Faculty Bibliography

RATIONALE/BACKGROUND:Obstructive Sleep Apnea (OSA) is associated with recurrent obstruction, sub-epithelial edema, and airway inflammation. The resultant inflammation may influence or be influenced by the nasal microbiome. OBJECTIVES/OBJECTIVE:To evaluate whether the composition of the nasal microbiota is associated with obstructive sleep apnea and inflammatory biomarkers. METHODS:Two large cohorts were utilized: 1) a discovery cohort of 472 subjects from the WTCSNORE cohort; and 2) a validation cohort of 93 subjects from the Zaragoza Sleep cohort. Sleep apnea was diagnosed using home sleep tests. Nasal lavages were obtained from cohort subjects to measure: 1) microbiome composition (based on 16S rRNA gene sequencing); 2) biomarkers for inflammation (inflammatory cells, IL-8, and IL-6). Longitudinal 3 months samples were obtained in the validation cohort including post-CPAP treatment when indicated. RESULTS:In both cohorts, we identified that: 1) severity of OSA correlated with differences in microbiome diversity and composition; 2) the nasal microbiome of subjects with severe OSA were enriched with Streptococcus, Prevotella, and Veillonella; 3) the nasal microbiome differences were associated with inflammatory biomarkers. Network analysis identified clusters of co-occurring microbes that defined communities. Several common oral commensals (e.g., Streptococcus, Rothia, Veillonella, and Fusobacterium) correlated with apnea-hypopnea index. Three months of treatment with CPAP did not change the composition of the nasal microbiota. CONCLUSIONS:We demonstrate that the presence of an altered microbiome in severe OSA is associated with inflammatory markers. Further experimental approaches to explore causal links are needed.

BACKGROUND:In lung cancer, upregulation of the PI3K pathway is an early event that contributes to cell proliferation, survival, and tissue invasion. Upregulation of this pathway was recently described as associated with enrichment of the lower airways with bacteria identified as oral commensals. We hypothesize that host-microbe interactions in the lower airways of subjects with lung cancer affect known cancer pathways. METHODS:Airway brushes were collected prospectively from subjects with lung nodules at time of diagnostic bronchoscopy, including 39 subjects with final lung cancer diagnoses and 36 subjects with non-cancer diagnosis. Additionally, samples from 10 healthy control subjects were included. 16S rRNA gene amplicon sequencing and paired transcriptome sequencing (RNAseq) were performed on all airway samples. In addition, an in vitro model with airway epithelial cells exposed to bacteria/bacterial products was performed. RESULTS:The composition of the lower airway transcriptome in the cancer patients was significantly different from the controls, which included upregulation of ERK and PI3K signaling pathways. The lower airways of lung cancer patients were enriched for oral taxa (Streptococcus and Veillonella), which was associated with upregulation of the ERK and PI3K signaling pathways. In vitro exposure of airway epithelial cells to Veillonella, Prevotella, and Streptococcus led to upregulation of these same signaling pathways. CONCLUSIONS:The data presented here shows that several transcriptomic signatures previously identified as relevant to lung cancer pathogenesis are associated with enrichment of the lower airway microbiota with oral commensals.

Background: Aspiration is associated with non-tuberculous mycobacterial (NTM) pulmonary disease and airway dysbiosis is associated with increased inflammation. We examined whether NTM disease was associated with a distinct airway microbiota and immune profile.Methods: 297 oral wash and induced sputum samples were collected from 106 participants with respiratory symptoms and imaging abnormalities compatible with NTM. Lower airway samples were obtained in 20 participants undergoing bronchoscopy. 16S rRNA gene and a nested mycobacteriome sequencing approaches characterised microbiota composition. Inflammatory profiles of lower airway samples were also examined.Results: The prevalence of NTM+ cultures was 58%. Few changes were noted in microbiota characteristic or composition in oral wash and sputum samples among groups. Among NTM+ samples, 27% of the lower airway samples were enriched with Mycobacterium A mycobacteriome approach identified Mycobacterium in a greater percentage of samples, including some non-pathogenic strains. In NTM+ lower airway samples, taxa identified as oral commensals were associated with increased inflammatory biomarkers.Conclusions: The 16S rRNA gene sequencing approach is not sensitive in identifying NTM among airway samples which are culture positive. However, associations between lower airway inflammation and microbiota signatures suggest a potential role for these microbes in the inflammatory process in NTM disease.

Four inhaler adherence clusters have been identified using the INCA audio device in COPD patients: (1) regular use/good technique, (2) regular use/frequent technique errors, (3) irregular use/good technique, and (4) irregular use/frequent technique errors. Their relationship with healthcare utilization and mortality was established, but the cost-effectiveness of adherence-enhancing interventions is unknown. In this exploratory study, we aimed to estimate the potential cost-effectiveness of reaching optimal adherence in the three suboptimal adherence clusters, i.e., a theoretical shift of clusters 2, 3, and 4 to cluster 1. Cost-effectiveness was estimated over a 5-year time horizon using the Irish healthcare payer perspective. We used a previously developed COPD health-economic model that was updated with INCA trial data and Irish national economic and epidemiological data. For each cluster, interventions would result in additional quality-adjusted life years gained at reasonable investment. Cost-effectiveness was most favorable in cluster 3, with possible cost savings of €845/annum/person.

