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Supplemental material, sj-pdf-1-tar-10.1177_17534666221148663 for Standardized airway wall thickness Pi10 from routine CT scans of COPD patients as imaging biomarker for disease severity, lung function decline, and mortality by Kathrin Kahnert, Rudolf A. Jörres, Hans-Ulrich Kauczor, Peter Alter, Franziska C. Trudzinski, Felix Herth, Bertram Jobst, Oliver Weinheimer, Sebastian Nauck, Pontus Mertsch, Diego Kauffmann-Guerrero, Jürgen Behr, Robert Bals, Henrik Watz, Klaus F. Rabe, Tobias Welte, Claus F. Vogelmeier and Jürgen Biederer in Therapeutic Advances in Respiratory Disease

Supplemental material, sj-docx-2-tar-10.1177_17534666221148663 for Standardized airway wall thickness Pi10 from routine CT scans of COPD patients as imaging biomarker for disease severity, lung function decline, and mortality by Kathrin Kahnert, Rudolf A. Jörres, Hans-Ulrich Kauczor, Peter Alter, Franziska C. Trudzinski, Felix Herth, Bertram Jobst, Oliver Weinheimer, Sebastian Nauck, Pontus Mertsch, Diego Kauffmann-Guerrero, Jürgen Behr, Robert Bals, Henrik Watz, Klaus F. Rabe, Tobias Welte, Claus F. Vogelmeier and Jürgen Biederer in Therapeutic Advances in Respiratory Disease

We studied whether in patients with COPD the use of metformin for diabetes treatment was linked to a pattern of lung function decline consistent with the hypothesis of anti-aging effects of metformin. Patients of GOLD grades 1–4 of the COSYCONET cohort with follow-up data of up to 4.5 y were included. The annual decline in lung function (FEV1, FVC) and CO diffusing capacity (KCO, TLCO) in %predicted at baseline was evaluated for associations with age, sex, BMI, pack-years, smoking status, baseline lung function, exacerbation risk, respiratory symptoms, cardiac disease, as well as metformin-containing therapy compared to patients without diabetes and metformin. Among 2741 patients, 1541 (mean age 64.4 y, 601 female) fulfilled the inclusion criteria. In the group with metformin treatment vs. non-diabetes the mean annual decline in KCO and TLCO was significantly lower (0.2 vs 2.3, 0.8 vs. 2.8%predicted, respectively; p 1 and FVC. These results were confirmed using multiple regression and propensity score analyses. Our findings demonstrate an association between the annual decline of lung diffusing capacity and the intake of metformin in patients with COPD consistent with the hypothesis of anti-aging effects of metformin as reflected in a surrogate marker of emphysema.

In chronic obstructive pulmonary disease (COPD), comorbidities and worse functional status predict worse outcomes, but how these predictors compare with regard to different outcomes is not well studied. We thus compared the role of cardiovascular comorbidities for mortality and exacerbations. Data from baseline and up to four follow-up visits of the COSYCONET cohort were used. Cox or Poisson regression was employed to determine the relationship of predictors to mortality or mean annual exacerbation rate, respectively. Predictors comprised major comorbidities (including cardiovascular disease), lung function (forced expiratory volume in 1 s [FEV1], diffusion capacity for carbon monoxide [TLCO]) and their changes over time, baseline symptoms, exacerbations, physical activity, and cardiovascular medication. Overall, 1817 patients were included. Chronic coronary artery disease (p = 0.005), hypertension (p = 0.044) and the annual decline in TLCO (p = 0.001), but not FEV1 decline, were predictors of mortality. In contrast, the annual decline of FEV1 (p = 0.019) but not that of TLCO or cardiovascular comorbidities were linked to annual exacerbation rate. In conclusion, the presence of chronic coronary artery disease and hypertension were predictors of increased mortality in COPD, but not of increased exacerbation risk. This emphasizes the need for broad diagnostic workup in COPD, including the assessment of cardiovascular comorbidity.Clinical Trials: NCT01245933.

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 1: Table S1. Results of the Random Forest and AdaBoost procedures for different sets of variables included. The table shows the variables in the rank order of importance according to the criterion of the mean decrease in accuracy (Random Forest) and the importance measure defined in the AdaBoost procedure. The overall classification error refers to 10-fold cross-validation in the case of AdaBoost and CHAID. SGRQ4 “I have attacks of wheezing”, SGRQ5 “How many attacks of chest trouble did you have during the last year?”, SGRQ8, “How would you describe your chest condition?”, SGRQ11 sub-item 5 “I have become frail or an invalid because of my chest”, SGRQ12 sub-item 1 “I take a long time to get washed or dressed”, SGRQ12 sub-item 3 “I walk slower than other people, or I stop for rests”, SGRQ12 sub-item 4 “Jobs such as housework take a long time, or I have to stop for rests”, SGRQ13 sub-item 1 “I cannot play sports or games”, SGRQ14 “How does your chest trouble affect you? Table S2. Technical details of the CT assessment. The acquisition protocol is given in the upper part and details on the scanner models in the lower part of the table. *Vendor-specific generic names for Siemens/GE/Philips. Table S3. Items from the “St. George’s Respiratory Questionnaire” (SGRQ) that turned out to be informative in the single decision trees. Table S4. Items from the COPD Assessment test (CAT). Table S5. Modified Medical Research Council (mMRC) scale. This self-rating questionnaire is used to measure the degree of disability that breathlessness poses on day-to-day activities on a scale from 0 to 4.