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2. Longitudinal Association Between Muscle Loss and Mortality in Ever Smokers

Author

Mason, Stefanie E, Moreta-Martinez, Rafael, Labaki, Wassim W, Strand, Matthew J, Regan, Elizabeth A, Bon, Jessica, San Jose Estepar, Ruben, Casaburi, Richard, McDonald, Merry-Lynn, Rossiter, Harry B, Make, Barry, Dransfield, Mark T, Han, MeiLan K, Young, Kendra, Curtis, Jeffrey L, Stringer, Kathleen, Kinney, Greg, Hokanson, John E, San Jose Estepar, Raul, Washko, George R, Crapo, James D, Silverman, Edwin K, Cummings, Sara, Madden, Kelley, Make, Barry J, Nabbosa, Juliet, Port, Emily, Rashdi, Serine, Stepp, Lori, Watts, Shandi, Weaver, Michael, Beaty, Terri, Bowler, Russell P, Lynch, David A, Regan, Elizabeth, Anderson, Gary, Bleecker, Eugene R, Coxson, Harvey O, Crystal, Ronald G, Hogg, James C, Province, Michael A, Rennard, Stephen I, Croxton, Thomas, Gan, Weiniu, Postow, Lisa A, Viviano, Lisa M, Costa-Davis, Corinne, Malanga, Elisha, Prieto, Delia, Tal-Singer, Ruth, Farzadegan, Homayoon, Hadji, Akila, Sathe, Leena, Baraghoshi, David, Chen, Grace, Crooks, James, Knowles, Ruthie, Pratte, Katherine, Wilson, Carla, Zelarney, Pearlanne T, Kechris, Katerina J, Leach, Sonia, Hokanson, Co-Chair John E, Austin, Erin E, Czizik, Annika, Kinney, Gregory, Li, Yisha, Lutz, Sharon M, Ragland, Margaret F, Richmond, Nicole, Young, Kendra A, Cho, Michael, Castaldi, Peter J, Glass, Kimberly, Hersh, Craig, Kim, Wonji, Liu, Yang-Yu, Hersh, Craig P, Bidinger, Jacqueline, Cho, Michael H, Conrad, Douglas, and DeMeo, Dawn L

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Nutrition, Clinical Research, Prevention, 2.1 Biological and endogenous factors, Good Health and Well Being, Body Composition, Body Mass Index, Humans, Longitudinal Studies, Lung, Pectoralis Muscles, Pulmonary Disease, Chronic Obstructive, Smokers, COPD, mortality, muscle wasting, sarcopenia, COPDGene Investigators, Respiratory System, Cardiovascular medicine and haematology, Clinical sciences
Abstract

BackgroundBody composition measures, specifically low weight or reduced muscle mass, are associated with mortality in COPD, but the effect of longitudinal body composition changes is undefined.Research questionIs the longitudinal loss of fat-free mass (FFM) associated with increased mortality, including in those with initially normal or elevated body composition metrics?Study design and methodsParticipants with complete data for at least one visit in the COPDGene study (n = 9,268) and the ECLIPSE study (n = 1,760) were included and monitored for 12 and 8 years, respectively. Pectoralis muscle area (PMA) was derived from thoracic CT scans and used as a proxy for FFM. A longitudinal mixed submodel for PMA and a Cox proportional hazards submodel for survival were fitted on a joint distribution, using a shared random intercept parameter and Markov chain Monte Carlo parameter estimation.ResultsBoth cohorts demonstrated a left-shifted distribution of baseline FFM, not reflected in BMI, and an increase in all-cause mortality risk associated with longitudinal loss of PMA. For each 1-cm2 PMA loss, mortality increased 3.1% (95% CI, 2.4%-3.7%; P < .001) in COPDGene, and 2.4% (95% CI, 0.9%-4.0%; P < .001) in ECLIPSE. Increased mortality risk was independent of enrollment values for BMI and disease severity [BODE (body mass, airflow obstruction, dyspnea, and exercise capacity) index quartiles] and was significant even in participants with initially greater than average PMA.InterpretationLongitudinal loss of PMA is associated with increased all-cause mortality, regardless of BMI or initial muscle mass. Consideration of novel screening tests and further research into mechanisms contributing to muscle decline may improve risk stratification and identify novel therapeutic targets in ever smokers.

Published
2022

3. Alpha-1 Antitrypsin MZ Heterozygosity Is an Endotype of Chronic Obstructive Pulmonary Disease.

Author

Ghosh, Auyon J, Hobbs, Brian D, Moll, Matthew, Saferali, Aabida, Boueiz, Adel, Yun, Jeong H, Sciurba, Frank, Barwick, Lucas, Limper, Andrew H, Flaherty, Kevin, Criner, Gerard, Brown, Kevin K, Wise, Robert, Martinez, Fernando J, Lomas, David, Castaldi, Peter J, Carey, Vincent J, DeMeo, Dawn L, Cho, Michael H, Silverman, Edwin K, Hersh, Craig P, Crapo, James D, Make, Barry J, Regan, Elizabeth A, Beaty, Terri H, El-Boueiz, Adel, Foreman, Marilyn G, Hayden, Lystra P, Hetmanski, Jacqueline, Hokanson, John E, Kim, Wonji, Laird, Nan, Lange, Christoph, Lutz, Sharon M, McDonald, Merry-Lynn, Prokopenko, Dmitry, Morrow, Jarrett, Qiao, Dandi, Sakornsakolpat, Phuwanat, Wan, Emily S, Centeno, Juan Pablo, Charbonnier, Jean-Paul, Coxson, Harvey O, Galban, Craig J, Han, MeiLan K, Hoffman, Eric A, Humphries, Stephen, Jacobson, Francine L, Judy, Philip F, Kazerooni, Ella A, Kluiber, Alex, Lynch, David A, Nardelli, Pietro, Newell, John D, Notary, Aleena, Oh, Andrea, Ross, James C, Estepar, Raul San Jose, Schroeder, Joyce, Sieren, Jered, Stoel, Berend C, Tschirren, Juerg, Van Beek, Edwin, van Ginneken, Bram, van Rikxoort, Eva, Sanchez-Ferrero, Gonzalo Vegas, Veitel, Lucas, Washko, George R, Wilson, Carla G, Jensen, Robert, Everett, Douglas, Crooks, Jim, Pratte, Katherine, Strand, Matt, Austin, Erin, Kinney, Gregory, Young, Kendra A, Bhatt, Surya P, Bon, Jessica, Diaz, Alejandro A, Make, Barry, Murray, Susan, Regan, Elizabeth, Soler, Xavier, Bowler, Russell P, Kechris, Katerina, Banaei-Kashani, Farnoush, Curtis, Jeffrey L, Pernicano, Perry G, and Hanania, Nicola

Subjects
Biomedical and Clinical Sciences, Cardiovascular Medicine and Haematology, Clinical Sciences, Chronic Obstructive Pulmonary Disease, Lung, Emphysema, Clinical Research, Aetiology, 2.1 Biological and endogenous factors, Respiratory, Adult, Aged, Aged, 80 and over, Case-Control Studies, Female, Genetic Markers, Genotype, Heterozygote, Humans, Longitudinal Studies, Male, Middle Aged, Phenotype, Pulmonary Disease, Chronic Obstructive, Respiratory Function Tests, Survival Analysis, Whole Genome Sequencing, alpha 1-Antitrypsin, COPDGene Investigators, RNA sequencing, alpha-1 antitrypsin, chronic obstructive pulmonary disease, meta-analysis, Medical and Health Sciences, Respiratory System, Cardiovascular medicine and haematology, Clinical sciences
Abstract

Rationale: Multiple studies have demonstrated an increased risk of chronic obstructive pulmonary disease (COPD) in heterozygous carriers of the AAT (alpha-1 antitrypsin) Z allele. However, it is not known if MZ subjects with COPD are phenotypically different from noncarriers (MM genotype) with COPD. Objectives: To assess if MZ subjects with COPD have different clinical features compared with MM subjects with COPD. Methods: Genotypes of SERPINA1 were ascertained by using whole-genome sequencing data in three independent studies. We compared outcomes between MM subjects with COPD and MZ subjects with COPD in each study and combined the results in a meta-analysis. We performed longitudinal and survival analyses to compare outcomes in MM and MZ subjects with COPD over time. Measurements and Main Results: We included 290 MZ subjects with COPD and 6,184 MM subjects with COPD across the three studies. MZ subjects had a lower FEV1% predicted and greater quantitative emphysema on chest computed tomography scans compared with MM subjects. In a meta-analysis, the FEV1 was 3.9% lower (95% confidence interval [CI], -6.55% to -1.26%) and emphysema (the percentage of lung attenuation areas

Published
2022

4. Emphysema Progression and Lung Function Decline Among Angiotensin Converting Enzyme Inhibitors and Angiotensin-Receptor Blockade Users in the COPDGene Cohort

Author

Tejwani, Vickram, Fawzy, Ashraf, Putcha, Nirupama, Castaldi, Peter J, Cho, Michael H, Pratte, Katherine A, Bhatt, Surya P, Lynch, David A, Humphries, Stephen M, Kinney, Gregory L, D’Alessio, Franco R, Hansel, Nadia N, Crapo, James D, Silverman, Edwin K, Make, Barry J, Regan, Elizabeth A, Beaty, Terri, Begum, Ferdouse, Cho, Michael, DeMeo, Dawn L, Boueiz, Adel R, Foreman, Marilyn G, Halper-Stromberg, Eitan, Hayden, Lystra P, Hersh, Craig P, Hetmanski, Jacqueline, Hobbs, Brian D, Hokanson, John E, Laird, Nan, Lange, Christoph, Lutz, Sharon M, McDonald, Merry-Lynn, Parker, Margaret M, Prokopenko, Dmitry, Qiao, Dandi, Regan, Elizabeth, Sakornsakolpat, Phuwanat, Wan, Emily S, Won, Sungho, Centeno, Juan Pablo, Charbonnier, Jean-Paul, Coxson, Harvey O, Galban, Craig J, Han, MeiLan K, Hoffman, Eric A, Humphries, Stephen, Jacobson, Francine L, Judy, Philip F, Kazerooni, Ella A, Kluiber, Alex, Nardelli, Pietro, Newell, John D, Notary, Aleena, Oh, Andrea, Ross, James C, San Jose Estepar, Raul, Schroeder, Joyce, Sieren, Jered, Stoel, Berend C, Tschirren, Juerg, Van Beek, Edwin, Ginneken, Bramvan, van Rikxoort, Eva, Sanchez-Ferrero, Gonzalo Vegas, Veitel, Lucas, Washko, George R, Wilson, Carla G, Jensen, Robert, Everett, Douglas, Crooks, Jim, Pratte, Katherine, Strand, Matt, Kinney, Gregory, Young, Kendra A, Bon, Jessica, Diaz, Alejandro A, Make, Barry, Murray, Susan, Soler, Xavier, Bowler, Russell P, Kechris, Katerina, Banaei-Kashani, Farnoush, Curtis, Jeffrey L, Pernicano, Perry G, Hanania, Nicola, Atik, Mustafa, Boriek, Aladin, Guntupalli, Kalpatha, and Guy, Elizabeth

Subjects
Biomedical and Clinical Sciences, Cardiovascular Medicine and Haematology, Clinical Sciences, Clinical Research, Emphysema, Lung, Tobacco, Chronic Obstructive Pulmonary Disease, Tobacco Smoke and Health, Cancer, Respiratory, Aged, Angiotensin Receptor Antagonists, Angiotensin-Converting Enzyme Inhibitors, Cohort Studies, Disease Progression, Female, Forced Expiratory Volume, Humans, Lung Volume Measurements, Male, Middle Aged, Prospective Studies, Protective Factors, Pulmonary Disease, Chronic Obstructive, Pulmonary Emphysema, Spirometry, Tomography, X-Ray Computed, Vital Capacity, Walk Test, angiotensin II, COPD, emphysema progression, COPDGene Investigators, Respiratory System, Cardiovascular medicine and haematology, Clinical sciences
Abstract

