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COPD phenotypes in biomass smoke – versus tobacco smoke-exposed Mexican women Ramirez-Venegas, Alejandra; Sansores, Raul H.; Alva, Luis F; McDougall, Jill E.; Sin, Don D.; Silva, C. Isabela S.; Rojas, Carlos E.; Coxson, Harvey O.; Camp, Patricia G.; Paré, P.D.; Müller, Nestor Luiz, 1948- 2014

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1   COPD PHENOTYPES IN BIOMASS SMOKE- VERSUS TOBACCO SMOKE-EXPOSED MEXICAN WOMEN  *Pat G. Camp, PhD1 2, *Alejandra Ramirez-Venegas, MD3, Raul H. Sansores, MD3, Luis F Alva, MD4,  Jill E. McDougall, BSN1, Don D. Sin, MD1 5, Peter D. Paré, MD1 5, Nestor L. Müller, MD6, C. Isabela S. Silva, MD7, Carlos E. Rojas, MD4, and Harvey O. Coxson, PhD1 6  *These authors made equal contributions to this work  1  University of British Columbia James Hogg Research Center, Vancouver, Canada  St. Paul’s Hospital  1081 Burrard Street  Vancouver, British Columbia  V6Z 1Y6  Canada  2      University of British Columbia Department of Physical Therapy, Vancouver, Canada  212 – 2177 Wesbrook Mall  Vancouver, British Columbia  V6T 1Z3  Canada  3  Departamento de Investigación en Tabaquismo y EPOC, Instituto Nacional de  Enfermedades Respiratorias Ismael Cosio Villegas  Calz.Tlalpan, No. 4502  Col. Seccion XVI  Deleg. Tlalpan, C.P. 14080  Mexico, DF  Mexico       2  4 Hospital Medica Sur, Mexico City  Unidad de Radiología e Imágen  Puente de Piedra 150  Col Toriello Guerra  Deleg Tlalpan  Mexico, DF  Mexico    5   University of British Columbia Department of Medicine  2775 Laurel Street, 10th Floor Vancouver, British Columbia  V5Z 1M9  Canada  6  University of British Columbia Department of Radiology  3350-95- West 10th Avenue  Vancouver, B.C.  V5Z 4E3  Canada  7 Department of Radiology  Clinica Delfin and Portuguese Hospital  Rua Prof. Amilcar Falcao n 193, ap.1801  Barra – Salvador, Bahia  CEP 40140-480  Brazil  Corresponding Author: Dr. Pat Camp     UBC James Hogg Research Centre     St. Paul’s Hospital     1081 Burrard Street     Vancouver, B.C.     V6Z 1Y6     Canada     Email:  pat.camp@hli.ubc.ca,  Telephone:  604-806-9144  Authors’ Contributions P. Camp and A. Ramirez-Venegas had primary responsibility for the development of the research question and design. P. Camp had primary responsibility for data analysis and wrote 3  the manuscript. A. Ramirez-Venegas performed the data collection and co-wrote the manuscript. R.H. Sansores provided guidance with the development of the research question, performed the data collection, and contributed to the manuscript. J. McDougall assisted with the data collection and analysis and contributed to the manuscript. Luis F Alva assisted with the patient recruitment, data collection and arranging for the CT scan acquisition.  D. Sin provided guidance on the analysis and contributed to the manuscript. P. Paré provided guidance with the development of the research question, the data analysis, and contributed to the manuscript. N. Müller assisted with the data collection, scored the CT scans, and contributed to the manuscript. I. Silva assisted with the data collection, scored the CT scans, and contributed to the manuscript. C. Rojas assisted with the data collection and contributed to the manuscript. H. Coxson provided guidance with the development of the research question and the interpretation of the data and contributed to the manuscript.    Funding This study was funded by a non-restricted research grant from GlaxoSmithKline Canada, from a Canadian Institute of Health Research ICEBERGS Team Grant, and internal research funds held at the Instituto Nacional de Enfermedades Respiratorias Ismael Cosio Villegas. At the time of this study, PGC was funded by a Canadian Institute of Health Research Fellowship, a Canadian Respiratory Health Professional Fellowship, and a trainee award from ICEBERGS, a CIHR Interdisciplinary Enhancement Team Grant. She is currently a Michael Smith Foundation for Health Research Clinical Scholar. HOC was a Canadian Institutes of Health Research/British Columbia Lung Association New Investigator and is currently funded in part by Pittsburgh COPD 4  SCCOR NIH 1P50 HL084948 and R01 HL085096 from the National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD to the University of Pittsburgh and the BC Lung Association Robert R Miller Fellowship in Thoracic Imaging. DDS holds Canada Research Chair in COPD. PDP is a Michael Smith Foundation for Health Research Distinguished Scholar and the Jacob Churg Distinguished Researcher.  