Additional file 1: Table S1. of Clinical, physiologic, and radiographic factors contributing to development of hypoxemia in moderate to severe COPD: a cohort study

IRB Approval and Protocol Numbers for COPDGene. (DOCX 26 kb

Additional file 1 of Pectoralis muscle area and mortality in smokers without airflow obstruction

Figure S1. Plot of the pectoralis muscle area (PMA) to paravertebral muscle area (PVMA). The relationship between the muscle groups was significant (R2 = 0.44, P < 0.0001). Table S1 Baseline characteristics of at-risk smokers by quartile of PVMA (N = 3705). (DOCX 207 kb

Characteristics of participants with COPD based upon number of comorbidities.

<p>All values as mean(SD) unless otherwise indicated.</p><p>Characteristics of participants with COPD based upon number of comorbidities.</p

Discrimination measures (AUC) and calibration measures (Hosmer-Lemeshow calibration statistics) for comorbidity count with regards to outcomes of exacerbations, MMRC, and 6MWD, in the SPIROMICS participants.

<p>Above models also include terms for age, gender, race, baseline FEV1, pack-years smoked and current smoking status. Every score above added to “empty” model, with addition of score improving AUC significantly (p<0·001 for all comparisons). The AUCs for the empty models are as follows: SGRQ 0.7741, Exacerbations 0.7223, MMRC 0.7499, 6MWD 0.6970. For associations with outcome, OR for exacerbations represents risk for exacerbation conferred by one point increase in comorbidity score, OR for MMRC represents risk for worse dyspnea score conferred by one point increase in comorbidity score, and β's for SGRQ and 6MWD represent decrement in health status and exercise capacity conferred by one point increase in comorbidity score. All ROCs estimated using logistic regression with outcomes of SGRQ (group mean 35.4, SD 18.9), MMRC (group mean 1.18, SD 0.99) and 6MWD (group mean 395.5, SD 112.5) dichotomized at group mean.</p><p>Discrimination measures (AUC) and calibration measures (Hosmer-Lemeshow calibration statistics) for comorbidity count with regards to outcomes of exacerbations, MMRC, and 6MWD, in the SPIROMICS participants.</p

Areas Under the Curve (AUCs) for discrimination of comorbidity scores with regards to primary outcome SGRQ.

<p>All three scores compared to “empty” model (including age, gender, race, FEV1, pack-years smoked and current smoking status). ROC for empty model is 0.7393.</p

Mean, SD and adjusted associations of comorbidity scores and SGRQ value using COPDGene.

<p>Coefficients adjusted for age, race, FEV1, pack-years smoked, current smoking status and gender.</p><p>Equation for weighted comorbidity score: (4·93*coronary heart disease) + (4·69*diabetes) + (6·53*congestive heart failure) + (5·96*stroke) + (5·13*osteoarthritis) + (4·31*osteoporosis) + (3·24*hypertension) + (2·14*high cholesterol) + (6·45*GERD) + (4·94*stomach ulcers) + (5*obesity) + (8·83*sleep apnea) + (2·75*hay fever) + (3·71*peripheral vascular disease).</p><p>Equation for weighted score based on backwards selection: (2·16*coronary heart disease) + (1·39*diabetes) + (2·37*congestive heart failure) + (4·71*stroke) + (2·35*osteoarthritis) + (3·29*osteoporosis) + (0·89*hypertension) + (4·13*GERD) + (2·48*stomach ulcers) + (2·69*obesity) + (6·49*sleep apnea) + (1·20*hay fever).</p><p>Mean, SD and adjusted associations of comorbidity scores and SGRQ value using COPDGene.</p

COPD: Providing the right treatment for the right patient at the right time.

