Blood and sputum eosinophils in COPD; relationship with bacterial load.

BACKGROUND: Sputum and blood eosinophil counts predict corticosteroid effects in COPD patients. Bacterial infection causes increased airway neutrophilic inflammation. The relationship of eosinophil counts with airway bacterial load in COPD patients is uncertain. We tested the hypothesis that bacterial load and eosinophil counts are inversely related. METHODS: COPD patients were seen at stable state and exacerbation onset. Sputum was processed for quantitative polymerase chain reaction detection of the potentially pathogenic microorganisms (PPM) H. influenzae, M. catarrhalis and S. pneumoniae. PPM positive was defined as total load ≥1 × 104copies/ml. Sputum and whole blood were analysed for differential cell counts. RESULTS: At baseline, bacterial counts were not related to blood eosinophils, but sputum eosinophil % was significantly lower in patients with PPM positive compared to PPM negative samples (medians: 0.5% vs. 1.25% respectively, p = 0.01). Patients with PPM positive samples during an exacerbation had significantly lower blood eosinophil counts at exacerbation compared to baseline (medians: 0.17 × 109/L vs. 0.23 × 109/L respectively, p = 0.008), while no blood eosinophil change was observed with PPM negative samples. CONCLUSIONS: These findings indicate an inverse relationship between bacterial infection and eosinophil counts. Bacterial infection may influence corticosteroid responsiveness by altering the profile of neutrophilic and eosinophilic inflammation.

Relationship between lung function and quantitative computed tomographic parameters of airway remodeling, air trapping, and emphysema in patients with asthma and chronic obstructive pulmonary disease: A single-center study.

BACKGROUND: There is a paucity of studies comparing asthma and chronic obstructive pulmonary disease (COPD) based on thoracic quantitative computed tomographic (QCT) parameters. OBJECTIVES: We sought to compare QCT parameters of airway remodeling, air trapping, and emphysema between asthmatic patients and patients with COPD and explore their relationship with airflow limitation. METHODS: Asthmatic patients (n = 171), patients with COPD (n = 81), and healthy subjects (n = 49) recruited from a single center underwent QCT and clinical characterization. RESULTS: Proximal airway percentage wall area (%WA) was significantly increased in asthmatic patients (62.5% [SD, 2.2]) and patients with COPD (62.7% [SD, 2.3]) compared with that in healthy control subjects (60.3% [SD, 2.2], P < .001). Air trapping measured based on mean lung density expiratory/inspiratory ratio was significantly increased in patients with COPD (mean, 0.922 [SD, 0.037]) and asthmatic patients (mean, 0.852 [SD, 0.061]) compared with that in healthy subjects (mean, 0.816 [SD, 0.066], P < .001). Emphysema assessed based on lung density measured by using Hounsfield units below which 15% of the voxels lie (Perc15) was a feature of COPD only (patients with COPD: mean, -964 [SD, 19.62] vs asthmatic patients: mean, -937 [SD, 22.7] and healthy subjects: mean, -937 [SD, 17.1], P < .001). Multiple regression analyses showed that the strongest predictor of lung function impairment in asthmatic patients was %WA, whereas in the COPD and asthma subgrouped with postbronchodilator FEV1 percent predicted value of less than 80%, it was air trapping. Factor analysis of QCT parameters in asthmatic patients and patients with COPD combined determined 3 components, with %WA, air trapping, and Perc15 values being the highest loading factors. Cluster analysis identified 3 clusters with mild, moderate, or severe lung function impairment with corresponding decreased lung density (Perc15 values) and increased air trapping. CONCLUSIONS: In asthmatic patients and patients with COPD, lung function impairment is strongly associated with air trapping, with a contribution from proximal airway narrowing in asthmatic patients.

Phenotyping the heterogeneity of chronic obstructive pulmonary disease.

COPD (chronic obstructive pulmonary disease) is a heterogeneous disease associated with significant morbidity and mortality. Current diagnostic criteria based on the presence of fixed airflow obstruction and symptoms do not integrate the complex pathological changes occurring within lung, do not define different airway inflammatory patterns, nor do they define different physiological changes or differences in structure as can be defined by imaging. Over recent years, there has been interest in describing this heterogeneity and using this information to subgroup patients into COPD phenotypes. Most approaches to phenotyping have considered disease at a single scale and have not integrated information from different scales (e.g. organ-whole person, tissue-organ, cell-tissue and gene-cell) of disease to provide multi-dimensional phenotypes. Integration of disease biology with clinical expression is critical to improve understanding of this disease. When combined with biostatistical modelling, this information may lead to identification of new drug targets, new end points for clinical trials and targeted treatment for subgroups of COPD patients. It is hoped this will ultimately improve COPD outcomes and represent a move towards personalised medicine. In the present review, we will consider these aspects of multi-dimensional phenotyping in more detail.

Utility of severity assessment tools in COVID-19 pneumonia: a multicentre observational study.