5  ABSTRACT We hypothesized that biomass smoke exposure is associated with an airway-predominant COPD phenotype, while tobacco-related COPD is associated with an emphysema-predominant phenotype.   In this cross-sectional study, female never-smokers with COPD and biomass exposure (n=21) and female ex-cigarette smokers with COPD without biomass exposure (n=22) completed computed tomography (CT) at inspiration and expiration, pulmonary function, blood gas, exercise tolerance, and quality of life measures.  Two radiologists scored the extent of emphysema and air trapping on CT.  Quantitative emphysema severity and distribution, and airway wall thickness were calculated using specialized software.    Women in the tobacco group had significantly more emphysema than the biomass group (radiologist score 2·3 vs 0·7, p=0·001; % emphysema on CT scan 27% vs 19%, p=0·046; and a larger size of emphysematous spaces, p=0·006). Women in the biomass group had significantly more air trapping than the tobacco group (radiologist score = 2·6 and 1·5 respectively; p=0·02) and also scored lower on the symptom, activities and confidence domains of quality of life and had lower oxygen saturation at rest and during exercise (p<0·05).   Biomass smoke exposure is associated with less emphysema but more air trapping than tobacco smoke exposure, suggesting an airway-predominant phenotype.  6   KEY WORDS  Chronic obstructive pulmonary disease Emphysema Bronchiolitis Computed tomography  7   Biomass smoke is a risk factor for the development of airflow obstruction and chronic obstructive pulmonary disease (COPD), especially in developing countries (1-4). Approximately 50% of the world’s population use biomass fuels for cooking, light, and heat, often in dwellings where ventilation is poor. The clinical characteristics of COPD associated with exposure to biomass smoke have been well described. Ramirez-Venegas et al (5) reported that COPD patients exposed to biomass smoke were more likely to be women, with similar symptoms, exercise capacity, quality of life, and need for supplementary oxygen, yet have less severe airflow obstruction than tobacco smokers with COPD.    There are two major phenotypes of COPD: emphysema and bronchiolitis, also referred to as “small airways disease”. In tobacco-related COPD, the two phenotypes generally co-exist in variable proportions. Little is known about biomass-related COPD phenotypes because COPD pathophysiology related to biomass smoke is poorly understood. In a study of human lung tissue from autopsy, Rivera et al (6) found that the lungs of individuals who were exposed to wood smoke demonstrated more severe bronchiolitis and less emphysema. Details on lung disease diagnosis or other clinical characteristics of these patients were not available. It is not known if similar findings would be detected in living patients with confirmed COPD.   In vivo assessment of COPD phenotypes is now possible with the advent of multi-detector computed tomography (MDCT) scanning. This technology has made the investigation of the site, magnitude, and distribution of parenchymal destruction, gas trapping, airway remodeling 8  and airway narrowing possible. We hypothesized that biomass smoke exposure is associated with an airway-predominant phenotype of COPD, with increased air trapping and airway wall thickness, while tobacco-related COPD would be associated with an emphysema phenotype.  METHODS Study Setting This was a cross-sectional comparison of two groups of women with COPD who had comparable airflow obstruction but different exposure histories. The study was performed in the COPD clinic of the Instituto Nacional de Enfermedades Respiratoris Ismael Cosio Villegas (INER) in Mexico City, Mexico. This facility is a public tertiary-care centre that focuses on medical care, teaching and research and provides health care services to the economically-deprived population of Mexico. The study was approved by the ethical review boards at INER (Comite de Ciencia y Bioetica en Investigacion C08-05), and the University of British Columbia (Providence Health Care H07-00095) in Vancouver and all women provided written, informed consent. This study was funded by the Research Program at INER (patient recruitment, testing, and technical support); and by GlaxoSmithKline Canada and the Canadian Institutes of Health Research (CT scan acquisition and analysis).     