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Johanna I Lutter,1 Rudolf A Jörres,2 Tobias Welte,3 Henrik Watz,4 Benjamin Waschki,5 Peter Alter,6 Franziska C Trudzinski,7 Johan Ohlander,1,8 Jürgen Behr,9 Robert Bals,10 Michael Studnicka,11 Rolf Holle,12 Claus F Vogelmeier,6 Kathrin Kahnert9 1Institute of Health Economics and Health Care Management, Helmholtz Zentrum München GmbH - German Research Center for Environmental Health, Comprehensive Pneumology Center Munich (CPC-M), Member of the German Center for Lung Research, Munich, Germany; 2Institute and Outpatient Clinic for Occupational, Social and Environmental Medicine, Comprehensive Pneumology Center Munich (CPC-M), Ludwig-Maximilians-Universität München, Munich 80336, Germany; 3Department of Pneumology, Hannover Medical School, Hannover 30625, Germany; 4Pulmonary Research Institute at Lungen Clinic Grosshansdorf, Airway Research Center North (ARCN), Member of the German Center for Lung Research (DZL), Grosshansdorf 22927, Germany; 5Department of General and Interventional Cardiology, University Heart Center Hamburg, Hamburg, Germany; 6Department of Medicine, Pulmonary and Critical Care Medicine, University Medical Center Giessen and Marburg, Philipps-University Marburg (UMR), Germany, Member of the German Center for Lung Research (DZL), Marburg 35043, Germany; 7Department of Diagnostic & Interventional Radiology, University Hospital of Heidelberg, Heidelberg, Germany; 8Institute for Risk Assessment Sciences, Utrecht University, Utrecht 3584 CM, Netherlands; 9Department of Internal Medicine V, University of Munich (LMU), Comprehensive Pneumology Center, Member of the German Center for Lung Research (DZL), Munich, 80336, Germany; 10Department of Internal Medicine V – Pulmonology, Allergology, Respiratory Intensive Care Medicine, Saarland University Hospital, Homburg 66424, Germany; 11Department of Pneumology, Paracelsus Medical University Salzburg, Universitätsklinikum Salzburg, Salzburg 5020, Austria; 12Institute for Medical Informatics, Biometry and Epidemiology, University Hospital Ludwig-Maximilians-University Munich (LMU), Munich 81377, GermanyCorrespondence: Kathrin KahnertDepartment of Internal Medicine V, University of Munich (LMU), Comprehensive Pneumology Center, Member of the German Center for Lung Research (DZL), Ziemssenstr. 1, Munich 80336, GermanyEmail Kathrin.Kahnert@med.uni-muenchen.deBackground: Beyond smoking, several risk factors for the development of chronic obstructive pulmonary disease (COPD) have been described, among which socioeconomic status including education is of particular interest. We studied the contribution of education to lung function and symptoms relative to smoking in a group of never-smokers with COPD compared to a group of long-time ex-smokers with COPD.Methods: We used baseline data of the COSYCONET cohort, including patients of GOLD grades 1– 4 who were either never-smokers (n=150, age 68.5y, 53.3% female) or ex-smokers (≥ 10 packyears) for at least 10 years (n=616, 68.3y, 29.9% female). Socioeconomic status was analyzed using education level and mortality was assessed over a follow-up period of 4.5 years. Analyses were performed using ANOVA and regression models.Results: Spirometric lung function did not differ between groups, whereas CO diffusing capacity and indicators of lung hyperinflation/air-trapping showed better values in the never-smoker group. In both groups, spirometric lung function depended on the education level, with better values for higher education. Quality of life and 6-MWD were significantly different in never-smokers as well as patients with higher education. Asthma, alpha-1-antitrypsin deficiency, and bronchiectasis were more often reported in never-smokers, and asthma was more often reported in patients with higher education. Higher education was also associated with reduced mortality (hazard ratio 0.46; 95% CI 0.22– 0.98).Conclusion: Overall, in the COSYCONET COPD cohort, differences in functional status between never-smokers and long-time ex-smokers were not large. Compared to that, the dependence on education level was more prominent, with higher education associated with better outcomes, including mortality. These data indicate that non-smoking COPD patients’ socioeconomic factors are relevant and should be taken into account by clinicians.Keywords: COPD, never-smoker, education, socioeconomic status