BACKGROUND: Severity scores in pneumonia and sepsis are being applied to SARS-CoV-2 infection. We aimed to assess whether these severity scores are accurate predictors of early adverse outcomes in COVID-19. METHODS: We conducted a multicentre observational study of hospitalised SARS-CoV-2 infection. We assessed risk scores (CURB65, qSOFA, Lac-CURB65, MuLBSTA and NEWS2) in relation to admission to intensive care or death within 7 days of admission, defined as early severe adverse events (ESAE). The 4C Mortality Score was also assessed in a sub-cohort of patients. FINDINGS: In 2,387 participants, the overall mortality was 18%. In all scores examined, increasing score was associated with increased risk of ESAE. Area under the curve (AUC) to predict ESAE for CURB65, qSOFA, Lac-CURB65, MuLBSTA and NEWS2 were 0.61, 0.62, 0.59, 0.59 and 0.68, respectively. AUC to predict ESAE was 0.60 with ISARIC 4C Mortality Score. CONCLUSION: None of the scores examined accurately predicted ESAE in SARS-CoV-2 infection. Non-validated scores should not be used to inform clinical decision making in COVID-19.

Association between pathogens detected using quantitative polymerase chain reaction with airway inflammation in COPD at stable state and exacerbations.

BACKGROUND: Relationships between airway inflammation and respiratory potentially pathogenic microorganisms (PPMs) quantified using quantitative polymerase chain reaction (qPCR) in subjects with COPD are unclear. Our aim was to evaluate mediators of airway inflammation and their association with PPMs in subjects with COPD at stable state and during exacerbations. METHODS: Sputum from 120 stable subjects with COPD was analyzed for bacteriology (colony-forming units; total 16S; and qPCR targeting Haemophilus influenzae, Moraxella catarrhalis, and Streptococcus pneumoniae), differential cell counts, and inflammatory mediators using the Meso-Scale Discovery Platform. Subjects were classified as colonized if any PPM was identified above the threshold of detection by qPCR. Symptoms were quantified using the visual analog scale. RESULTS: At stable state, 60% of subjects were qPCR positive for H influenzae, 48% for M catarrhalis, and 28% for S pneumoniae. Elevated sputum concentrations of IL-1ß, IL-10, and tumor necrosis factor (TNF)-α were detected in samples qPCR positive for either H influenzae or M catarrhalis. Bacterial loads of H influenzae positively correlated with IL-1ß, IL-8, IL-10, TNF-α, and symptoms; and M catarrhalis correlated with IL-10 and TNF-α. H influenzae qPCR bacterial load was an independent predictor of sputum TNF-α and IL-1ß. In 55 subjects with paired exacerbation data, qPCR bacterial load fold change at exacerbation in M catarrhalis but not H influenzae correlated to changes in sputum TNF-α and IL-1ß concentrations. CONCLUSIONS: At stable state, H influenzae is associated with increased airway inflammation in COPD. The relationship between bacterial load changes of specific pathogens and airway inflammation at exacerbation and recovery warrants further investigation.

Systemic and pulmonary inflammation is independent of skeletal muscle changes in patients with chronic obstructive pulmonary disease.

BACKGROUND: Nutritional depletion is an important manifestation of chronic obstructive pulmonary disease (COPD), which has been related to systemic inflammation. It remains unclear to what degree airway inflammation contributes to the presence or progression of nutritional depletion. OBJECTIVES: To determine whether airway inflammation and lung bacterial colonization are related to nutritional status or predict progressive weight loss and muscle atrophy in patients with COPD. METHODS: Body composition using dual energy X-ray absorptiometry, indices of airway inflammation, and bacterial colonization were measured in 234 COPD patients. Systemic inflammation was assessed from serum C reactive protein (CRP) and circulating total and differential leukocyte counts. Nutritional depletion was defined as a body mass index (BMI) less than 21 kg/m(2) and/or fat-free mass index (FFMI) less than 15 or 17 kg/m(2) in women and men, respectively. FFMI was calculated as the fat-free mass (FFM) corrected for body surface area. Measurements were repeated in 94 patients after a median 16-month follow-up. Regression analysis was used to assess the relationships of weight change and FFM change with indices of bacterial colonization and airway and systemic inflammation. RESULTS: Nutritional depletion occurred in 37% of patients. Lung function was worsened in patients with nutritional depletion compared to those without (forced expiratory volume in 1 second 1.17 L versus 1.41 L, mean difference 0.24, 95% confidence interval 0.10 to 0.38, P<0.01). There were no differences in airway inflammation and bacterial colonization in patients with and without nutritional depletion. At baseline, BMI correlated positively with serum CRP (rs=0.14, P=0.04). Change in weight and change in FFM over time could not be predicted from baseline patient characteristics. CONCLUSION: Nutritional depletion and progressive muscle atrophy are not related to airway inflammation or bacterial colonization. Overspill of pulmonary inflammation is not a key driver of muscle atrophy in COPD.