Diagnosis of COPD We recruited women from a cohort of female patients with COPD from INER. Eligible women were between 40 and 80 years old, had post-bronchodilator spirometric evidence of COPD (the ratio of forced expiratory volume in the first second (FEV1) to forced vital capacity (FVC) less 9  than 0·70; and FEV1 % predicted less than 80%) and past exposure to either biomass or tobacco smoke. Women with biomass smoke exposure had never smoked cigarettes and had at least six months of daily biomass smoke exposure but were not currently exposed. Women with tobacco smoke exposure had at least ten pack years of smoking history, were ex-smokers, and had never been exposed to biomass smoke. Participants were exacerbation-free for at least one month. Each biomass-smoke exposed woman was matched by age (within 5 years) and post-bronchodilator FEV1 % predicted (within 10%) to a woman exposed to cigarette smoke to reduce the impact of confounding factors.   Outcomes Smoke exposure was determined by the standardized Spanish version of the American Thoracic Society (ATS) questionnaire (7) supplemented by questions related to cooking fuels. The questions related to exposure to biomass fuels are presented in the online supplement.  Cumulative exposure to biomass was expressed as hour-years, calculated by multiplying the number of years cooking with wood stoves by the average daily hours spent in the kitchen.  Subjects underwent pre- and post-bronchodilator spirometry with a dry rolling seal volume spirometer (Sensormedics, Yorba Linda, CA) and plethysomographic lung volume measurements following the procedures recommended by the ATS. We used Mexican standard reference equations for predicted values of FEV1 and FVC (8) which are similar to the third National Health and Nutrition Examination Survey values for Mexican Americans. Arterial blood gas samples were taken at rest on room air. Mexico City’s mean altitude is 2,240 m above sea 10  level.  In Mexico City, normal mean partial pressure of arterial oxygen (PaO2) and carbon dioxide (PaCO2) values in young subjects are 66 to 72 and 28 to 32 mmHg, respectively, and oxyhemoglobin saturation values are typically 95 to 96%.  Each woman completed a six minute walk test and the percent predicted of the distance walked was calculated (9).  We measured oxyhemoglobin saturation (SpO2) and heart rate with a pulse oximeter and dyspnea with the Borg scale (10) at rest and at the end of the walk test. Subjects also completed the modified Medical Research Council (mMRC) Dyspnea Scale (11), the St. George’s Respiratory Questionnaire (SGRQ) (12) and the Chronic Respiratory Questionnaire (CRQ) (13) translated into Spanish.    The MDCT scans were performed at suspended full inspiration and expiration (120 kVp, 90 mAs) on a multi–detector row CT scanner (Sensation 16, Siemens, Forchheim, Germany). Contiguous CT images were reconstructed with 1mm slice thickness and a low (b35f) spatial frequency reconstruction algorithm for emphysema measurements, and a high (b65f) spatial frequency reconstruction algorithm for airway measurements. In addition, two radiologists who were blinded to the exposure history of each subject independently scored each scan. Using a 6-point scale (0=none to 5=extensive), air trapping was scored by comparing inspiratory to expiratory scans, while emphysema was scored with  a similar 6 point scale using the inspiratory CT scans, as per a similar COPD study (14).  The radiologists also noted the presence or absence of bronchiectasis.      11  Quantitative assessment of emphysema was performed using custom software (EmphylxJ, Vancouver, Canada). For emphysema, we calculated the percent of the parenchyma less than -950 Hounsfield Units (% low attenuation area (%LAA) <950HU) on the inspiratory CT scans (15). We estimated the size of the emphysematous spaces using low attenuation cluster analysis. This analysis calculates the relationship between the number of low attenuation voxels and how those voxels are connected to each other (16). The slope of the relationship between number of low attenuation clusters and the size of the cluster (D) (log-log plot) has a smaller value if the low attenuation voxels are clustered into “larger emphysematous spaces”.    