Identification of a set of genes in blood associated with the frequent exacerbator phenotype; multivariate analysis using micro-array data 1 year follow up data (n = 106), followed by univariate analysis of 3 year follow up data (n = 46) and univariate analysis of a different population (n = 215) by PCR.

<p>FE =  frequent exacerbators; ZE  =  zero exacerbators.</p

Demographics of the COPD subjects for microarray analysis.

<p>Exacerbations were defined over a 3 year follow up; frequent denotes 2 or more exacerbations each year, zero denotes no exacerbations in any year, and intermediate denotes patients who did not fit the frequent or zero exacerbation phenotype. Blood counts are mean values (X10⁹ cells/L).</p><p>Demographics of the COPD subjects for microarray analysis.</p

The 10 most highly regulated genes in sputum and blood from microarray analysis; a positive fold change = increase in frequent exacerbators compared to zero exacerbators, a negative fold change = decrease in frequent exacerbators compared to zero exacerbators.

<p>Affy. ID  =  affymatrix identification number. There was no overlap between sputum and blood for highly expressed genes.</p><p>The 10 most highly regulated genes in sputum and blood from microarray analysis; a positive fold change  =  increase in frequent exacerbators compared to zero exacerbators, a negative fold change  =  decrease in frequent exacerbators compared to zero exacerbators.</p

Gene expression changes in the 6 gene panel identified by microarray analysis using the phenotype data after 1 year.

<p>The gene expression changes using the phenotype data after 3 years from the microarray population and PCR population are shown. Positive fold change  =  increase in frequent exacerbators compared to zero exacerbators, negative fold change  =  decrease in frequent exacerbators compared to zero exacerbators.</p><p>Gene expression changes in the 6 gene panel identified by microarray analysis using the phenotype data after 1 year.</p

Venn diagram showing the number of differentially regulated genes in blood (fold change +/−1.5 and p<0.01) between frequent exacerbators (F), the intermediate group (I), and zero exacerbators (Z); for example, F vs I denotes number of differentially regulated genes between frequent exacerbators and the intermediate group.

<p>Venn diagram showing the number of differentially regulated genes in blood (fold change +/−1.5 and p<0.01) between frequent exacerbators (F), the intermediate group (I), and zero exacerbators (Z); for example, F vs I denotes number of differentially regulated genes between frequent exacerbators and the intermediate group.</p

GeneGo pathway mapping of the microarray data for the comparison of zero vs frequent exacerbators; the 5 most highly regulated pathways are shown.

<p>The number of genes regulated within each pathway are shown.</p><p>GeneGo pathway mapping of the microarray data for the comparison of zero vs frequent exacerbators; the 5 most highly regulated pathways are shown.</p

Demographics of the COPD subjects for PCR analysis.

<p>Exacerbations were defined over a 3 year follow up; frequent denotes 2 or more exacerbations each year, zero denotes no exacerbations in any year, and intermediate denotes patients who did not fit the frequent or zero exacerbation phenotype. Blood counts are mean values (X10⁹ cells/L).</p><p>Demographics of the COPD subjects for PCR analysis.</p

Additional file 1: Table S1. of Multiple biomarkers predict disease severity, progression and mortality in COPD

Association Between Biomarkers and COPD Outcomes. Table S2. Statistical Models. Table S3. Demographics of Subjects at Baseline: COPDGene Cohort*. Table S4. Demographics of Subjects at Baseline: ECLIPSE Cohort*. Table S5. Analysis of COPDGene cohort. Grey shading indicates each model with lines for each biomarker in that model. Columns are beta coefficient in model (B), odds ratio, standard error (SE), correlation coefficient (R2) or pseudo R2 Cragg and Uhler’s (CU) or R2m (the marginal portion of the R2), Akaike Information Criteria (AIC), and number of subjects analyzed (N). The type of model is listed on top right of table. The best model highlighted in yellow. Table S6. Analysis of ECLIPSE cohort. Best model in ECLIPSE cohort highlighted in yellow. Grey shading indicates each model with lines for each biomarker in that model. Columns are beta coefficient in model (B), odds ratio, standard error (SE), correlation coefficient (R2) or pseudo R2 Cragg and Uhler’s (CU) or R2m (the marginal portion of the R2), Akaike Information Criteria (AIC), and number of subjects analyzed (N). The type of model is listed on top right of table. Best model in COPDGene cohort in red font. Table S7. Biomarkers Associated with FEV1/FVC. Table S8. Biomarkers Associated with (A) Total (Moderate and Severe) Exacerbations and (B) Severe Exacerbations in the Previous 12 Months. Table S9. Biomarkers Associated with (A) Prospective Total (Moderate and Severe) Exacerbations or (B) Prospective Severe Exacerbations. Table S10. Enrollment Centers. Table S11. Baseline Characteristics of Subjects with Biomarker Data Compared with Entire COPDGene Cohort. Table S12. Correlation Between Biomarkers. Table S13. Biomarkers Associated with Mortality. Analysis of COPDGene and ECLIPSE cohorts by C-statistic. Covariates were BODE, age, age2, gender, and severe exacerbations. (ZIP 485 kb

Additional file 3: of Multiple biomarkers predict disease severity, progression and mortality in COPD

Supplemental Methods. (DOCX 76 kb

Additional file 2: Figure S1. of Multiple biomarkers predict disease severity, progression and mortality in COPD

Distribution of Biomarkers. Biomarker levels were log transformed. Figure S2. Relationship Between Individual Biomarkers and FEV1. Beeswarm/box plot of biomarker levels in never smokers, smokers with normal lung function PRISm, and Gold Stage 1–4 COPD patients. Central box bars represent the median and end box bars represent the first and third quartiles. Analysis by linear regression. *p < 10−5. Figure S3. Relationship Between Individual Biomarkers and Emphysema. Analysis performed by ordinal logistic regression. Covariates were FEV1, age, smoking status, gender, race, and BMI. % Emphysema defined as % of voxels with HU < −950. *p < 0.01. (PDF 312 kb