Inflammatory cytokine response to exercise in alpha-1-antitrypsin deficient COPD patients ‘on’ or ‘off’ augmentation therapy

Background: There is still limited information on systemic inflammation in alpha-1-antitrypsin-deficient (AATD) COPD patients and what effect alpha-1-antitrypsin augmentation therapy and/or exercise might have on circulating inflammatory cytokines. We hypothesized that AATD COPD patients on augmentation therapy (AATD + AUG) would have lower circulating and skeletal muscle inflammatory cytokines compared to AATD COPD patients not receiving augmentation therapy (AATD-AUG) and/or the typical non-AATD (COPD) patient. We also hypothesized that cytokine response to exercise would be lower in AATD + AUG compared to AATD-AUG or COPD subjects. Methods: Arterial and femoral venous concentration and skeletal muscle expression of TNFα, IL-6, IL-1β and CRP were measured at rest, during and up to 4-hours after 50% maximal 1-hour knee extensor exercise in all COPD patient groups, including 2 additional groups (i.e. AATD with normal lung function, and healthy age-/activity-matched controls). Results: Circulating CRP was higher in AATD + AUG (4.7 ± 1.6 mg/dL) and AATD-AUG (3.3 ± 1.2 mg/dL) compared to healthy controls (1.5 ± 0.3 mg/dL, p < 0.05), but lower in AATD compared to non-AATD-COPD patients (6.1 ± 2.6 mg/dL, p < 0.05). TNFα, IL-6 and IL-1β were significantly increased by 1.7-, 1.7-, and 4.7-fold, respectively, in non-AATD COPD compared to AATD COPD (p < 0.05), and 1.3-, 1.7-, and 2.2-fold, respectively, compared to healthy subjects (p < 0.05). Skeletal muscle TNFα was on average 3–4 fold greater in AATD-AUG compared to the other groups (p < 0.05). Exercise showed no effect on these cytokines in any of our patient groups. Conclusion: These data show that AATD COPD patients do not experience the same chronic systemic inflammation and exhibit reduced inflammation compared to non-AATD COPD patients. Augmentation therapy may help to improve muscle efflux of TNFα and reduce muscle TNFα concentration, but showed no effect on IL-6, IL-1β or CRP

The prevalence of undiagnosed renal failure in a cohort of COPD patients in western Norway

SummaryPatients with COPD are at risk for other comorbid diseases, like heart failure, coronary heart disease, and depression. However, little is known about COPD phenotypes and prevalence of sub-clinical renal failure.433 COPD patients and 233 subjects without COPD, from Western Norway, age 40–75, GOLD stage II–IV, were examined in 2006/07 upon entry to the Bergen COPD Cohort Study. Plasma creatinine was measured in 422 of the COPD patients. The Glomerular Flow Rate (GFR) was determined with the Cockcroft Gault formula, and having a GFR < 60 was defined as renal failure. Examined explanatory factors were sex, age, smoking habits, GOLD stage, hypoxemia, exacerbation history, cachexia, use of daily inhaled steroids, Charlson comorbidity score, use of ACE inhibitors and/or ARBs, and the inflammatory plasma markers C-reactive protein (CRP), soluble tumor necrosis factor receptor 1 (sTNF-R1) and neutrophil gelatinase associated lipocalin (NGAL). Associations between explanatory variables and renal failure were examined by a logistic regression analysis.The prevalence of having GFR < 60 was 9.6% in female COPD patients and 5.1% in male COPD patients (p = 0.08). In multivariable analysis, female sex, higher age, cachexia, and the inflammatory markers sTNF-R1 and NGAL were all independently associated with a higher risk for renal failure, whereas use of inhaled steroids, Charlson score, GOLD stage, respiratory failure, and exacerbation frequency were not.Undiagnosed renal failure is a concern particularly in elderly COPD patients and COPD patients with cachexia

Data from: Laboratory contamination in airway microbiome studies

Background: The low bacterial load in samples acquired from the lungs, have made studies on the airway microbiome vulnerable to contamination from bacterial DNA introduced during sampling and laboratory processing. We have examined the impact of laboratory contamination on samples collected from the lower airways by protected (through a sterile catheter) bronchoscopy and explored various in silico approaches to dealing with the contamination post-sequencing. Our analyses included quantitative PCR and targeted amplicon sequencing of the bacterial 16S rRNA gene. Results: The mean bacterial load varied by sample type for the 23 study subjects (oral wash>1st fraction of protected bronchoalveolar lavage>protected specimen brush>2nd fraction of protected bronchoalveolar lavage; p < 0.001). By comparison to a dilution series of know bacterial composition and load, an estimated 10-50% of the bacterial community profiles for lower airway samples could be traced back to contaminating bacterial DNA introduced from the laboratory. We determined the main source of laboratory contaminants to be the DNA extraction kit (FastDNA Spin Kit). The removal of contaminants identified using tools within the Decontam R package appeared to provide a balance between keeping and removing taxa found in both negative controls and study samples. Conclusions: The influence of laboratory contamination will vary across airway microbiome studies. By reporting estimates of contaminant levels and taking use of contaminant identification tools (e.g. the Decontam R package) based on statistical models that limit the subjectivity of the researcher, the accuracy of inter-study comparisons can be improved