Quantitative assessment of airway wall dimensions was performed using the full-width at half maximum method (17). The internal lumen perimeter (Pi), the lumen area (Ai) the wall area (Aaw) and the percentage of the airway occupied by the wall (wall area percent (WA% = Aaw/Ai+Aaw x 100)) were calculated on airways cut in cross-section. To standardize airway wall measurements between subjects and reduce the bias of airway sampling, we created a regression equation to determine the square root of the wall area at a lumen perimeter of 10mm (SQRTWA-pi10) for each subject (14). Air trapping was quantified by measuring the percent of the parenchyma less than -856 Hounsfield Units (20) on the expiratory CT scans (%LAA <856HU – expiratory). We also calculated the ratio of the mean lung density on the expiratory scan to the mean lung density on the inspiratory scan (CT-Lung Density Ratio).  This measurement has been previously reported by Mets et al as “CT – Air Trapping” (19).   Statistical Analysis 12  The baseline characteristics of the subjects were described using means and standard deviations. Initial comparisons were made using a Student’s t-test for normally distributed data, or the Mann Whitney U test for non-normally distributed data. Using multiple linear regression models, we also calculated the relationship between exposure group and the emphysema variables (%LAA < 950HU – inspiratory scan; D; or radiologist score of emphysema); and between exposure group and airways disease variables (SQRTWA_pi10; wall area percent;  %LAA < 856 HU – expiratory scan; radiologist score of air trapping, CT-Air Trapping), adjusting for age and post-bronchodilator FEV1 % predicted. Height was also entered into all models due to the differences in height between groups.  SAS 9·3 was used for all analyses (SAS Institute, Seattle WA).    RESULTS  Patient Characteristics  Of the 173 women with COPD in the respiratory clinic cohort, 52 tobacco-smoked exposed and 53 cigarette smoke exposed women met the study criteria (Figure 1). Of these, 22 women in each group were matched by post-bronchodilator FEV1 % predicted and age. One woman in the Biomass Group was subsequently removed from the analysis as she did not have a post-bronchodilator FEV1/FVC < 0.70 at the time of data collection. There were no significant differences between the two groups with respect to age; post-bronchodilator FEV1 % predicted, FVC % predicted, or FEV1/FVC; lung volumes; weight; or BMI (Table 1) although women in the Tobacco Group were taller. The mean cumulative exposure for the Biomass Group was 275 hour-years, which is equivalent to a daily exposure history of 8 hours per day for 34 years.  13  Women in the Biomass Group reported that biomass smoke exposure had mainly occurred during cooking activities.  In addition, 76% of the women had used only wood, plant material, and/or manure for fuel; 14% had used only charcoal; and 10% had used both charcoal and plant material.  The majority of women in both groups were in level 2 of the Global Initiative for Chronic Obstructive Lung Disease (GOLD) (20) disease severity scale.    Quality of Life, Arterial Blood Gas, and Functional Exercise Capacity Although the mMRC Dyspnea score was similar in both groups, the scores from the quality of life questionnaires indicated that women in the Biomass Group reported significantly more symptoms, more activity limitation and less control over their disease (Table 2). Although a higher proportion of women in the Biomass Group reported cough and sputum symptoms, this was not significant.  Most of the women in both groups were hypoxemic (PaO2 less than 60mmHg) at rest (mean + standard deviation (SD) PaO2 = 49·3 (7·7) and 52·5 (4·0) mmHg, respectively). The severity  of hypoxemia in most of these women is not unexpected, as the values for PaO2 and PaCO2 for the healthy population living in the high altitude of Mexico City is low (67.7 and 31.1 mmHg, respectively.   