BackgroundAttenuation of transforming growth factor β by blocking angiotensin II has been shown to reduce emphysema in a murine model. General population studies have demonstrated that the use of angiotensin converting enzyme inhibitors (ACEis) and angiotensin-receptor blockers (ARBs) is associated with reduction of emphysema progression in former smokers and that the use of ACEis is associated with reduction of FEV1 progression in current smokers.Research questionIs use of ACEi and ARB associated with less progression of emphysema and FEV1 decline among individuals with COPD or baseline emphysema?MethodsFormer and current smokers from the Genetic Epidemiology of COPD Study who attended baseline and 5-year follow-up visits, did not change smoking status, and underwent chest CT imaging were included. Adjusted linear mixed models were used to evaluate progression of adjusted lung density (ALD), percent emphysema (%total lung volume

Published
2021

5. The Association Between Lung Hyperinflation and Coronary Artery Disease in Smokers

Author

Chandra, Divay, Gupta, Aman, Kinney, Gregory L, Fuhrman, Carl R, Leader, Joseph K, Diaz, Alejandro A, Bon, Jessica, Barr, R Graham, Washko, George, Budoff, Matthew, Hokanson, John, Sciurba, Frank C, Crapo, James D, Silverman, Edwin K, Make, Barry J, Regan, Elizabeth A, Beaty, Terri, Begum, Ferdouse, Boueiz, Adel R, Castaldi, Peter J, Cho, Michael, DeMeo, Dawn L, Foreman, Marilyn G, Halper-Stromberg, Eitan, Hayden, Lystra P, Hersh, Craig P, Hetmanski, Jacqueline, Hobbs, Brian D, Hokanson, John E, Laird, Nan, Lange, Christoph, Lutz, Sharon M, McDonald, Merry-Lynn, Parker, Margaret M, Prokopenko, Dmitry, Qiao, Dandi, Sakornsakolpat, Phuwanat, Wan, Emily S, Won, Sungho, Al Qaisi, Mustafa, Coxson, Harvey O, Gray, Teresa, Han, MeiLan K, Hoffman, Eric A, Humphries, Stephen, Jacobson, Francine L, Judy, Philip F, Kazerooni, Ella A, Kluiber, Alex, Lynch, David A, Newell, John D, Ross, James C, San Jose Estepar, Raul, Schroeder, Joyce, Sieren, Jered, Stinson, Douglas, Stoel, Berend C, Tschirren, Juerg, Van Beek, Edwin, van Ginneken, Bram, van Rikxoort, Eva, Wilson, Carla G, Jensen, Robert, Crooks, Jim, Everett, Douglas, Moore, Camille, Strand, Hughes, John, Kinney, Gregory, Pratte, Katherine, Young, Kendra A, Bhatt, Surya, Martinez, Carlos, Murray, Susan, Soler, Xavier, Banaei-Kashani, Farnoush, Bowler, Russell P, Kechris, Katerina, Curtis, Jeffrey L, Pernicano, Perry G, Hanania, Nicola, Atik, Mustafa, Boriek, Aladin, Guntupalli, Kalpatha, Guy, Elizabeth, and Parulekar, Amit

Subjects
Biomedical and Clinical Sciences, Cardiovascular Medicine and Haematology, Clinical Sciences, Tobacco Smoke and Health, Emphysema, Chronic Obstructive Pulmonary Disease, Atherosclerosis, Biomedical Imaging, Clinical Research, Heart Disease - Coronary Heart Disease, Heart Disease, Lung, Tobacco, Cardiovascular, Prevention, Respiratory, Good Health and Well Being, Airway Obstruction, Airway Remodeling, Asymptomatic Diseases, Biological Variation, Population, Coronary Artery Disease, Coronary Vessels, Female, Humans, Male, Middle Aged, Organ Size, Plethysmography, Pulmonary Emphysema, Respiratory Function Tests, Risk Factors, Smoking, Tomography, X-Ray Computed, United States, COPD, coronary artery disease, lung hyperinflation, smoking, COPDGene Investigators, Respiratory System, Cardiovascular medicine and haematology, Clinical sciences
Abstract

BackgroundSmokers manifest varied phenotypes of pulmonary impairment.Research questionWhich pulmonary phenotypes are associated with coronary artery disease (CAD) in smokers?Study design and methodsWe analyzed data from the University of Pittsburgh COPD Specialized Center for Clinically Oriented Research (SCCOR) cohort (n = 481) and the Genetic Epidemiology of COPD (COPDGene) cohort (n = 2,580). Participants were current and former smokers with > 10 pack-years of tobacco exposure. Data from the two cohorts were analyzed separately because of methodologic differences. Lung hyperinflation was assessed by plethysmography in the SCCOR cohort and by inspiratory and expiratory CT scan lung volumes in the COPDGene cohort. Subclinical CAD was assessed as the coronary artery calcium score, whereas clinical CAD was defined as a self-reported history of CAD or myocardial infarction (MI). Analyses were performed in all smokers and then repeated in those with airflow obstruction (FEV1 to FVC ratio, < 0.70).ResultsPulmonary phenotypes, including airflow limitation, emphysema, lung hyperinflation, diffusion capacity, and radiographic measures of airway remodeling, showed weak to moderate correlations (r < 0.7) with each other. In multivariate models adjusted for pulmonary phenotypes and CAD risk factors, lung hyperinflation was the only phenotype associated with calcium score, history of clinical CAD, or history of MI (per 0.2 higher expiratory and inspiratory CT scan lung volume; coronary calcium: OR, 1.2; 95% CI, 1.1-1.5; P = .02; clinical CAD: OR, 1.6; 95% CI, 1.1-2.3; P = .01; and MI in COPDGene: OR, 1.7; 95% CI, 1.0-2.8; P = .05). FEV1 and emphysema were associated with increased risk of CAD (P < .05) in models adjusted for CAD risk factors; however, these associations were attenuated on adjusting for lung hyperinflation. Results were the same in those with airflow obstruction and were present in both cohorts.InterpretationLung hyperinflation is associated strongly with clinical and subclinical CAD in smokers, including those with airflow obstruction. After lung hyperinflation was accounted for, FEV1 and emphysema no longer were associated with CAD. Subsequent studies should consider measuring lung hyperinflation and examining its mechanistic role in CAD in current and former smokers.

Published
2021

6. Pulmonary Arterial Pruning and Longitudinal Change in Percent Emphysema and Lung Function The Genetic Epidemiology of COPD Study

Author

Pistenmaa, Carrie L, Nardelli, P, Ash, SY, Come, CE, Diaz, AA, Rahaghi, FN, Barr, RG, Young, KA, Kinney, GL, Simmons, JP, Wade, RC, Wells, JM, Hokanson, JE, Washko, GR, San José Estépar, R, Crapo, James D, Silverman, Edwin K, Make, Barry J, Regan, Elizabeth A, Beaty, Terri H, Castaldi, Peter J, Cho, Michael H, DeMeo, Dawn L, Boueiz, Adel El, Foreman, Marilyn G, Ghosh, Auyon, Hayden, Lystra P, Hersh, Craig P, Hetmanski, Jacqueline, Hobbs, Brian D, Hokanson, John E, Kim, Wonji, Laird, Nan, Lange, Christoph, Lutz, Sharon M, McDonald, Merry-Lynn, Prokopenko, Dmitry, Moll, Matthew, Morrow, Jarrett, Qiao, Dandi, Regan, Elizabeth, Saferali, Aabida, Sakornsakolpat, Phuwanat, Wan, Emily S, Yun, Jeong, Centeno, Juan Pablo, Charbonnier, Jean-Paul, Coxson, Harvey O, Galban, Craig J, Han, MeiLan K, Hoffman, Eric A, Humphries, Stephen, Jacobson, Francine L, Judy, Philip F, Kazerooni, Ella A, Kluiber, Alex, Lynch, David A, Nardelli, Pietro, Newell, John D, Notary, Aleena, Oh, Andrea, Ross, James C, San Jose Estepar, Raul, Schroeder, Joyce, Sieren, Jered, Stoel, Berend C, Tschirren, Juerg, Van Beek, Edwin, Ginneken, Bramvan, van Rikxoort, Eva, Ferrero, Gonzalo Vegas Sanchez-, Veitel, Lucas, Washko, George R, Wilson, Carla G, Jensen, Robert, Everett, Douglas, Crooks, Jim, Pratte, Katherine, Strand, Matt, Austin, Erin, Kinney, Gregory, Young, Kendra A, Bhatt, Surya P, Bon, Jessica, Diaz, Alejandro A, Make, Barry, Murray, Susan, Soler, Xavier, Bowler, Russell P, Kechris, Katerina, Banaei-Kashani, Farnoush, and Curtis, Jeffrey L

Subjects
Emphysema, Tobacco, Tobacco Smoke and Health, Lung, Chronic Obstructive Pulmonary Disease, Clinical Research, Biomedical Imaging, Respiratory, Disease Progression, Endothelium, Vascular, Female, Humans, Longitudinal Studies, Male, Middle Aged, Pulmonary Artery, Pulmonary Disease, Chronic Obstructive, Respiratory Function Tests, Smokers, Tomography, X-Ray Computed, emphysema, imaging, longitudinal, lung function, pulmonary circulation, COPDGene Investigators, Clinical Sciences, Respiratory System
Abstract

BackgroundPulmonary endothelial damage has been shown to precede the development of emphysema in animals, and vascular changes in humans have been observed in COPD and emphysema.Research questionIs intraparenchymal vascular pruning associated with longitudinal progression of emphysema on CT imaging or decline in lung function over 5 years?Study design and methodsThe Genetic Epidemiology of COPD Study enrolled ever smokers with and without COPD from 2008 through 2011. The percentage of emphysema-like lung, or "percent emphysema," was assessed at baseline and after 5 years on noncontrast CT imaging as the percentage of lung voxels < -950 Hounsfield units. An automated CT imaging-based tool assessed and classified intrapulmonary arteries and veins. Spirometry measures are postbronchodilator. Pulmonary arterial pruning was defined as a lower ratio of small artery volume (< 5 mm2 cross-sectional area) to total lung artery volume. Mixed linear models included demographics, anthropomorphics, smoking, and COPD, with emphysema models also adjusting for CT imaging scanner and lung function models adjusting for clinical center and baseline percent emphysema.ResultsAt baseline, the 4,227 participants were 60 ± 9 years of age, 50% were women, 28% were Black, 47% were current smokers, and 41% had COPD. Median percent emphysema was 2.1 (interquartile range, 0.6-6.3) and progressed 0.24 percentage points/y (95% CI, 0.22-0.26 percentage points/y) over 5.6 years. Mean FEV1 to FVC ratio was 68.5 ± 14.2% and declined 0.26%/y (95% CI, -0.30 to -0.23%/y). Greater pulmonary arterial pruning was associated with more rapid progression of percent emphysema (0.11 percentage points/y per 1-SD increase in arterial pruning; 95% CI, 0.09-0.16 percentage points/y), including after adjusting for baseline percent emphysema and FEV1. Arterial pruning also was associated with a faster decline in FEV1 to FVC ratio (-0.04%/y per 1-SD increase in arterial pruning; 95% CI, -0.008 to -0.001%/y).InterpretationPulmonary arterial pruning was associated with faster progression of percent emphysema and more rapid decline in FEV1 to FVC ratio over 5 years in ever smokers, suggesting that pulmonary vascular differences may be relevant in disease progression.Trial registryClinicalTrials.gov; No.: NCT00608764; URL: www.clinicaltrials.gov.