Chronic Obstructive Pulmonary Disease (COPD) is a common disease associated with significant morbidity and mortality that is both preventable and treatable. However, a major challenge in recognizing, preventing, and treating COPD is understanding its complexity. While COPD has historically been characterized as a disease defined by airflow limitation, we now understand it as a multi-component disease with many clinical phenotypes, systemic manifestations, and associated co-morbidities. Evidence is rapidly emerging in our understanding of the many factors that contribute to the pathogenesis of COPD and the identification of "early" or "pre-COPD" which should provide exciting opportunities for early treatment and disease modification. In addition to breakthroughs in our understanding of the origins of COPD, we are optimizing treatment strategies and delivery of care that are showing impressive benefits in patient-centered outcomes and healthcare utilization. This special issue of Respiratory Medicine, "COPD: Providing the Right Treatment for the Right Patient at the Right Time" is a summary of the proceedings of a conference held in Stresa, Italy in April 2022 that brought together international experts to discuss emerging evidence in COPD and Pulmonary Rehabilitation in honor of a distinguished friend and colleague, Claudio Ferdinando Donor (1948-2021). Claudio was a true pioneer in the field of pulmonary rehabilitation and the comprehensive care of individuals with COPD. He held numerous leadership roles in in the field, provide editorial stewardship of several respiratory journals, authored numerous papers, statement and guidelines in COPD and Pulmonary Rehabilitation, and provided mentorship to many in our field. Claudio's most impressive talent was his ability to organize spectacular conferences and symposia that highlighted cutting edge science and clinical medicine. It is in this spirit that this conference was conceived and planned. These proceedings are divided into 4 sections which highlight crucial areas in the field of COPD: (1) New concepts in COPD pathogenesis; (2) Enhancing outcomes in COPD; (3) Non-pharmacologic management of COPD; and (4) Optimizing delivery of care for COPD. These presentations summarize the newest evidence in the field and capture lively discussion on the exciting future of treating this prevalent and impactful disease. We thank each of the authors for their participation and applaud their efforts toward pushing the envelope in our understanding of COPD and optimizing care for these patients. We believe that this edition is a most fitting tribute to a dear colleague and friend and will prove useful to students, clinicians, and researchers as they continually strive to provide the right treatment for the right patient at the right time. It has been our pleasure and a distinct honor to serve as editors and oversee such wonderful scholarly work

Additional file 1: Tables S1. of Genome-wide imputation study identifies novel HLA locus for pulmonary fibrosis and potential role for auto-immunity in fibrotic idiopathic interstitial pneumonia

205 SNPs Associated with IIP at 5 × 10−8 in Imputation Analysis. Table S2: SNPs with 5×10−8 < Pimputed-adjusted < .0001. Table S3: GWAS-Significant SNPs after Meta-Analysis in All Regions (Most Significant in Region in Bold Type). Table S4: Table of 70 SNPs (46 original, 24 from imputation) Adjusted for Top SNP in Region if More than One SNP in Region. Top SNP Defined in Bold Type in Table S3a. All Cases (Discovery and GWAS) Compared to Replication Controls. Table S5: HLA Allele Imputation Accuracy Summary. Table S6: HLA Allele Association with IIP. Table S7: Chromosome 6p21 genes studied via RNA-seq. Table S8: Differential Expression by Case–control Status. Table S9: Differential Expression by Genotype at rs7887 Among Controls. Table S10: Differential Expression by Imputed Number of Copies of DRB1*15:01 Among Cases (RNA-seq). Table S11: Differential Expression by Imputed Number of Copies of DQB1*06*02 Among Cases (RNA-seq). Table S12: 214 Novel SNPs Meeting Carry-forward Threshold at P < 1 × 10−4 in Imputation Analysis using 1000 Genomes Reference. Figure S1: Q-Q plots of P-values from SNPTest using Imputed Dosage for a) Genotyped SNPs and b) Imputed SNPs Prior to Genomic Control Adjustment Based on Genotyped SNP P-values from GEMMA analysis (see manuscript statistical methods for details). Figure S2: Q-Q plot of imputed (adjusted) p-values (inflation factor = 1.04) after genomic control correction. Figure S3: Imputation GWAS Results Fig. 1: Imputation GWAS results with 1616 cases and 4683 controls under additive model. SNPs above red line were genome-wide significant at P < 5 × 10-8. A subset of these SNPs and SNPs between red and blue lines, corresponding to 5 × 10−8 < P-value < .0001, were selected for follow-up and genotyped in 878 cases and 2017 controls. Figure S4: Histogram of. INFO Scores from SNPTest. Only SNPs with .INFO >0.5 Included. Figure S5: Comparison of SNPTest p-values after genomic control (see manuscript statistical methods) and GEMMA dosage-values. GEMMA dosage p-values computed after initial study complete. (DOCX 8917 kb