Women in the Biomass Group had a significantly lower SaO2% at rest compared to the Tobacco Group (82% versus 87%, respectively; p=0·01). In addition to a low resting SpO2 both groups had a large drop in SpO2 during the walk test (Biomass = -9·3%; Tobacco = -7·3%; p= 0·20).   Emphysema and Small Airways Disease Phenotypes Women in the Tobacco Group had more emphysema compared to women in the Biomass 14  Group, based on both quantitative estimates and radiologists’ scoring (Tables 3 and 4). Figure 2 shows CT scan images from women with tobacco or biomass smoke exposure. Figure 2A from a woman in the Tobacco Group shows severe centrilobular emphysema. This finding was corroborated by the radiologists’ CT scores as women in the Tobacco Group had significantly higher scores for emphysema (Tobacco Group mean score=2·3; Biomass Group mean score=0·7; p=0·0001). The Tobacco Group had a greater percentage of emphysema compared to the Biomass Group (LAA% < 950HU-inspiratory = 27% versus 19%, respectively; p=0·02) and a smaller ‘D’, indicating larger emphysematous spaces (Tobacco = 2·07; Biomass = 2·57; p=0·002). Adjusting for age, post-bronchodilator FEV1 % predicted and height did not alter these results (Table 4).  Figure B is a representative expiratory CT scan image from a woman in the Biomass Group. The scan shows airway wall thickening and patchy areas of decreased attenuation and vascularity. The radiologists’ scoring confirmed that the biomass smoke exposed women had significantly higher levels of air trapping than the tobacco exposed women (Biomass Group air trapping score = 2·6; Tobacco Group air trapping Score 1·5), a difference which persisted after adjusting for age, post-bronchodilator FEV1 % predicted, and height (p=0·022)(Table 3 and 4). In addition, the radiologists indicated the presence of bronchiectasis in the scans of 14% of the Biomass exposed women compared to 0% of the Tobacco Exposed Women (p=0.009).  Women in the Biomass Group had a lower CT Lung Density Ratio on the univariate analysis; however, there was no difference between the groups in the two quantitative measures of air trapping (% LAA < -856 HU on the expiratory CT scans or the CT Lung Density Ratio) or measures of airway 15  thickness on the inspiratory scans (Table 3 and 4) after adjusting for covariates.    DISCUSSION In this study we found important structural differences between women with COPD with either biomass or tobacco smoke exposure. We found that women exposed to biomass smoke had less emphysema, based on both qualitative and quantitative CT scan measures, than women exposed to tobacco smoke.   In addition, less emphysema in the presence of similar airflow obstruction supports the argument that biomass smoke may lead to an airways disease phenotype.  Although the quantitative CT scans did not show differences in airways metrics after adjusting for covariates, the radiologists’ rating showed worse air trapping in women exposed to biomass smoke.  These results are consistent with the conclusions from Rivera’s (6) autopsy study and to our knowledge, represent the first direct comparison of COPD phenotypes in comparably obstructed living individuals exposed to biomass versus tobacco smoke, using questionnaire, exercise tolerance, lung function and MDCT data.  The body of evidence linking biomass smoke exposure to COPD is growing. Hu et al (21) conducted a meta-analysis based on the literature published up to 2009 and reported that individuals exposed to biomass smoke were over two times more likely to develop COPD than those who were not exposed (odds ratio 2·44, 95% confidence interval 1·9, 3·33). Po et al (22) investigated the risk of COPD and other lung diseases due to biomass smoke in women and children and reported a similar odds ratio (OR 2·40, CI 1·47, 3·93). These findings are supported by a recent cross-sectional study by Kurmi et al (23) who reported reduced lung function, even 16  in young adults, in those exposed to biomass smoke compared to non-exposed individuals, and by an earlier case-control study by Perez-Padilla et al (4) who compared biomass exposure levels in women with chronic bronchitis, chronic airflow obstruction, or both to women who had other lung disease, and healthy individuals. They found that women with chronic bronchitis and airflow obstruction were 14 times more likely to have been exposed to wood-smoke, at similar levels as our subjects, compared to healthy individuals.    