Published
2021

7. Liver Investigation: Testing Marker Utility in Steatohepatitis (LITMUS): Assessment & validation of imaging modality performance across the NAFLD spectrum in a prospectively recruited cohort study (the LITMUS imaging study): Study protocol

Author

Pavlides, Michael, Mózes, Ferenc E., Akhtar, Salma, Wonders, Kristy, Cobbold, Jeremy, Tunnicliffe, Elizabeth M., Allison, Michael, Godfrey, Edmund M., Aithal, Guruprasad P., Francis, Susan, Romero-Gomez, Manuel, Castell, Javier, Fernandez-Lizaranzu, Isabel, Aller, Rocio, González, Rebeca Sigüenza, Agustin, Salvador, Pericàs, Juan M., Boursier, Jerome, Aube, Christophe, Ratziu, Vlad, Wagner, Mathilde, Petta, Salvatore, Antonucci, Michela, Bugianesi, Elisabetta, Faletti, Riccardo, Miele, Luca, Geier, Andreas, Schattenberg, Jörn M., Tilman, Emrich, Ekstedt, Mattias, Lundberg, Peter, Berzigotti, Annalisa, Huber, Adrian T., Papatheodoridis, George, Yki-Järvinen, Hannele, Porthan, Kimmo, Schneider, Moritz Jörg, Hockings, Paul, Shumbayawonda, Elizabeth, Banerjee, Rajarshi, Pepin, Kay, Kalutkiewicz, Mike, Ehman, Richard L., Trylesinksi, Aldo, Coxson, Harvey O., Martic, Miljen, Yunis, Carla, Tuthill, Theresa, Bossuyt, Patrick M., Anstee, Quentin M., Neubauer, Stefan, and Harrison, Stephen

Published
2023
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8. Machine Learning Characterization of COPD Subtypes Insights From the COPDGene Study

Author

Castaldi, Peter J, Boueiz, Adel, Yun, Jeong, San Jose Estepar, Raul, Ross, James C, Washko, George, Cho, Michael H, Hersh, Craig P, Kinney, Gregory L, Young, Kendra A, Regan, Elizabeth A, Lynch, David A, Criner, Gerald J, Dy, Jennifer G, Rennard, Stephen I, Casaburi, Richard, Make, Barry J, Crapo, James, Silverman, Edwin K, Hokanson, John E, Crapo, James D, Beaty, Terri, Begum, Ferdouse, Cho, Michael, DeMeo, Dawn L, Boueiz, Adel R, Foreman, Marilyn G, Halper-Stromberg, Eitan, Hayden, Lystra P, Hetmanski, Jacqueline, Hobbs, Brian D, Laird, Nan, Lange, Christoph, Lutz, Sharon M, McDonald, Merry-Lynn, Parker, Margaret M, Prokopenko, Dmitry, Qiao, Dandi, Regan, Elizabeth, Sakornsakolpat, Phuwanat, Wan, Emily S, Won, Sungho, Centeno, Juan Pablo, Charbonnier, Jean-Paul, Coxson, Harvey O, Galban, Craig J, Han, MeiLan K, Hoffman, Eric A, Humphries, Stephen, Jacobson, Francine L, Judy, Philip F, Kazerooni, Ella A, Kluiber, Alex, Nardelli, Pietro, Newell, John D, Notary, Aleena, Oh, Andrea, Schroeder, Joyce, Sieren, Jered, Stoel, Berend C, Tschirren, Juerg, Van Beek, Edwin, van Ginneken, Bram, van Rikxoort, Eva, Sanchez-Ferrero, Gonzalo Vegas, Veitel, Lucas, Washko, George R, Wilson, Carla G, Jensen, Robert, Everett, Douglas, Crooks, Jim, Pratte, Katherine, Strand, Matt, Kinney, Gregory, Bhatt, Surya P, Bon, Jessica, Diaz, Alejandro A, Make, Barry, Murray, Susan, Soler, Xavier, Bowler, Russell P, and Kechris, Katerina

Subjects
Biomedical and Clinical Sciences, Cardiovascular Medicine and Haematology, Clinical Sciences, Genetics, Chronic Obstructive Pulmonary Disease, Lung, 2.1 Biological and endogenous factors, Aetiology, Respiratory, Cluster Analysis, Diagnostic Imaging, Disease Progression, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Machine Learning, Molecular Epidemiology, Phenotype, Pulmonary Disease, Chronic Obstructive, Respiratory Function Tests, COPD, emphysema, machine learning, COPDGene Investigators, Respiratory System, Cardiovascular medicine and haematology, Clinical sciences
Abstract

COPD is a heterogeneous syndrome. Many COPD subtypes have been proposed, but there is not yet consensus on how many COPD subtypes there are and how they should be defined. The COPD Genetic Epidemiology Study (COPDGene), which has generated 10-year longitudinal chest imaging, spirometry, and molecular data, is a rich resource for relating COPD phenotypes to underlying genetic and molecular mechanisms. In this article, we place COPDGene clustering studies in context with other highly cited COPD clustering studies, and summarize the main COPD subtype findings from COPDGene. First, most manifestations of COPD occur along a continuum, which explains why continuous aspects of COPD or disease axes may be more accurate and reproducible than subtypes identified through clustering methods. Second, continuous COPD-related measures can be used to create subgroups through the use of predictive models to define cut-points, and we review COPDGene research on blood eosinophil count thresholds as a specific example. Third, COPD phenotypes identified or prioritized through machine learning methods have led to novel biological discoveries, including novel emphysema genetic risk variants and systemic inflammatory subtypes of COPD. Fourth, trajectory-based COPD subtyping captures differences in the longitudinal evolution of COPD, addressing a major limitation of clustering analyses that are confounded by disease severity. Ongoing longitudinal characterization of subjects in COPDGene will provide useful insights about the relationship between lung imaging parameters, molecular markers, and COPD progression that will enable the identification of subtypes based on underlying disease processes and distinct patterns of disease progression, with the potential to improve the clinical relevance and reproducibility of COPD subtypes.

Published
2020

9. Disease Progression Modeling in Chronic Obstructive Pulmonary Disease

Author

Young, Alexandra L, Bragman, Felix JS, Rangelov, Bojidar, Han, MeiLan K, Galbán, Craig J, Lynch, David A, Hawkes, David J, Alexander, Daniel C, Hurst, John R, Crapo, James D, Silverman, Edwin K, Make, Barry J, Regan, Elizabeth A, Beaty, Terri, Begum, Ferdouse, Castaldi, Peter J, Cho, Michael, DeMeo, Dawn L, Boueiz, Adel R, Foreman, Marilyn G, Halper-Stromberg, Eitan, Hayden, Lystra P, Hersh, Craig P, Hetmanski, Jacqueline, Hobbs, Brian D, Hokanson, John E, Laird, Nan, Lange, Christoph, Lutz, Sharon M, McDonald, Merry-Lynn, Parker, Margaret M, Qiao, Dandi, Wan, Emily S, Won, Sungho, Sakornsakolpat, Phuwanat, Prokopenko, Dmitry, Al Qaisi, Mustafa, Coxson, Harvey O, Gray, Teresa, Hoffman, Eric A, Humphries, Stephen, Jacobson, Francine L, Judy, Philip F, Kazerooni, Ella A, Kluiber, Alex, Newell, John D, Ross, James C, Estepar, Raul San Jose, Schroeder, Joyce, Sieren, Jered, Stinson, Douglas, Stoel, Berend C, Tschirren, Juerg, Van Beek, Edwin, van Ginneken, Bram, van Rikxoort, Eva, Washko, George, Wilson, Carla G, Jensen, Robert, Everett, Douglas, Crooks, Jim, Moore, Camille, Strand, Matt, Hughes, John, Kinney, Gregory, Pratte, Katherine, Young, Kendra A, Bhatt, Surya, Bon, Jessica, Martinez, Carlos, Murray, Susan, Soler, Xavier, Bowler, Russell P, Kechris, Katerina, Banaei-Kashani, Farnoush, Curtis, Jeffrey L, Martinez, Carlos H, Pernicano, Perry G, Hanania, Nicola, Alapat, Philip, Atik, Mustafa, Bandi, Venkata, Boriek, Aladin, Guntupalli, Kalpatha, Guy, Elizabeth, Nachiappan, Arun, Parulekar, Amit, Barr, R Graham, Austin, John, D’Souza, Belinda, Pearson, Gregory DN, Rozenshtein, Anna, Thomashow, Byron, MacIntyre, Neil, McAdams, H Page, Washington, Lacey, McEvoy, Charlene, and Tashjian, Joseph

Subjects
Biomedical Imaging, Lung, Clinical Research, Chronic Obstructive Pulmonary Disease, Aetiology, 4.2 Evaluation of markers and technologies, Detection, screening and diagnosis, 4.1 Discovery and preclinical testing of markers and technologies, 2.1 Biological and endogenous factors, Respiratory, Aged, Disease Progression, Female, Humans, Male, Middle Aged, Models, Theoretical, Pulmonary Disease, Chronic Obstructive, Tomography, X-Ray Computed, clustering, CT imaging, emphysema, bronchitis, chronic obstructive pulmonary disease, COPDGene Investigators, Medical and Health Sciences, Respiratory System
Abstract

Rationale: The decades-long progression of chronic obstructive pulmonary disease (COPD) renders identifying different trajectories of disease progression challenging.Objectives: To identify subtypes of patients with COPD with distinct longitudinal progression patterns using a novel machine-learning tool called "Subtype and Stage Inference" (SuStaIn) and to evaluate the utility of SuStaIn for patient stratification in COPD.Methods: We applied SuStaIn to cross-sectional computed tomography imaging markers in 3,698 Global Initiative for Chronic Obstructive Lung Disease (GOLD) 1-4 patients and 3,479 controls from the COPDGene (COPD Genetic Epidemiology) study to identify subtypes of patients with COPD. We confirmed the identified subtypes and progression patterns using ECLIPSE (Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints) data. We assessed the utility of SuStaIn for patient stratification by comparing SuStaIn subtypes and stages at baseline with longitudinal follow-up data.Measurements and Main Results: We identified two trajectories of disease progression in COPD: a "Tissue→Airway" subtype (n = 2,354, 70.4%), in which small airway dysfunction and emphysema precede large airway wall abnormalities, and an "Airway→Tissue" subtype (n = 988, 29.6%), in which large airway wall abnormalities precede emphysema and small airway dysfunction. Subtypes were reproducible in ECLIPSE. Baseline stage in both subtypes correlated with future FEV1/FVC decline (r = -0.16 [P

