six discriminating VOCs, underscoring the potential of VOCs as non-invasive biomarkers for disease stratification in PPCC. While it could be a building block towards a better understanding of the metabolic disruptions underlying PPCC, further research with larger patient cohorts is necessary to elucidate the mechanisms driving these differences.

Interstitial lung disease (ILD) encompasses pulmonary disorders characterised by varying degrees of inflammation and/or fibrosis. The presence and extent of these pulmonary abnormalities on (high-resolution) computed tomography (CT) have consequences for diagnosis and treatment; however, inter-observer assessment varies. Analysis of exhaled volatile organic compounds (VOCs) through gas chromatography-mass spectrometry (GC-MS) offers a noninvasive approach to biomarker discovery and pathophysiology understanding. Our study aims to explore the ability of GC-MS-driven exhaled breath analysis to differentiate ILD patients with predominant fibrotic, inflammatory, or a combination of fibrotic and inflammatory pulmonary abnormalities in a training and an external validation cohort. In a multicentre cross-sectional study, patients diagnosed with ILD were recruited. After central review of chest CT scans by independent radiologists, patients were categorised as fibrotic, inflammatory or mixed phenotype group based on the percentage of chest CT scan abnormalities. Breath samples were collected and analysed via GC-MS. Significantly different VOC fragments between groups were selected and used to differentiate groups in the training cohort with sparse partial least squares discriminant analysis. Analyses were validated with patients from an external cohort. 53 patients were included, 21 patients in the fibrotic, 14 in the inflammatory and 18 in the mixed phenotype group. Area under the curve (AUCs) for discrimination between groups ranged from 0.89–1.00 in training cohorts. An attempt to confirm these findings in our external validation cohort resulted in AUCs of 0.63–0.84. Re-evaluation of the training model led to an AUC of 0.78–0.83. This study shows that GC-MS driven exhaled breath analysis towards differentiation of ILD phenotypes is challenging. Current findings emphasise the importance of predefined validation steps during the process of biomarker discovery.

Acid sphingomyelinase deficiency (ASMD) is a rare lysosomal storage disease. The most commonly affected organs are the spleen, the liver, and the lungs. Pulmonary involvement resembles interstitial lung disease and often leads to decreased diffusion capacity of the lungs for carbon monoxide (DLCO). An emerging technique in pulmonary research is the analysis of exhaled breath. The aim of this study was to investigate potential markers of pulmonary involvement in the exhaled breath of adult chronic visceral ASMD patients and to quantify findings on high‐resolution computed tomography (HRCT) of the lungs in order to be able to correlate HRCT findings with (the potential) markers for pulmonary involvement. Fifteen adult, chronic visceral ASMD patients and 34 age‐, sex‐, and smoking habit‐matched healthy controls were recruited and provided two different types of exhaled breath samples: exhaled air and exhaled condensate. Additionally, pulmonary function testing was performed for both patients and healthy controls, and HRCT of the lungs and biochemical markers were available for patients. Exhaled breath samples were analyzed using gas and liquid chromatography‐mass spectrometry (GC–MS and LC–MS respectively). Fifteen compounds of interest were identified based on significant differences between ASMD patients and healthy controls, of which the most promising were 2‐hydroperoxyhexane, 6‐heptyn‐2‐one, and 4‐pentenyl acetate. Other compounds have been described in the context of systemic sclerosis (i.e., acetophenone) or lung cancer (i.e., benzaldehyde and dodecane). Some markers were associated with the pathophysiological process of lipid peroxidation (i.e., decane, dodecane and 2‐methylnonane). SPLSDA and AUROCC analyses showed that the model was better able to distinguish the patients with pulmonary involvement from their matched controls than all patients from all controls. Lastly, a quantitative HRCT score was performed and correlated with patients' DLCO (R = −0.74, p = 0.006). The most promising markers based on our analyses (i.e., 2‐hydroperoxyhexane, 6‐heptyn‐2‐one and 4‐pentenyl acetate) have not been described in previous studies in the pulmonary field and might be ASMD‐specific.

Background: Severe acute asthma (SAA) in children can be life-threatening. There has been a significant rise in paediatric intensive care unit (PICU) admissions due to SAA over the past two decades. While asthma is a heterogeneous disease, its underlying pathophysiological pathways remain underexplored. This study aimed to assess the value of non-invasive targeted exhaled breath metabolomics analysis to better characterise SAA. Methods: Breath samples from 17 children admitted to the PICU with SAA (cases) and 27 children with controlled severe asthma (controls) were analysed using thermal desorption gas chromatography–mass spectrometry (TD-GC-MS). Results: A targeted volatile organic compound (VOC) analysis identified 25 compounds, of which 16 were shared between groups. Four VOCs were significantly more often present in SAA, and nine VOCs exhibited higher concentrations in SAA. Longitudinal analysis of VOCs from follow-up samples of 10 cases showed no significant temporal differences, reinforcing the reproducibility of identified biomarkers. Conclusions: This study exemplifies the potential of exhaled breath analysis to provide insights into the molecular background of SAA. Breath metabolomics may enable early recognition of severe asthma attacks and preventive therapeutic interventions in children with severe asthma.