Alterations of cognitive functions have been described in COPD. Our study aimed to disentangle the relationship between the degree of cognitive function and COPD characteristics including quality of life (QoL).Data from 1969 COPD patients of the COSYCONET cohort (GOLD grades 1–4; 1216 male/ 753 female; mean (SD) age 64.9 ± 8.4 years) were analysed using regression and path analysis. The DemTect screening tool was used to measure cognitive function, and the St. George‘s respiratory questionnaire (SGRQ) to assess disease-specific QoL.DemTect scores were =60 years of age. For statistical reasons, we used the average of both algorithms independent of age in all subsequent analyses. The DemTect scores were associated with oxygen content, 6-min-walking distance (6-MWD), C-reactive protein (CRP), modified Medical Research Council dyspnoea scale (mMRC) and the SGRQ impact score. Conversely, the SGRQ impact score was independently associated with 6-MWD, FVC, mMRC and DemTect. These results were combined into a path analysis model to account for direct and indirect effects. The DemTect score had a small, but independent impact on QoL, irrespective of the inclusion of COPD-specific influencing factors or a diagnosis of cognitive impairment.We conclude that in patients with stable COPD lower oxygen content of blood as a measure of peripheral oxygen supply, lower exercise capacity in terms of 6-MWD, and higher CRP levels were associated with reduced cognitive capacity. Furthermore, a reduction in cognitive capacity was associated with reduced disease-specific quality of life. As a potential clinical implication of this work, we suggest to screen especially patients with low oxygen content and low 6-MWD for cognitive impairment.

Additional file 3. Table A3 Inverse Probability Weighting: Cross-sectional and longitudinal estimates for the association between FEV1 and disease-specific HRQL as measured with the SGRQ.

Additional file 2. Inverse Probability Weighting: Absolute adjusted mean change in SGRQ (a) and EQ VAS (b) after 36 months

Additional file 1. Table A1 Baseline characteristics of COPD patients who completed the 36-month follow-up and Table A2 Clinically important change in HRQL after 36-month.

Norbert Königsdorfer,1 Rudolf A Jörres,1 Sandra Söhler,2 Tobias Welte,3 Jürgen Behr,4 Joachim H Ficker,5 Robert Bals,6 Henrik Watz,7 Johanna I Lutter,8 Tanja Lucke,1 Frank Biertz,9 Peter Alter,10 Claus F Vogelmeier,10 Kathrin Kahnert4 1Institute and Outpatient Clinic for Occupational, Social and Environmental Medicine, Comprehensive Pneumology Center Munich (CPC-M), Ludwig-Maximilians-Universität München, Munich 80336, Germany; 2ASCONET Study Coordination Office, University of Marburg, Marburg 35043, Germany; 3Department of Pneumology, Hannover Medical School, Hannover 30625, Germany; 4Department of Internal Medicine V, University of Munich, Comprehensive Pneumology Center, Member of the German Center for Lung Research, Munich 80336, Germany; 5Department of Respiratory Medicine, Allergology and Sleep Medicine, General Hospital Nuernberg, Paracelsus Medical University, Nuernberg, Germany; 6Department of Internal Medicine V, Pneumology, Allergology, Respiratory Intensive Care Medicine, Saarland University Hospital, Homburg 66424, Germany; 7Pulmonary Research Institute at LungenClinic Grosshansdorf, Airway Research Center North, Member of the German Center for Lung Research, Grosshansdorf 22927, Germany; 8Institute of Health Economics and Health Care Management, Helmholtz Zentrum München GmbH – German Research Center for Environmental Health, Comprehensive Pneumology Center Munich (CPC-M), Munich 85764, Germany; 9Institute for Biostatistics, Hannover Medical School, Hannover 30625, Germany; 10Department of Medicine, Pulmonary and Critical Care Medicine, University Medical Center Giessen and Marburg, Philipps-University Marburg, Marburg 35043, GermanyCorrespondence: Kathrin KahnertDepartment of Internal Medicine V, University of Munich, Comprehensive Pneumology Center, Member of the German Center for Lung Research, Ziemssenstr. 1, Munich 80336, GermanyTel +49 89 4400 2590Email Kathrin.Kahnert@med.uni-muenchen.deBackground: Adherence to COPD medication is often considered to be lower than in other chronic diseases. In view of the frequent comorbidities of COPD, the economic impact of nonadherence and the potential for adverse effects, a direct comparison between the adherence to respiratory and nonrespiratory medication in the same patients seems of particular interest.Objectives: We aimed to investigate the intake of respiratory and nonrespiratory medication in the same patients with COPD and frequent comorbidities.Method: Within the COPD cohort COSYCONET, we contacted 1042 patients, mailing them a list with all medication regarding all their diseases, asking for regular, irregular and non-intake.Results: Valid responses were obtained in 707 patients covering a wide spectrum of drugs. Intake of LABA, LAMA or ICS was regular in 91.9% of patients, even higher for cardiovascular and antidiabetes medication but lower for hyperlipidemia and depression/anxiety medication. Regular intake of respiratory medication did not depend on GOLD groups A-D or grades 1–4, was highest in patients with concomitant cardiovascular disorders and was lowest for concomitant asthma. It was slightly larger for LAMA and LABA administered via combined compared to single inhalers, and lower when similar compounds were prescribed twice. Most differences did not reach statistical significance owing to the overall high adherence.Conclusion: Our results indicate a high adherence to respiratory medication in participants of a COPD cohort, especially in those with cardiovascular comorbidities. Compared to the lower adherence reported in the literature for COPD patients, our observations still suggest some room for improvement, possibly through disease management programs.Keywords: COPD, treatment adherence, respiratory medication, nonrespiratory medication