Published
2020

10. Prevalence of abnormal spirometry in individuals with a smoking history and no known obstructive lung disease

Author

Crapo, James D., Silverman, Edwin K., Make, Barry J., Regan, Elizabeth A., Beaty, Terri H., Castaldi, Peter J., Cho, Michael H., DeMeo, Dawn L., El Boueiz, Adel, Foreman, Marilyn G., Ghosh, Auyon, Hayden, Lystra P., Hersh, Craig P., Hetmanski, Jacqueline, Hobbs, Brian D., Hokanson, John E., Kim, Wonji, Laird, Nan, Lange, Christoph, Lutz, Sharon M., McDonald, Merry-Lynn, Prokopenko, Dmitry, Moll, Matthew, Morrow, Jarrett, Qiao, Dandi, Saferali, Aabida, Sakornsakolpat, Phuwanat, Wan, Emily S., Yun, Jeong, Centeno, Juan Pablo, Charbonnier, Jean-Paul, Coxson, Harvey O., Galban, Craig J., Han, MeiLan K., Hoffman, Eric A., Humphries, Stephen, Jacobson, Francine L., Judy, Philip F., Kazerooni, Ella A., Kluiber, Alex, Lynch, David A., Nardelli, Pietro, Newell, John D., Jr., Notary, Aleena, Oh, Andrea, Ross, James C., San Jose Estepar, Raul, Schroeder, Joyce, Sieren, Jered, Stoel, Berend C., Tschirren, Juerg, Van Beek, Edwin, van Ginneken, Bram, van Rikxoort, Eva, Sanchez Ferrero, Gonzalo Vegas, Veitel, Lucas, Washko, George R., Wilson, Carla G., Jensen, Robert, Everett, Douglas, Crooks, Jim, Pratte, Katherine, Strand, Matt, Austin, Erin, Kinney, Gregory, Young, Kendra A., Bhatt, Surya P., Bon, Jessica, Diaz, Alejandro A., Make, Barry, Murray, Susan, Regan, Elizabeth, Soler, Xavier, Bowler, Russell P., Kechris, Katerina, BanaeiKashani, Farnoush, Curtis, Jeffrey L., Pernicano, Perry G., Hanania, Nicola, Atik, Mustafa, Boriek, Aladin, Guntupalli, Kalpatha, Guy, Elizabeth, Parulekar, Amit, Hersh, Craig, Washko, George, Barr, R. Graham, Austin, John, D'Souza, Belinda, Thomashow, Byron, MacIntyre, Neil, Jr., McAdams, H. Page, Washington, Lacey, McEvoy, Charlene, Tashjian, Joseph, Wise, Robert, Brown, Robert, Hansel, Nadia N., Horton, Karen, Lambert, Allison, Putcha, Nirupama, Casaburi, Richard, Adami, Alessandra, Budoff, Matthew, Fischer, Hans, Porszasz, Janos, Rossiter, Harry, Stringer, William, Sharafkhaneh, Amir, Lan, Charlie, Wendt, Christine, Bell, Brian, Kunisaki, Ken M., Flenaugh, Eric L., Gebrekristos, Hirut, Ponce, Mario, Terpenning, Silanath, Westney, Gloria, Bowler, Russell, Rosiello, Richard, Pace, David, Criner, Gerard, Ciccolella, David, Cordova, Francis, Dass, Chandra, D'Alonzo, Gilbert, Desai, Parag, Jacobs, Michael, Kelsen, Steven, Kim, Victor, Mamary, A. James, Marchetti, Nathaniel, Satti, Aditi, Shenoy, Kartik, Steiner, Robert M., Swift, Alex, Swift, Irene, Vega-Sanchez, Maria Elena, Dransfield, Mark, Bailey, William, Iyer, Anand, Nath, Hrudaya, Wells, J. Michael, Conrad, Douglas, Yen, Andrew, Comellas, Alejandro P., Hoth, Karin F., Newell, John, Jr., Thompson, Brad, Kazerooni, Ella, Labaki, Wassim, Galban, Craig, Vummidi, Dharshan, Billings, Joanne, Begnaud, Abbie, Allen, Tadashi, Sciurba, Frank, Chandra, Divay, Weissfeld, Joel, Anzueto, Antonio, Adams, Sandra, Maselli-Caceres, Diego, Ruiz, Mario E., Singh, Harjinder, Tran, Thuonghien V., Kinney, Gregory L., Comellas, Alejandro, Baldomero, Arianne K., Hokanson, John, and Fortis, Spyridon

Published
2023
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11. Clinical Markers Associated With Risk of Suicide or Drug Overdose Among Individuals With Smoking Exposure: A Longitudinal Follow-up Study of the COPDGene Cohort

Author

Crapo, James D., Silverman, Edwin K., Make, Barry J., Regan, Elizabeth A., Beaty, Terri H., Castaldi, Peter J., Cho, Michael H., DeMeo, Dawn L., El Boueiz, Adel, Foreman, Marilyn G., Ghosh, Auyon, Hayden, Lystra P., Hersh, Craig P., Hetmanski, Jacqueline, Hobbs, Brian D., Hokanson, John E., Kim, Wonji, Laird, Nan, Lange, Christoph, Lutz, Sharon M., McDonald, Merry-Lynn, Prokopenko, Dmitry, Moll, Matthew, Morrow, Jarrett, Qiao, Dandi, Saferali, Aabida, Sakornsakolpat, Phuwanat, Wan, Emily S., Yun, Jeong, Centeno, Juan Pablo, Charbonnier, Jean-Paul, Coxson, Harvey O., Galban, Craig J., Han, MeiLan K., Hoffman, Eric A., Humphries, Stephen, Jacobson, Francine L., Judy, Philip F., Kazerooni, Ella A., Kluiber, Alex, Lynch, David A., Nardelli, Pietro, Newell, John D., Jr., Notary, Aleena, Oh, Andrea, Ross, James C., San Jose Estepar, Raul, Schroeder, Joyce, Sieren, Jered, Stoel, Berend C., Tschirren, Juerg, Van Beek, Edwin, van Ginneken, Bram, van Rikxoort, Eva, Sanchez-Ferrero, Gonzalo Vegas, Veitel, Lucas, Washko, George R., Wilson, Carla G., Jensen, Robert, Strand, Matthew, Crooks, Jim, Pratte, Katherine, Khatiwada, Aastha, Austin, Erin, Kinney, Gregory, Young, Kendra A., Bhatt, Surya P., Bon, Jessica, Diaz, Alejandro A., Make, Barry, Murray, Susan, Regan, Elizabeth, Soler, Xavier, Bowler, Russell P., Kechris, Katerina, Banaei-Kashani, Farnoush, Curtis, Jeffrey L., Pernicano, Perry G., Hanania, Nicola, Atik, Mustafa, Boriek, Aladin, Guntupalli, Kalpatha, Guy, Elizabeth, Parulekar, Amit, Hersh, Craig, Washko, George, Barr, R. Graham, Austin, John, D’Souza, Belinda, Thomashow, Byron, MacIntyre, Neil, Jr., McAdams, H. Page, Washington, Lacey, McEvoy, Charlene, Tashjian, Joseph, Wise, Robert, Brown, Robert, Hansel, Nadia N., Horton, Karen, Lambert, Allison, Putcha, Nirupama, Casaburi, Richard, Adami, Alessandra, Budoff, Matthew, Fischer, Hans, Porszasz, Janos, Rossiter, Harry, Stringer, William, Sharafkhaneh, Amir, Lan, Charlie, Wendt, Christine, Bell, Brian, Kunisaki, Ken M., Flenaugh, Eric L., Gebrekristos, Hirut, Ponce, Mario, Terpenning, Silanath, Westney, Gloria, Bowler, Russell, Rosiello, Richard, Pace, David, Criner, Gerard, Ciccolella, David, Cordova, Francis, Dass, Chandra, D’Alonzo, Gilbert, Desai, Parag, Jacobs, Michael, Kelsen, Steven, Kim, Victor, Mamary, A. James, Marchetti, Nathaniel, Satti, Aditi, Shenoy, Kartik, Steiner, Robert M., Swift, Alex, Swift, Irene, Vega-Sanchez, Maria Elena, Dransfield, Mark, Bailey, William, Iyer, Anand, Nath, Hrudaya, Wells, J. Michael, Conrad, Douglas, Yen, Andrew, Comellas, Alejandro P., Hoth, Karin F., Newell, John, Jr., Thompson, Brad, Kazerooni, Ella, Labaki, Wassim, Galban, Craig, Vummidi, Dharshan, Billings, Joanne, Begnaud, Abbie, Allen, Tadashi, Sciurba, Frank, Chandra, Divay, Weissfeld, Joel, Anzueto, Antonio, Adams, Sandra, Maselli-Caceres, Diego, Ruiz, Mario E., Singh, Harjinder, Adviento, Brigid A., Iyer, Anand S., Kinney, Gregory L., Hanania, Nicola A., Lowe, Katherine E., Holm, Kristen E., Yohannes, Abebaw M., Shinozaki, Gen, and Fiedorowicz, Jess G.

Published
2023
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13. Longitudinal Association Between Muscle Loss and Mortality in Ever Smokers