Rationale: Chronic obstructive pulmonary disease (COPD) exacerbations significantly contribute to disease progression, hospitalizations, and decreased quality of life. Early detection of exacerbations through non-invasive methods, such as exhaled volatile organic compounds (VOCs), could enable timely interventions. This study aimed to identify and validate candidate VOC biomarkers that are associated with exacerbations and stable phases of COPD, and could contribute to the development of a breath-based monitoring device. Methods: A systematic review was conducted to identify VOCs associated with COPD and exacerbations. VOCs were selected as candidate biomarkers if they were reported in at least two studies by different research groups. These VOCs were then validated using longitudinal exhaled breath data from the TEXACOLD study, where exhaled breath samples were collected at baseline, during exacerbation, and at follow-up in 14 COPD patients. Sparse Partial Least Squares-Discriminant Analysis (sPLS-DA) was applied to differentiate between samples collected during exacerbation and those at stable phases. Diagnostic accuracy was assessed using Receiver Operating Characteristic (ROC) curves. Results: The systematic review identified nine candidate VOCs. Three were excluded from validation because their dataset overlapped with one used in one of the included review studies. Validation confirmed the discriminatory power of a composite model of these six VOCs, achieving an area under the ROC curve of 0.98, a diagnostic accuracy of 94.3% and a sensitivity of 0.97 and a specificity of 0.93. Conclusion: This study demonstrates that exhaled VOCs can differentiate between exacerbations and stable phases in COPD patients. The validated biomarkers hold promise for future clinical applications, particularly in the development of a non-invasive, breath-based monitoring device for early detection and management of COPD exacerbations, potentially reducing hospitalizations and improving patient outcomes.

The exhaled breath represents an ideal matrix for noninvasive biomarker discovery, and exhaled metabolomics have the potential to be clinically useful in the era of precision medicine. In this concise translational review, we specifically address volatile organic compounds in the breath, with a view toward fulfilling the promise of these as actionable biomarkers, in particular, for lung diseases. We review the literature paying attention to seminal work linked to key milestones in breath research; discuss potential applications for breath biomarkers across disease areas and healthcare systems, including the perspectives of industry; and outline critical aspects of study design that will need to be considered for any pivotal research going forward if breath analysis is to provide robust validated biomarkers that meet the requirements for future clinical implementation.

Background: Lung quantitative computed tomography (qCT) severe asthma clusters have been reported, but their replication and underlying disease mechanisms are unknown. We identified and replicated qCT clusters of severe asthma in two independent asthma cohorts and determined their association with molecular pathways, using radiomultiomics, integrating qCT, multiomics and machine learning/artificial intelligence.Methods: We used consensus clustering on qCT measurements of airway and lung CT scans, performed in 105 severe asthmatic adults from the U-BIOPRED cohort. The same qCT measurements were used to replicate qCT clusters in a subsample of the ATLANTIS asthma cohort (n=97). We performed integrated enrichment analysis using blood, sputum, bronchial biopsies, bronchial brushings and nasal brushings transcriptomics and blood and sputum proteomics to characterise radiomultiomic-associated clusters (RACs).Results: qCT clusters and clinical features in U-BIOPRED were replicated in the matched ATLANTIS cohort. In the U-BIOPRED cohort, RAC1 (n=30) was predominantly female with elevated body mass index, mild airflow limitation, decreased CT lung volume and increased lung density and upregulation of the complement pathway. RAC2 (n=34) subjects had airway wall thickness and a mild degree of airflow limitation, with upregulation of proliferative pathways including neurotrophic receptor tyrosine kinase 2/tyrosine kinase receptor B, and downregulation of semaphorin pathways. RAC3 (n=41) showed increased lung attenuation area and air trapping, severe airflow limitation, hyperinflation, and upregulation of cytokine signalling and signalling by interleukin pathways, and matrix metallopeptidase 1, 2 and 9.Conclusions: U-BIOPRED severe asthma qCT clusters were replicated in a matched independent asthmatic cohort and associated with specific molecular pathways. Radiomultiomics might represent a novel strategy to identify new molecular pathways in asthma pathobiology.