Hundreds of millions of people worldwide have asthma and COPD. Current medications to control these chronic respiratory diseases can be administered using inhaler devices, such as the pressurized metered dose inhaler and the dry powder inhaler. Provided that they are used as prescribed, inhalers can improve patient clinical outcomes and quality of life. Poor patient inhaler adherence (both time of use and user technique) is, however, a major clinical concern and is associated with poor disease control, increased hospital admissions, and increased mortality rates, particularly in low- and middle-income countries. There are currently limited methods available to health-care professionals to objectively and remotely monitor patient inhaler adherence. This review describes recent sensor-based technologies that use audio-based approaches that show promising opportunities for monitoring inhaler adherence in clinical practice. This review discusses how one form of sensor-based technology, audio-based monitoring systems, can provide clinically pertinent information regarding patient inhaler use over the course of treatment. Audio-based monitoring can provide health-care professionals with quantitative measurements of the drug delivery of inhalers, signifying a clear clinical advantage over other methods of assessment. Furthermore, objective audio-based adherence measures can improve the predictability of patient outcomes to treatment compared with current standard methods of adherence assessment used in clinical practice. Objective feedback on patient inhaler adherence can be used to personalize treatment to the patient, which may enhance precision medicine in the treatment of chronic respiratory diseases.

In severe asthma, poor control could reflect issues of medication adherence or inhaler technique, or that the condition is refractory. This study aimed to determine if an intervention with (bio)feedback on the features of inhaler use would identify refractory asthma and enhance inhaler technique and adherence.Patients with severe uncontrolled asthma were subjected to a stratified-by-site random block design. The intensive education group received repeated training in inhaler use, adherence and disease management. The intervention group received the same intervention, enhanced by (bio)feedback-guided training. The primary outcome was rate of actual inhaler adherence. Secondary outcomes included a pre-defined assessment of clinical outcome. Outcome assessors were blinded to group allocation. Data were analysed on an intention-to-treat and per-protocol basis.The mean rate of adherence during the third month in the (bio)feedback group (n=111) was higher than that in the enhanced education group (intention-to-treat, n=107; 73% versus 63%; 95% CI 2.8%-17.6%; p=0.02). By the end of the study, asthma was either stable or improved in 54 patients (38%); uncontrolled, but poorly adherent in 52 (35%); and uncontrolled, but adherent in 40 (27%).Repeated feedback significantly improved inhaler adherence. After a programme of adherence and inhaler technique assessment, only 40 patients (27%) were refractory and adherent, and might therefore need add-on therapy.

OBJECTIVE:We derive a novel model-based metric for effective adherence to medication, and validate it using data from the INhaler Compliance Assessment device (INCATM). This technique employs dose timing data to estimate the threshold drug concentration needed to maintain optimal health. METHODS:The parameters of the model are optimised against patient outcome data using maximum likelihood methods. The model is fitted and validated by secondary analysis of two independent datasets from two remote-monitoring studies of adherence, conducted through clinical research centres of 5 Irish hospitals. Training data came from a cohort of asthma patients (~ 47,000 samples from 218 patients). Validation data is from a cohort of 204 patients with COPD recorded between 2014 and 2016. RESULTS:The time above threshold measure is strongly predictive of adverse events (exacerbations) in COPD patients (Odds Ratio of exacerbation = 0.52 per SD increase in adherence, 95% Confidence Interval [0.34-0.79]). This compares well with the best known previous method, the Area Under the dose-time Curve (AUC) (Odds Ratio = 0.69, 95% Confidence Interval [0.48-0.99]). In addition, the fitted value of the dose threshold (0.56 of prescribed dosage) suggests that prescribed doses may be unnecessarily high given good adherence. CONCLUSIONS:The resulting metric accounts for missed doses, dose-timing errors, and errors in inhaler technique, and provides enhanced predictive validity in comparison to previously used measures. In addition, the method allows us to estimate the correct dosage required to achieve the effect of the medication using the patients' own adherence data and outcomes. The adherence score does depend not on sex or other demographic factors suggesting that effective adherence is driven by individual behavioural factors.

OBJECTIVE:), a novel audio-recording device objectively measuring timing and proficiency of inhaler use, against established adherence measures, and explore its discriminant and predictive validity. DESIGN/METHODS:-enabled salmeterol/fluticasone inhaler for one-month post-hospital discharge. MAIN OUTCOME MEASURES/METHODS:(Attempted, Attempted Interval, Actual) adherence correlated with Doses Used Rate, self-reported adherence and prescription refill for concurrent validity. Discriminant validity for reason for admission, cognition and lung function; predictive validity for health status and quality-of-life. RESULTS:adherence. Attempted and Attempted Interval predicted health status, while Doses Used Rate predicted quality-of-life. CONCLUSION/CONCLUSIONS:as a method to identify intentional and unintentional adherence to inhaled medication and facilitate targeted intervention.