Author

Crapo, James D., Silverman, Edwin K., Cummings, Sara, Madden, Kelley, Make, Barry J., Nabbosa, Juliet, Port, Emily, Rashdi, Serine, Regan, Elizabeth A., Stepp, Lori, Watts, Shandi, Weaver, Michael, Beaty, Terri, Bowler, Russell P., Curtis, Jeffrey L., Han, MeiLan K., Hokanson, John E., Lynch, David A., Strand, Matthew J., Anderson, Gary, Bleecker, Eugene R., Coxson, Harvey O., Crystal, Ronald G., Hogg, James C., Province, Michael A., Rennard, Stephen I., Croxton, Thomas, Gan, Weiniu, Postow, Lisa A., Viviano, Lisa M., Costa-Davis, Corinne, Malanga, Elisha, Prieto, Delia, Tal-Singer, Ruth, Farzadegan, Homayoon, Hadji, Akila, Sathe, Leena, Baraghoshi, David, Chen, Grace, Crooks, James, Knowles, Ruthie, Pratte, Katherine, Wilson, Carla, Zelarney, Pearlanne T., Kechris, Katerina J., Leach, Sonia, Austin, Erin E., Czizik, Annika, Kinney, Gregory, Li, Yisha, Lutz, Sharon M., Ragland, Margaret F., Richmond, Nicole, Young, Kendra A., Cho, Michael, Castaldi, Peter J., Glass, Kimberly, Hersh, Craig, Kim, Wonji, Liu, Yang-Yu, Hersh, Craig P., Bidinger, Jacqueline, Cho, Michael H., Conrad, Douglas, DeMeo, Dawn L., El-Boueiz, Adel R., Foreman, Marilyn G., Ghosh, Auyon, Hahn, Georg, Hansel, Nadia N., Hayden, Lystra P., Hobbs, Brian, Kim, Woori, Lange, Christoph, McDonald, Merry- Lynn, McGeachie, Michael, Moll, Matthew, Morris, Melody, Patsopoulos, Nikolaos A., Qiao, Dandi, Ruczinski, Ingo, Wan, Emily S., Dy, Jennifer G., Fain, Sean B., Ginsburg, Shoshana, Hoffman, Eric A., Humphries, Stephen, Judy, Philip F., Stefanie Mason, Alex Kluiber, Oh, Andrea, Poynton, Clare, Reinhardt, Joseph M., Ross, James, San Jose Estepar, Raul, Schroeder, Joyce D., Sitek, Arkadiusz, Steiner, Robert M., van Beek, Edwin, Ginneken, Bram van, van Rikxoort, Eva, Washko, George R., Jensen, Robert, John E. Hokanson, Co-Chair, Bhatt, Surya P., Casaburi, Richard, Kim, Victor, Putcha, Nirupama, Han, MeiLan, Bon, Jessica, Diaz, Alejandro A., Regan, Elizabeth, Anzueto, Antonio, Bailey, William C., Criner, Gerard J., Dransfield, Mark T., Kinney, Greg, Sprenger, Kim, Benos, Takis, Hanania, Nicola A., Hoth, Karin F., Lambert, Allison, Lowe, Katherine, Oates, Gabriela, Parekh, Trisha, Westney, Gloria, Young, Kendra, Balasubramanian, Aparna, Boriek, Aladin, Fawzy, Ashraf, Jacobson, Francine, LaFon, David C., MacIntyre, Neil, Maselli-Caceres, Diego, McCormack, Meredith C., McDonald, Merry-Lynn, Sciurba, Frank, Soler, Xavier, Tejwani, Vickram, van Beek, Edwin JR., Wade, Raymond C., Wells, Mike, Wendt, Chris H., Yun, Jeong H., Zhang, Jingzhou, Gillenwater, Lucas, Lowe, Katherine E., Pratte, Katherine A., Ragland, Margaret, Attaway, Amy, Mason, Stefanie, Rossiter, Harry B., Saha, Punam Kumar, Wilson, Ava, Amaza, Hannatu, Baldomero, Adrienne, Mamary, A. James, O’Brien, James, Wise, Robert A., Eakin, Michelle, Fiedorowicz, Jess G., Henkle, Ben, Holm, Kristen, Iyer, Anand, Kunisaki, Ken M., McEvoy, Charlene, Mkorombindo, Takudzwa, Shinozaki, Gen, Yohannes, Abebaw, Hobbs, Brian D., Miller, Bruce E., Retson, Tara, McCloskey, Lisa, Pernicano, Perry G., Atik, Mustafa, Bertrand, Laura, Monaco, Thomas, Narendra, Dharani, Lenge de Rosen, Veronica V., Badu-Danso, Kwame, Jacobson, Francine L., Kaufman, Laura, Maguire, Cherie, Struble, Sophie, Wilson, Seth, Barr, R. Graham, Almonte, Casandra, Austin, John H.M., Gomez Blum, Maria Lorena, D’Souza, Belinda M., Florez, Emilay, Martinez, Rodney, MacIntyre, Neil, Jr., Curry, Wendy, McAdams, H. Page, Reikofski, Charlotte V., Washington, Lacey, Brown, Robert, Clare, Cheryl, Daniel, Marie, Horton, Karen, Ting “Tony” Lin, Cheng, Mirza, Tahira, Scott, Meagan, Shade, Becky, Budoff, Matt, Calmelat, Robert, Cavanaugh, Deborah, Dailing, Chris, Diaz, Leticia, Fischer, Hans, Indelicato, Renee Love, Porszasz, Janos, Soriano, April, Stringer, William, Urrutia, Miriam, Baldomero, Arianne, Bell, Brian, Deconcini, Miranda, Loes, Linda, Phelan, Jonathan, Robichaux, Camille, Sasse, Cheryl, Tashjian, Joseph H., Flenaugh, Eric L., Abson, Kema, Gebrekristos, Hirut, Johnson, Priscilla, Jordan, Jessica, Ponce, Mario, Terpenning, Silanath, Wilson, Derrick, Broadhurst, Grace, Dyer, Debra, Engel, Elena, Finigan, Jay, Hill, Andrew, Jones, Alex, Jones, Ryan, Owen, Jordan, Rosiello, Richard, Andries, Nicole, Charpentier, Mary, Kirk, Diane, Pace, David, Ciccolella, David, Cordova, Francis, Dass, Chandra, D’Alonzo, Gilbert, Davis, Valena, Desai, Parag, Fehrle, Dee, Grabianowski, Carla, Jacobs, Michael, Jameson, Laurie, Jones, Gayle M., Kelsen, Steven, Marchetti, Nathaniel, McGonagle, Francine, Satti, Aditi, Shenoy, Kartik, Sheridan, Regina, Vega-Sanchez, Maria, Wallace, Samantha, Akinseye-kolapo, Samuel, Baker, Matthew, Goggins, Arnissa, McClain, Anny, Nath, Hrudaya, Singh, Satinder P., Sonavane, Sushil K., Westfall, Elizabeth, Gil, Marissa, El Hajjaoui, Tarek, Hsiao, Albert, Martineau, Amber, Mielke, Jenna, Perez, Karl, Querido, Gabriel, Reston, Tara, Yen, Andrew, Comellas, Alejandro, Fortis, Spyridon, Galizia, Mauricio, Garcia, Eric, Keating, Janet, Laroia, Archana, Lee, Changhyun, Meyer, Amber, Mullan, Brian, Nagpal, Prashant, Ofori, Oloigbe, Suiter, Sierra, Mason, Stefanie E., Moreta-Martinez, Rafael, Labaki, Wassim W., San Jose Estepar, Ruben, Make, Barry, and Stringer, Kathleen

Published
2022
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14. Blood eosinophil count thresholds and exacerbations in patients with chronic obstructive pulmonary disease

Author

Yun, Jeong H, Lamb, Andrew, Chase, Robert, Singh, Dave, Parker, Margaret M, Saferali, Aabida, Vestbo, Jørgen, Tal-Singer, Ruth, Castaldi, Peter J, Silverman, Edwin K, Hersh, Craig P, Crapo, James D, Make, Barry J, Regan, Elizabeth A, Beaty, Terri, Begum, Ferdouse, Busch, Robert, Cho, Michael, DeMeo, Dawn L, Boueiz, Adel R, Foreman, Marilyn G, Halper-Stromberg, Eitan, Hansel, Nadia N, Hardin, Megan E, Hayden, Lystra P, Hetmanski, Jacqueline, Hobbs, Brian D, Hokanson, John E, Laird, Nan, Lange, Christoph, Lutz, Sharon M, McDonald, Merry-Lynn, Qiao, Dandi, Santorico, Stephanie, Silverman, E, Wan, Emily S, Won, Sungho, Qaisi, Mustafa Al, Coxson, Harvey O, Gray, Teresa, Han, MeiLan K, Hoffman, Eric A, Humphries, Stephen, Jacobson, Francine L, Judy, Philip F, Kazerooni, Ella A, Kluiber, Alex, Lynch, David A, Newell, John D, Ross, James C, San Jose Estepar, Raul, Schroeder, Joyce, Sieren, Jered, Stinson, Douglas, Stoel, Berend C, Tschirren, Juerg, Van Beek, Edwin, van Ginneken, Bram, van Rikxoort, Eva, Washko, George, Wilson, Carla G, Jensen, Robert, Everett, Douglas, Crooks, Jim, Moore, Camille, Strand, Matt, Hughes, John, Kinney, Gregory, Pratte, Katherine, Young, Kendra A, Curtis, Jeffrey L, Martinez, Carlos H, Pernicano, Perry G, Hanania, Nicola, Alapat, Philip, Atik, Mustafa, Bandi, Venkata, Boriek, Aladin, Guntupalli, Kalpatha, Guy, Elizabeth, Nachiappan, Arun, Parulekar, Amit, Hersh, Craig, Barr, R Graham, Austin, John, D'Souza, Belinda, Pearson, Gregory DN, and Rozenshtein, Anna

Subjects
Lung, Chronic Obstructive Pulmonary Disease, Clinical Research, Respiratory, Aged, Disease Progression, Eosinophils, Female, Humans, Leukocyte Count, Longitudinal Studies, Male, Middle Aged, Observational Studies as Topic, Pulmonary Disease, Chronic Obstructive, Chronic obstructive pulmonary disease, asthma, eosinophil, exacerbation, COPDGene and ECLIPSE Investigators, Immunology, Allergy
Abstract

BACKGROUND:Eosinophilic airway inflammation in patients with chronic obstructive pulmonary disease (COPD) is associated with exacerbations and responsivity to steroids, suggesting potential shared mechanisms with eosinophilic asthma. However, there is no consistent blood eosinophil count that has been used to define the increased exacerbation risk. OBJECTIVE:We sought to investigate blood eosinophil counts associated with exacerbation risk in patients with COPD. METHODS:Blood eosinophil counts and exacerbation risk were analyzed in patients with moderate-to-severe COPD by using 2 independent studies of former and current smokers with longitudinal data. The Genetic Epidemiology of COPD (COPDGene) study was analyzed for discovery (n = 1,553), and the Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints (ECLIPSE) study was analyzed for validation (n = 1,895). A subset of the ECLIPSE study subjects were used to assess the stability of blood eosinophil counts over time. RESULTS:COPD exacerbation risk increased with higher eosinophil counts. An eosinophil count threshold of 300 cells/μL or greater showed adjusted incidence rate ratios for exacerbations of 1.32 in the COPDGene study (95% CI, 1.10-1.63). The cutoff of 300 cells/μL or greater was validated for prospective risk of exacerbation in the ECLIPSE study, with adjusted incidence rate ratios of 1.22 (95% CI, 1.06-1.41) using 3-year follow-up data. Stratified analysis confirmed that the increased exacerbation risk associated with an eosinophil count of 300 cells/μL or greater was driven by subjects with a history of frequent exacerbations in both the COPDGene and ECLIPSE studies. CONCLUSIONS:Patients with moderate-to-severe COPD and blood eosinophil counts of 300 cells/μL or greater had an increased risk exacerbations in the COPDGene study, which was prospectively validated in the ECLIPSE study.

Published
2018

15. Lobar Emphysema Distribution Is Associated With 5-Year Radiological Disease Progression

Author

Boueiz, Adel, Chang, Yale, Cho, Michael H, Washko, George R, San José Estépar, Raul, Bowler, Russell P, Crapo, James D, DeMeo, Dawn L, Dy, Jennifer G, Silverman, Edwin K, Castaldi, Peter J, Crapo, James, Silverman, Edwin, Make, Barry, Regan, Elizabeth, Beaty, Terri, Laird, Nan, Lange, Christoph, Santorico, Stephanie, Hokanson, John, DeMeo, Dawn, Hansel, Nadia, Hersh, Craig, Castaldi, Peter, McDonald, Merry-Lynn, Wan, Emily, Hardin, Megan, Hetmanski, Jacqueline, Parker, Margaret, Foreman, Marilyn, Hobbs, Brian, Busch, Robert, Qiao, Dandi, Halper-Stromberg, Eitan, Begum, Ferdouse, Won, Sungho, Lutz, Sharon, Lynch, David A, Coxson, Harvey O, Han, MeiLan K, Hoffman, Eric A, Humphries, Stephen, Jacobson, Francine L, Judy, Philip F, Kazerooni, Ella A, Newell, John D, Ross, James C, Estépar, Raul José, Stoel, Berend C, Tschirren, Juerg, van Rikxoort, Eva, van Ginneken, Bram, Wilson, Carla G, Qaisi, Mustafa Al, Gray, Teresa, Kluiber, Alex, Mann, Tanya, Sieren, Jered, Stinson, Douglas, Schroeder, Joyce, Van Beek, Edwin, Jensen, Robert, Everett, Douglas, Faino, Anna, Strand, Matt, Wilson, Carla, Hokanson, John E, Kinney, Gregory, Young, Kendra, Pratte, Katherine, Duca, Lindsey, Curtis, Jeffrey L, Martinez, Carlos H, Pernicano, Perry G, Hanania, Nicola, Alapat, Philip, Bandi, Venkata, Atik, Mustafa, Boriek, Aladin, Guntupalli, Kalpatha, Guy, Elizabeth, Parulekar, Amit, Nachiappan, Arun, Jacobson, Francine, Barr, R Graham, Thomashow, Byron, Austin, John, D’Souza, Belinda, and Pearson, Gregory DN

Subjects
Clinical Research, Chronic Obstructive Pulmonary Disease, Lung, Emphysema, Prevention, 2.1 Biological and endogenous factors, Aetiology, Respiratory, Aged, Comorbidity, Disease Progression, Female, Forced Expiratory Volume, Humans, Male, Middle Aged, Pulmonary Emphysema, Severity of Illness Index, Tomography, X-Ray Computed, clustering, COPD, COPD disease progression, emphysema distribution, machine learning, COPDGene Investigators, Clinical Sciences, Respiratory System
Abstract

BackgroundEmphysema has considerable variability in its regional distribution. Craniocaudal emphysema distribution is an important predictor of the response to lung volume reduction. However, there is little consensus regarding how to define upper lobe-predominant and lower lobe-predominant emphysema subtypes. Consequently, the clinical and genetic associations with these subtypes are poorly characterized.MethodsWe sought to identify subgroups characterized by upper-lobe or lower-lobe emphysema predominance and comparable amounts of total emphysema by analyzing data from 9,210 smokers without alpha-1-antitrypsin deficiency in the Genetic Epidemiology of COPD (COPDGene) cohort. CT densitometric emphysema was measured in each lung lobe. Random forest clustering was applied to lobar emphysema variables after regressing out the effects of total emphysema. Clusters were tested for association with clinical and imaging outcomes at baseline and at 5-year follow-up. Their associations with genetic variants were also compared.ResultsThree clusters were identified: minimal emphysema (n = 1,312), upper lobe-predominant emphysema (n = 905), and lower lobe-predominant emphysema (n = 796). Despite a similar amount of total emphysema, the lower-lobe group had more severe airflow obstruction at baseline and higher rates of metabolic syndrome compared with subjects with upper-lobe predominance. The group with upper-lobe predominance had greater 5-year progression of emphysema, gas trapping, and dyspnea. Differential associations with known COPD genetic risk variants were noted.ConclusionsSubgroups of smokers defined by upper-lobe or lower-lobe emphysema predominance exhibit different functional and radiological disease progression rates, and the upper-lobe predominant subtype shows evidence of association with known COPD genetic risk variants. These subgroups may be useful in the development of personalized treatments for COPD.