Background: Underlying immunological mechanisms in children with moderate-to-severe asthma are complex and unclear. We aimed to investigate the association between blood inflammatory parameters and asthma burden in children with moderate-to-severe asthma.Methods: Blood inflammatory parameters (eosinophil and neutrophil counts and inflammatory mediators using multiplex immunoassay technology) were measured in children (6–17 years) with moderate-to-severe asthma from the SysPharmPediA cohort across four European countries. Based upon low/high blood eosinophil (LBE/HBE) counts of </≥0.3×10⁹·L⁻¹, respectively and low/high blood neutrophil (LBN/HBN) counts of </≥4×10⁹·L⁻¹, respectively, mixed (HBE-HBN), eosinophilic (HBE-LBN), neutrophilic (LBE-HBN) and paucigranulocytic (LBE-LBN) phenotypes were defined. Inflammatory mediator profiles and burden of disease (asthma control status, exacerbations and school days missed in the past year) were compared between phenotypes using adjusted logistic regression models.Results: Among 126 included children (41% girls and mean (sd) age of 11.94 (2.76)), 22%, 44%, 11% and 23% were classified as mixed, eosinophilic, neutrophilic and paucigranulocytic phenotypes, respectively. Neutrophilic children had the lowest lung function (forced expiratory volume in 1 s % predicted pre-salbutamol) compared with other groups. Children with mixed asthma were most often uncontrolled and had the highest asthma-related school absence in the past year. Interleukin (IL)-6 and matrix metalloproteinase-9 levels were significantly higher in patients with mixed or neutrophilic asthma, whereas tissue inhibitor of metalloproteinase-2 was lower in patients with neutrophilic asthma compared with eosinophilic or paucigranulocytic asthma. IL-5 was increased in eosinophilic group compared with the neutrophilic and paucigranulocytic groups, irrespective of the chosen cut-off for eosinophilia.Conclusion: Differences in asthma burden-related clinical expression and distinct blood inflammatory mediator profiles were found between phenotypes, highlighting implications for optimising personalised treatment and management strategies in children with moderate-to-severe asthma.

Analyzing exhaled breath for volatile organic compounds (VOCs) using thermal desorption–gas chromatography–mass spectrometry (TD-GC-MS) offers a non-invasive diagnostic approach for various diseases. Despite its promise, the method faces challenges like sampling heterogeneity and high costs. Following the European Respiratory Society’s advocacy for methodological standardization, we developed the SPIRITAS (Standardized Product for Inexpensive Respiratory InvesTigation: A breath Sampler), a low-cost, disposable breath sampler. This study evaluates the SPIRITAS’s effectiveness in detecting targeted VOCs. We tested the SPIRITAS using the Peppermint Experiment, a standardized protocol that allows for comparison between different breath sampling and analytical practices by assessing the ability to detect five peppermint-specific VOCs after ingestion of a 200-milligram peppermint oil capsule. We included ten subjects and performed six breath samples per participant, including a baseline measurement taken before ingestion. We used the Wilcoxon signed-rank test to evaluate whether baseline values were significantly lower than the peak values of the targeted VOCs. Additionally, we conducted an experiment utilizing humidified medical-grade air to identify any VOCs attributable to the SPIRITAS setup itself. Results showed successful detection of four out of five targeted “peppermint-associated” VOCs: alpha-pinene (p ≤ 0.01), beta-pinene (p ≤ 0.01), menthone (p = 0.01), and menthol (p = 0.02), indicating significant differences between the baseline and peak values in the volunteers’ breath. However, detection of eucalyptol was inconsistent. In addition, we identified 16 VOCs that were released by the SPIRITAS, one of which remains unidentified. Our findings underscore the SPIRITAS’s potential for clinical applications, paving the way for broader biomarker research. The combination of ease of use, low cost, reduced risk of contamination, and standardization makes SPIRITAS very suitable for large-scale international studies. Furthermore, we have demonstrated the SPIRITAS’s effectiveness in detecting specific VOCs and identified 16 compounds originating from the SPIRITAS, ensuring that these compounds would not be mis-qualified as potential biomarkers in future clinical studies.

Volatile organic compounds (VOCs) might be associated with pulmonary oxygen toxicity (POT). This pilot study aims to identify VOCs linked to oxidative stress employing an in vitro model of alveolar basal epithelial cells exposed to hyperbaric and hyperoxic conditions. In addition, the feasibility of this in vitro model for POT biomarker research was evaluated. The hyperbaric exposure protocol, similar to the U.S. Navy Treatment Table 6, was conducted on human alveolar basal epithelial cells, and the headspace VOCs were analyzed using gas chromatography–mass spectrometry. Three compounds (nonane [p = 0.005], octanal [p = 0.009], and decane [p = 0.018]), of which nonane and decane were also identified in a previous in vivo study with similar hyperbaric exposure, varied significantly between the intervention group which was exposed to 100% oxygen and the control group which was exposed to compressed air. VOC signal intensities were lower in the intervention group, but cellular stress markers (IL8 and LDH) confirmed increased stress and injury in the intervention group. Despite the observed reductions in compound expression, the model holds promise for POT biomarker exploration, emphasizing the need for further investigation into the complex relationship between VOCs and oxidative stress.