Published
2018

17. The transition from normal lung anatomy to minimal and established fibrosis in idiopathic pulmonary fibrosis (IPF)

Author

Xu, Feng, Tanabe, Naoya, Vasilescu, Dragos M., McDonough, John E., Coxson, Harvey O., Ikezoe, Kohei, Kinose, Daisuke, Ng, Kevin W., Verleden, Stijn E., Wuyts, Wim A., Vanaudenaerde, Bart M., Verschakelen, Johny, Cooper, Joel D., Lenburg, Marc E., Morshead, Katrina B., Abbas, Alexander R., Arron, Joseph R., Spira, Avrum, Hackett, Tillie-Louise, Colby, Thomas V., Ryerson, Christopher J., Ng, Raymond T., and Hogg, James C.

Published
2021
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18. This Is What Metabolic Dysfunction–Associated Steatotic Liver Disease Looks Like: Potential of a Multiparametric MRI Protocol.

Author

Fischer, Anja M., Lechea, Nazim, and Coxson, Harvey O.

Subjects
LIVER diseases, MAGNETIC resonance imaging, LIVER biopsy, LIVER histology
Abstract

Metabolic dysfunction–associated steatotic liver disease (MASLD) is a prevalent condition with a broad spectrum defined by liver biopsy. This gold standard method evaluates three features: steatosis, activity (ballooning and lobular inflammation), and fibrosis, attributing them to certain grades or stages using a semiquantitative scoring system. However, liver biopsy is subject to numerous restrictions, creating an unmet need for a reliable and reproducible method for MASLD assessment, grading, and staging. Noninvasive imaging modalities, such as magnetic resonance imaging (MRI), offer the potential to assess quantitative liver parameters. This review aims to provide an overview of the available MRI techniques for the three criteria evaluated individually by liver histology. Here, we discuss the possibility of combining multiple MRI parameters to replace liver biopsy with a holistic, multiparametric MRI protocol. In conclusion, the development and implementation of such an approach could significantly improve the diagnosis and management of MASLD, reducing the need for invasive procedures and paving the way for more personalized treatment strategies. [ABSTRACT FROM AUTHOR]

Published
2024
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20. Machine Learning Characterization of COPD Subtypes: Insights From the COPDGene Study

Author

Crapo, James D., Silverman, Edwin K., Make, Barry J., Regan, Elizabeth A., Beaty, Terri, Begum, Ferdouse, Castaldi, Peter J., Cho, Michael, DeMeo, Dawn L., Boueiz, Adel R., Foreman, Marilyn G., Halper-Stromberg, Eitan, Hayden, Lystra P., Hersh, Craig P., Hetmanski, Jacqueline, Hobbs, Brian D., Hokanson, John E., Laird, Nan, Lange, Christoph, Lutz, Sharon M., McDonald, Merry-Lynn, Parker, Margaret M., Prokopenko, Dmitry, Qiao, Dandi, Sakornsakolpat, Phuwanat, Wan, Emily S., Won, Sungho, Centeno, Juan Pablo, Charbonnier, Jean-Paul, Coxson, Harvey O., Galban, Craig J., Han, MeiLan K., Hoffman, Eric A., Humphries, Stephen, Jacobson, Francine L., Judy, Philip F., Kazerooni, Ella A., Kluiber, Alex, Lynch, David A., Nardelli, Pietro, Newell, John D., Jr., Notary, Aleena, Oh, Andrea, Ross, James C., Estepar, Raul San Jose, Schroeder, Joyce, Sieren, Jered, Stoel, Berend C., Tschirren, Juerg, Van Beek, Edwin, van Ginneken, Bram, van Rikxoort, Eva, Sanchez-Ferrero, Gonzalo Vegas, Veitel, Lucas, Washko, George R., Wilson, Carla G., Jensen, Robert, Everett, Douglas, Crooks, Jim, Pratte, Katherine, Strand, Matt, Kinney, Gregory, Young, Kendra A., Bhatt, Surya P., Bon, Jessica, Diaz, Alejandro A., Make, Barry, Murray, Susan, Regan, Elizabeth, Soler, Xavier, Bowler, Russell P., Kechris, Katerina, Banaei-Kashani, Farnoush, Boueiz, Adel, Yun, Jeong, Washko, George, Cho, Michael H., Kinney, Gregory L., Criner, Gerald J., Dy, Jennifer G., Rennard, Stephen I., Casaburi, Richard, and Crapo, James

Published
2020
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21. Association Between Interstitial Lung Abnormalities and All-Cause Mortality

Author

Putman, Rachel K, Hatabu, Hiroto, Araki, Tetsuro, Gudmundsson, Gunnar, Gao, Wei, Nishino, Mizuki, Okajima, Yuka, Dupuis, Josée, Latourelle, Jeanne C, Cho, Michael H, El-Chemaly, Souheil, Coxson, Harvey O, Celli, Bartolome R, Fernandez, Isis E, Zazueta, Oscar E, Ross, James C, Harmouche, Rola, San José Estépar, Raúl, Diaz, Alejandro A, Sigurdsson, Sigurdur, Gudmundsson, Elías F, Eiríksdottír, Gudny, Aspelund, Thor, Budoff, Matthew J, Kinney, Gregory L, Hokanson, John E, Williams, Michelle C, Murchison, John T, MacNee, William, Hoffmann, Udo, O’Donnell, Christopher J, Launer, Lenore J, Harrris, Tamara B, Gudnason, Vilmundur, Silverman, Edwin K, O’Connor, George T, Washko, George R, Rosas, Ivan O, and Hunninghake, Gary M

Subjects
Epidemiology, Health Sciences, Chronic Obstructive Pulmonary Disease, Lung, Human Genome, Clinical Research, Genetics, Aetiology, 2.4 Surveillance and distribution, Respiratory, Cause of Death, Cohort Studies, Coronary Artery Disease, Female, Humans, Male, Neoplasms, Prevalence, Proportional Hazards Models, Prospective Studies, Pulmonary Disease, Chronic Obstructive, Pulmonary Emphysema, Radiography, Smoking, Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints (ECLIPSE) Investigators, COPDGene Investigators, Medical and Health Sciences, General & Internal Medicine, Biomedical and clinical sciences, Health sciences
Abstract

ImportanceInterstitial lung abnormalities have been associated with lower 6-minute walk distance, diffusion capacity for carbon monoxide, and total lung capacity. However, to our knowledge, an association with mortality has not been previously investigated.ObjectiveTo investigate whether interstitial lung abnormalities are associated with increased mortality.Design, setting, and populationProspective cohort studies of 2633 participants from the FHS (Framingham Heart Study; computed tomographic [CT] scans obtained September 2008-March 2011), 5320 from the AGES-Reykjavik Study (Age Gene/Environment Susceptibility; recruited January 2002-February 2006), 2068 from the COPDGene Study (Chronic Obstructive Pulmonary Disease; recruited November 2007-April 2010), and 1670 from ECLIPSE (Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints; between December 2005-December 2006).ExposuresInterstitial lung abnormality status as determined by chest CT evaluation.Main outcomes and measuresAll-cause mortality over an approximate 3- to 9-year median follow-up time. Cause-of-death information was also examined in the AGES-Reykjavik cohort.ResultsInterstitial lung abnormalities were present in 177 (7%) of the 2633 participants from FHS, 378 (7%) of 5320 from AGES-Reykjavik, 156 (8%) of 2068 from COPDGene, and in 157 (9%) of 1670 from ECLIPSE. Over median follow-up times of approximately 3 to 9 years, there were more deaths (and a greater absolute rate of mortality) among participants with interstitial lung abnormalities when compared with those who did not have interstitial lung abnormalities in the following cohorts: 7% vs 1% in FHS (6% difference [95% CI, 2% to 10%]), 56% vs 33% in AGES-Reykjavik (23% difference [95% CI, 18% to 28%]), and 11% vs 5% in ECLIPSE (6% difference [95% CI, 1% to 11%]). After adjustment for covariates, interstitial lung abnormalities were associated with a higher risk of death in the FHS (hazard ratio [HR], 2.7 [95% CI, 1.1 to 6.5]; P = .03), AGES-Reykjavik (HR, 1.3 [95% CI, 1.2 to 1.4]; P

Published
2016

23. Common Genetic Variants Associated with Resting Oxygenation in Chronic Obstructive Pulmonary Disease

Author

McDonald, Merry-Lynn N, Cho, Michael H, Sørheim, Inga-Cecilie, Lutz, Sharon M, Castaldi, Peter J, Lomas, David A, Coxson, Harvey O, Edwards, Lisa D, MacNee, William, Vestbo, Jørgen, Yates, Julie C, Agusti, Alvar, Calverley, Peter MA, Celli, Bartolome, Crim, Courtney, Rennard, Stephen I, Wouters, Emiel FM, Bakke, Per, Tal-Singer, Ruth, Miller, Bruce E, Gulsvik, Amund, Casaburi, Richard, Wells, J Michael, Regan, Elizabeth A, Make, Barry J, Hokanson, John E, Lange, Christoph, Crapo, James D, Beaty, Terri H, Silverman, Edwin K, and Hersh, Craig P

Subjects
Biological Sciences, Biomedical and Clinical Sciences, Genetics, Lung, Prevention, Clinical Research, Chronic Obstructive Pulmonary Disease, Human Genome, 2.1 Biological and endogenous factors, Aetiology, Respiratory, Black or African American, Aged, Aged, 80 and over, Chromosomes, Human, Pair 15, Female, Genetic Predisposition to Disease, Genetic Variation, Genome-Wide Association Study, Humans, Hypoxia, Male, Middle Aged, Oximetry, Oxygen, Polymorphism, Single Nucleotide, Prognosis, Pulmonary Disease, Chronic Obstructive, Rest, White People, chronic obstructive pulmonary disease, hypoxemia, pulse oximetry, genome-wide association study, oxygen saturation, Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints and COPDGene Investigators, Cardiorespiratory Medicine and Haematology, Respiratory System, Biochemistry and cell biology, Cardiovascular medicine and haematology
Abstract

Hypoxemia is a major complication of chronic obstructive pulmonary disease (COPD) that correlates with disease prognosis. Identifying genetic variants associated with oxygenation may provide clues for deciphering the heterogeneity in prognosis among patients with COPD. However, previous genetic studies have been restricted to investigating COPD candidate genes for association with hypoxemia. To report results from the first genome-wide association study (GWAS) of resting oxygen saturation (as measured by pulse oximetry [Spo2]) in subjects with COPD, we performed a GWAS of Spo2 in two large, well characterized COPD populations: COPDGene, including both the non-Hispanic white (NHW) and African American (AA) groups, and Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints (ECLIPSE). We identified several suggestive loci (P < 1 × 10(-5)) associated with Spo2 in COPDGene in the NHW (n = 2810) and ECLIPSE (n = 1758) groups, and two loci on chromosomes 14 and 15 in the AA group (n = 820) from COPDGene achieving a level of genome-wide significance (P < 5 × 10(-8)). The chromosome 14 single-nucleotide polymorphism, rs6576132, located in an intergenic region, was nominally replicated (P < 0.05) in the NHW group from COPDGene. The chromosome 15 single-nucleotide polymorphisms were rare in subjects of European ancestry, so the results could not be replicated. The chromosome 15 region contains several genes, including TICRR and KIF7, and is proximal to RHCG (Rh family C glyocoprotein gene). We have identified two loci associated with resting oxygen saturation in AA subjects with COPD, and several suggestive regions in subjects of European descent with COPD. Our study highlights the importance of investigating the genetics of complex traits in different racial groups.

Published
2014

24. The St. George’s Respiratory Questionnaire Definition of Chronic Bronchitis May Be a Better Predictor of COPD Exacerbations Compared With the Classic Definition

Author

Crapo, James D., Silverman, Edwin K., Make, Barry J., Regan, Elizabeth A., Beaty, Terri, Begum, Ferdouse, Busch, Robert, Castaldi, Peter J., Cho, Michael, DeMeo, Dawn L., Boueiz, Adel R., Foreman, Marilyn G., Halper-Stromberg, Eitan, Hansel, Nadia N., Hardin, Megan E., Hayden, Lystra P., Hersh, Craig P., Hetmanski, Jacqueline, Hobbs, Brian D., Hokanson, John E., Laird, Nan, Lange, Christoph, Lutz, Sharon M., McDonald, Merry-Lynn, Parker, Margaret M., Qiao, Dandi, Santorico, Stephanie, Wan, Emily S., Won, Sungho, Charbonnier, Jean-Paul, Coxson, Harvey O., Han, MeiLan K., Hoffman, Eric A., Humphries, Stephen, Jacobson, Francine L., Judy, Philip F., Kazerooni, Ella A., Kluiber, Alex, Lynch, David A., Newell, John D., Jr., Ross, James C., San Jose Estepar, Raul, Sieren, Jered, Stoel, Berend C., Tschirren, Juerg, Van Beek, Edwin, van Ginneken, Bram, van Rikxoort, Eva, Washko, George, Wilson, Carla G., Jensen, Robert, Everett, Douglas, Crooks, Jim, Moore, Camille, Strand, Matt, Hughes, John, Kinney, Gregory, Pratte, Katherine, Young, Kendra A., Curtis, Jeffrey L., Martinez, Carlos H., Pernicano, Perry G., Hanania, Nicola, Alapat, Philip, Atik, Mustafa, Bandi, Venkata, Boriek, Aladin, Guntupalli, Kalpatha, Guy, Elizabeth, Nachiappan, Arun, Parulekar, Amit, Hersh, Craig, Barr, R. Graham, Austin, John, D’Souza, Belinda, Pearson, Gregory D.N., Rozenshtein, Anna, Thomashow, Byron, MacIntyre, Neil, Jr., McAdams, H. Page, Washington, Lacey, McEvoy, Charlene, Tashjian, Joseph, Wise, Robert, Brown, Robert, Horton, Karen, Lambert, Allison, Putcha, Nirupama, Casaburi, Richard, Adami, Alessandra, Budoff, Matthew, Fischer, Hans, Porszasz, Janos, Rossiter, Harry, Stringer, William, Sharafkhaneh, Amir, Lan, Charlie, Wendt, Christine, Bell, Brian, Berkowitz, Eugene, Flenaugh, Eric L., Westney, Gloria, Bowler, Russell, Rosiello, Richard, Pace, David, Criner, Gerard, Ciccolella, David, Cordova, Francis, Dass, Chandra, D’Alonzo, Gilbert, Desai, Parag, Jacobs, Michael, Kelsen, Steven, Kim, Victor, Mamary, A. James, Marchetti, Nathaniel, Satti, Aditi, Shenoy, Kartik, Steiner, Robert M., Vega-Sanchez, Maria Elena, Dransfield, Mark, Bailey, William, Bhatt, Surya, Iyer, Anand, Nath, Hrudaya, Oates, Gabriela, Sonavane, Sushil, Wells, J. Michael, Ramsdell, Joe, Friedman, Paul, Soler, Xavier, Yen, Andrew, Comellas, Alejandro P., Newell, John, Jr., Thompson, Brad, Kazerooni, Ella, Billings, Joanne, Begnaud, Abbie, Allen, Tadashi, Sciurba, Frank, Bon, Jessica, Chandra, Divay, Fuhrman, Carl, Weissfeld, Joel, Anzueto, Antonio, Adams, Sandra, Maselli-Caceres, Diego, Ruiz, Mario E., Zhao, Huaqing, Regan, Elizabeth, Jones, Paul W., and Criner, Gerard J.

Published
2019
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25. Quantitative computed tomography measures of pectoralis muscle area and disease severity in chronic obstructive pulmonary disease. A cross-sectional study.

Author

McDonald, Merry-Lynn N, Diaz, Alejandro A, Ross, James C, San Jose Estepar, Raul, Zhou, Linfu, Regan, Elizabeth A, Eckbo, Eric, Muralidhar, Nina, Come, Carolyn E, Cho, Michael H, Hersh, Craig P, Lange, Christoph, Wouters, Emiel, Casaburi, Richard H, Coxson, Harvey O, Macnee, William, Rennard, Stephen I, Lomas, David A, Agusti, Alvar, Celli, Bartolome R, Black-Shinn, Jennifer L, Kinney, Greg L, Lutz, Sharon M, Hokanson, John E, Silverman, Edwin K, and Washko, George R

Subjects
Pectoralis Muscles, Humans, Pulmonary Disease, Chronic Obstructive, Tomography, X-Ray Computed, Respiratory Function Tests, Body Mass Index, Severity of Illness Index, Case-Control Studies, Cohort Studies, Predictive Value of Tests, Smoking, Body Composition, Aged, Middle Aged, Female, Male, Chronic Obstructive Pulmonary Disease, Clinical Research, Obesity, Prevention, Nutrition, Lung, Musculoskeletal, Respiratory
Abstract

RationaleMuscle wasting in chronic obstructive pulmonary disease (COPD) is associated with a poor prognosis and is not readily assessed by measures of body mass index (BMI). BMI does not discriminate between relative proportions of adipose tissue and lean muscle and may be insensitive to early pathologic changes in body composition. Computed tomography (CT)-based assessments of the pectoralis muscles may provide insight into the clinical significance of skeletal muscles in smokers.ObjectivesWe hypothesized that objective assessment of the pectoralis muscle area on chest CT scans provides information that is clinically relevant and independent of BMI.MethodsData from the ECLIPSE (Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints) Study (n = 73) were used to assess the relationship between pectoralis muscle area and fat-free mass. We then used data in a subset (n = 966) of a larger cohort, the COPDGene (COPD Genetic Epidemiology) Study, to explore the relationship between pectoralis muscle area and COPD-related traits.Measurements and main resultsWe first investigated the correlation between pectoralis muscle area and fat-free mass, using data from a subset of participants in the ECLIPSE Study. We then further investigated pectoralis muscle area in COPDGene Study participants and found that higher pectoralis muscle area values were associated with greater height, male sex, and younger age. On subsequent clinical correlation, compared with BMI, pectoralis muscle area was more significantly associated with COPD-related traits, including spirometric measures, dyspnea, and 6-minute-walk distance (6MWD). For example, on average, each 10-cm(2) increase in pectoralis muscle area was associated with a 0.8-unit decrease in the BODE (Body mass index, Obstruction, Dyspnea, Exercise) index (95% confidence interval, -1.0 to -0.6; P < 0.001). Furthermore, statistically significant associations between pectoralis muscle area and COPD-related traits remained even after adjustment for BMI.ConclusionsCT-derived pectoralis muscle area provides relevant indices of COPD morbidity that may be more predictive of important COPD-related traits than BMI. However, the relationship with clinically relevant outcomes such as hospitalization and death requires additional investigation. Pectoralis muscle area is a convenient measure that can be collected in the clinical setting in addition to BMI.

Published
2014

30. Prevalence of abnormal spirometry in individuals with a smoking history and no known obstructive lung disease

Author

Tran, Thuonghien V., primary, Kinney, Gregory L., additional, Comellas, Alejandro, additional, Hoth, Karin F., additional, Baldomero, Arianne K., additional, Mamary, A. James, additional, Curtis, Jeffrey L., additional, Hanania, Nicola, additional, Casaburi, Richard, additional, Young, Kendra A., additional, Kim, Victor, additional, Make, Barry, additional, Wan, Emily S., additional, Diaz, Alejandro A., additional, Hokanson, John, additional, Crapo, James D., additional, Silverman, Edwin K., additional, Bhatt, Surya P., additional, Regan, Elizabeth, additional, Fortis, Spyridon, additional, Make, Barry J., additional, Regan, Elizabeth A., additional, Beaty, Terri H., additional, Castaldi, Peter J., additional, Cho, Michael H., additional, DeMeo, Dawn L., additional, El Boueiz, Adel, additional, Foreman, Marilyn G., additional, Ghosh, Auyon, additional, Hayden, Lystra P., additional, Hersh, Craig P., additional, Hetmanski, Jacqueline, additional, Hobbs, Brian D., additional, Hokanson, John E., additional, Kim, Wonji, additional, Laird, Nan, additional, Lange, Christoph, additional, Lutz, Sharon M., additional, McDonald, Merry-Lynn, additional, Prokopenko, Dmitry, additional, Moll, Matthew, additional, Morrow, Jarrett, additional, Qiao, Dandi, additional, Saferali, Aabida, additional, Sakornsakolpat, Phuwanat, additional, Yun, Jeong, additional, Centeno, Juan Pablo, additional, Charbonnier, Jean-Paul, additional, Coxson, Harvey O., additional, Galban, Craig J., additional, Han, MeiLan K., additional, Hoffman, Eric A., additional, Humphries, Stephen, additional, Jacobson, Francine L., additional, Judy, Philip F., additional, Kazerooni, Ella A., additional, Kluiber, Alex, additional, Lynch, David A., additional, Nardelli, Pietro, additional, Newell, John D., additional, Notary, Aleena, additional, Oh, Andrea, additional, Ross, James C., additional, San Jose Estepar, Raul, additional, Schroeder, Joyce, additional, Sieren, Jered, additional, Stoel, Berend C., additional, Tschirren, Juerg, additional, Van Beek, Edwin, additional, van Ginneken, Bram, additional, van Rikxoort, Eva, additional, Sanchez Ferrero, Gonzalo Vegas, additional, Veitel, Lucas, additional, Washko, George R., additional, Wilson, Carla G., additional, Jensen, Robert, additional, Everett, Douglas, additional, Crooks, Jim, additional, Pratte, Katherine, additional, Strand, Matt, additional, Austin, Erin, additional, Kinney, Gregory, additional, Bon, Jessica, additional, Murray, Susan, additional, Soler, Xavier, additional, Bowler, Russell P., additional, Kechris, Katerina, additional, BanaeiKashani, Farnoush, additional, Pernicano, Perry G., additional, Atik, Mustafa, additional, Boriek, Aladin, additional, Guntupalli, Kalpatha, additional, Guy, Elizabeth, additional, Parulekar, Amit, additional, Hersh, Craig, additional, Washko, George, additional, Barr, R. Graham, additional, Austin, John, additional, D'Souza, Belinda, additional, Thomashow, Byron, additional, MacIntyre, Neil, additional, McAdams, H. Page, additional, Washington, Lacey, additional, McEvoy, Charlene, additional, Tashjian, Joseph, additional, Wise, Robert, additional, Brown, Robert, additional, Hansel, Nadia N., additional, Horton, Karen, additional, Lambert, Allison, additional, Putcha, Nirupama, additional, Adami, Alessandra, additional, Budoff, Matthew, additional, Fischer, Hans, additional, Porszasz, Janos, additional, Rossiter, Harry, additional, Stringer, William, additional, Sharafkhaneh, Amir, additional, Lan, Charlie, additional, Wendt, Christine, additional, Bell, Brian, additional, Kunisaki, Ken M., additional, Flenaugh, Eric L., additional, Gebrekristos, Hirut, additional, Ponce, Mario, additional, Terpenning, Silanath, additional, Westney, Gloria, additional, Bowler, Russell, additional, Rosiello, Richard, additional, Pace, David, additional, Criner, Gerard, additional, Ciccolella, David, additional, Cordova, Francis, additional, Dass, Chandra, additional, D'Alonzo, Gilbert, additional, Desai, Parag, additional, Jacobs, Michael, additional, Kelsen, Steven, additional, Marchetti, Nathaniel, additional, Satti, Aditi, additional, Shenoy, Kartik, additional, Steiner, Robert M., additional, Swift, Alex, additional, Swift, Irene, additional, Vega-Sanchez, Maria Elena, additional, Dransfield, Mark, additional, Bailey, William, additional, Iyer, Anand, additional, Nath, Hrudaya, additional, Wells, J. Michael, additional, Conrad, Douglas, additional, Yen, Andrew, additional, Comellas, Alejandro P., additional, Newell, John, additional, Thompson, Brad, additional, Kazerooni, Ella, additional, Labaki, Wassim, additional, Galban, Craig, additional, Vummidi, Dharshan, additional, Billings, Joanne, additional, Begnaud, Abbie, additional, Allen, Tadashi, additional, Sciurba, Frank, additional, Chandra, Divay, additional, Weissfeld, Joel, additional, Anzueto, Antonio, additional, Adams, Sandra, additional, Maselli-Caceres, Diego, additional, Ruiz, Mario E., additional, and Singh, Harjinder, additional

Published
2023
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31. Clinical Markers Associated With Risk of Suicide or Drug Overdose Among Individuals With Smoking Exposure

Author

Adviento, Brigid A., primary, Regan, Elizabeth A., additional, Make, Barry J., additional, Han, MeiLan K., additional, Foreman, Marilyn G., additional, Iyer, Anand S., additional, Bhatt, Surya P., additional, Kim, Victor, additional, Bon, Jessica, additional, Soler, Xavier, additional, Kinney, Gregory L., additional, Hanania, Nicola A., additional, Lowe, Katherine E., additional, Holm, Kristen E., additional, Yohannes, Abebaw M., additional, Shinozaki, Gen, additional, Hoth, Karin F., additional, Fiedorowicz, Jess G., additional, Crapo, James D., additional, Silverman, Edwin K., additional, Beaty, Terri H., additional, Castaldi, Peter J., additional, Cho, Michael H., additional, DeMeo, Dawn L., additional, El Boueiz, Adel, additional, Ghosh, Auyon, additional, Hayden, Lystra P., additional, Hersh, Craig P., additional, Hetmanski, Jacqueline, additional, Hobbs, Brian D., additional, Hokanson, John E., additional, Kim, Wonji, additional, Laird, Nan, additional, Lange, Christoph, additional, Lutz, Sharon M., additional, McDonald, Merry-Lynn, additional, Prokopenko, Dmitry, additional, Moll, Matthew, additional, Morrow, Jarrett, additional, Qiao, Dandi, additional, Saferali, Aabida, additional, Sakornsakolpat, Phuwanat, additional, Wan, Emily S., additional, Yun, Jeong, additional, Centeno, Juan Pablo, additional, Charbonnier, Jean-Paul, additional, Coxson, Harvey O., additional, Galban, Craig J., additional, Hoffman, Eric A., additional, Humphries, Stephen, additional, Jacobson, Francine L., additional, Judy, Philip F., additional, Kazerooni, Ella A., additional, Kluiber, Alex, additional, Lynch, David A., additional, Nardelli, Pietro, additional, Newell, John D., additional, Notary, Aleena, additional, Oh, Andrea, additional, Ross, James C., additional, San Jose Estepar, Raul, additional, Schroeder, Joyce, additional, Sieren, Jered, additional, Stoel, Berend C., additional, Tschirren, Juerg, additional, Van Beek, Edwin, additional, van Ginneken, Bram, additional, van Rikxoort, Eva, additional, Sanchez-Ferrero, Gonzalo Vegas, additional, Veitel, Lucas, additional, Washko, George R., additional, Wilson, Carla G., additional, Jensen, Robert, additional, Strand, Matthew, additional, Crooks, Jim, additional, Pratte, Katherine, additional, Khatiwada, Aastha, additional, Austin, Erin, additional, Kinney, Gregory, additional, Young, Kendra A., additional, Diaz, Alejandro A., additional, Make, Barry, additional, Murray, Susan, additional, Regan, Elizabeth, additional, Bowler, Russell P., additional, Kechris, Katerina, additional, Banaei-Kashani, Farnoush, additional, Curtis, Jeffrey L., additional, Pernicano, Perry G., additional, Hanania, Nicola, additional, Atik, Mustafa, additional, Boriek, Aladin, additional, Guntupalli, Kalpatha, additional, Guy, Elizabeth, additional, Parulekar, Amit, additional, Hersh, Craig, additional, Washko, George, additional, Barr, R. Graham, additional, Austin, John, additional, D’Souza, Belinda, additional, Thomashow, Byron, additional, MacIntyre, Neil, additional, McAdams, H. Page, additional, Washington, Lacey, additional, McEvoy, Charlene, additional, Tashjian, Joseph, additional, Wise, Robert, additional, Brown, Robert, additional, Hansel, Nadia N., additional, Horton, Karen, additional, Lambert, Allison, additional, Putcha, Nirupama, additional, Casaburi, Richard, additional, Adami, Alessandra, additional, Budoff, Matthew, additional, Fischer, Hans, additional, Porszasz, Janos, additional, Rossiter, Harry, additional, Stringer, William, additional, Sharafkhaneh, Amir, additional, Lan, Charlie, additional, Wendt, Christine, additional, Bell, Brian, additional, Kunisaki, Ken M., additional, Flenaugh, Eric L., additional, Gebrekristos, Hirut, additional, Ponce, Mario, additional, Terpenning, Silanath, additional, Westney, Gloria, additional, Bowler, Russell, additional, Rosiello, Richard, additional, Pace, David, additional, Criner, Gerard, additional, Ciccolella, David, additional, Cordova, Francis, additional, Dass, Chandra, additional, D’Alonzo, Gilbert, additional, Desai, Parag, additional, Jacobs, Michael, additional, Kelsen, Steven, additional, Mamary, A. James, additional, Marchetti, Nathaniel, additional, Satti, Aditi, additional, Shenoy, Kartik, additional, Steiner, Robert M., additional, Swift, Alex, additional, Swift, Irene, additional, Vega-Sanchez, Maria Elena, additional, Dransfield, Mark, additional, Bailey, William, additional, Iyer, Anand, additional, Nath, Hrudaya, additional, Wells, J. Michael, additional, Conrad, Douglas, additional, Yen, Andrew, additional, Comellas, Alejandro P., additional, Newell, John, additional, Thompson, Brad, additional, Kazerooni, Ella, additional, Labaki, Wassim, additional, Galban, Craig, additional, Vummidi, Dharshan, additional, Billings, Joanne, additional, Begnaud, Abbie, additional, Allen, Tadashi, additional, Sciurba, Frank, additional, Chandra, Divay, additional, Weissfeld, Joel, additional, Anzueto, Antonio, additional, Adams, Sandra, additional, Maselli-Caceres, Diego, additional, Ruiz, Mario E., additional, and Singh, Harjinder, additional

Published
2023
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37. The Role of Chest Computed Tomography in the Evaluation and Management of the Patient with Chronic Obstructive Pulmonary Disease

Author

Labaki, Wassim W., Martinez, Carlos H., Martinez, Fernando J., Galbán, Craig J., Ross, Brian D., Washko, George R., Barr, Graham R., Regan, Elizabeth A., Coxson, Harvey O., Hoffman, Eric A., Newell, John D., Jr., Curran-Everett, Douglas, Hogg, James C., Crapo, James D., Lynch, David A., Kazerooni, Ella A., and Han, MeiLan K.

Published
2017
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47. Quantitative CT Lung Imaging and Machine Learning Improves Prediction of Emergency Room Visits and Hospitalizations in COPD.

Author

Moslemi, Amir, Makimoto, Kalysta, Tan, Wan C., Bourbeau, Jean, Hogg, James C., Coxson, Harvey O., and Kirby, Miranda

Abstract

Predicting increased risk of future healthcare utilization in chronic obstructive pulmonary disease (COPD) patients is an important goal for improving patient management. Our objective was to determine the importance of computed tomography (CT) lung imaging measurements relative to other demographic and clinical measurements for predicting future health services use with machine learning in COPD. In this retrospective study, lung function measurements and chest CT images were acquired from Canadian Cohort of Obstructive Lung Disease study participants from 2010 to 2017 (https://clinicaltrials.gov , NCT00920348). Up to two follow-up visits (1.5- and 3-year follow-up) were performed and participants were asked for details related to healthcare utilization. Healthcare utilization was defined as any COPD hospitalization or emergency room visit due to respiratory problems in the 12 months prior to the follow-up visits. CT analysis was performed (VIDA Diagnostics Inc.); a total of 108 CT quantitative emphysema, airway and vascular measurements were investigated. A hybrid feature selection method with support vector machine classifier was used to predict healthcare utilization. Performance was determined using accuracy, F1-measure and area under the receiver operating characteristic curve (AUC) and Matthews's correlation coefficient (MC). Of the 527 COPD participants evaluated, 179 (35%) used healthcare services at follow-up. There were no significant differences between the participants with or without healthcare utilization at follow-up for age (p = 0.50), sex (p = 0.44), BMI (p = 0.05) or pack-years (p = 0.76). The accuracy for predicting subsequent healthcare utilization was 80% ± 3% (F1-measure = 74%, AUC = 0.80, MC = 0.6) when all measurements were considered, 76% ± 6% (F1-measure = 72%, AUC = 0.77, MC = 0.55) for CT measurements alone and 65% ± 5% (F1-measure = 60%, AUC = 0.67, MC = 0.34) for demographic and lung function measurements alone. The combination of CT lung imaging and conventional measurements leads to greater prediction accuracy of subsequent health services use than conventional measurements alone, and may provide needed prognostic information for patients suffering from COPD. [ABSTRACT FROM AUTHOR]

Published
2023
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