Less is known about the pathophysiology of COPD due to biomass smoke. Much of our knowledge of COPD today results from extensive research on the effects of tobacco smoke on the airways and lung parenchyma. Tobacco smoke exposure leads to decreases in expiratory flow and COPD by two distinct pathophysiological processes -- emphysematous destruction of the lung parenchyma, and/or narrowing and obliteration of the small peripheral airways (24). Previous work on the pathophysiological changes related to biomass smoke exposure has not specifically addressed COPD phenotypes of emphysema or airway disease. For example, Arslan et al and Kara et al (25, 26) assessed the effects of biomass smoke and reported CT evidence of fibrotic bands and peribronchovascular thickening, yet did not focus on COPD.   Our study shows that women with tobacco exposure had more emphysema while women with biomass smoke exposure had more air trapping as detected by radiologists, and worse Symptoms, Activities and Mastery scores on the SGRQ and the Chronic Respiratory Questionnaire.  Although the radiologists’ scoring and health status measures indicated more airway involvement in the Biomass Group, the quantitative CT measures of the airway 17  dimensions and air trapping were not different between the two groups after adjustment for covariates. The women in our sample were relatively young (mean age 69 years), GOLD Two, and were not severely limited on their six-minute walk test. It is possible that although radiologists detected air trapping and women reported increased symptoms in the Biomass Group, there were no measurable changes in their airway wall thickness and quantitative measures of air trapping at this stage of their disease process.  In this study we used multiple different measures of “airway disease”. Some metrics such as the CT estimates of airway dimensions and the radiologist’s estimate of bronchiectasis reflect structural remodeling of intermediate and larger airways, whereas the CT measures of gas trapping (%LAA and CT Lung Density Ratio) reflect the structure and function of the small airways whose measurements are beyond the range of resolution of CT scanning.  Even gas exchange is a measure of the heterogeneity of small airway narrowing in that it is a reflection of ventilation-perfusion mismatching. Although we have previously shown that structural changes in the larger airways, visible on CT scans, reflect the airway dimensions of smaller membranous bronchioles (27) the different metrics may not related to each other and may have different structural basis and functional effects.    Alternatively there might be too much variation in these measurements to detect real differences with our sample size.  In addition, several of the airway measures were no longer significant between the two groups after adjusting for height.  One possible explanation is that some of the airway parameters are influenced by lung size and shorter women would have smaller lungs and therefore smaller airway wall dimensions.  Another explanation is that 18  differences were due to differences in lung development relative to poor nutrition or low socioeconomic status.  Since the women recruited for this study were all patients from INER, which is a hospital that serves the very low income population of Mexico City it is likely that the current SES status of the women was similar.  However, it is true that other developmental factors could have been different between the two groups and could have influenced the patients’ height. Alternatively, the model may be over-corrected in the sense that height is a surrogate for exposure and correcting for height may artificially remove any real differences.     The women in both exposure groups had a high prevalence of hypoxemia.  This is common in patients with COPD who live in Mexico City due to the high altitude (2420 meters).  Healthy individuals typically have oxyhemoglobin saturation and PaO2 values that are lower than individuals at sea level (typically 95% and 68mmHg, respectively) and often individuals with COPD have even further reductions in their PaO2.    Several mechanisms may be responsible for the phenotypic differences between exposure groups observed in this study. First, although biomass smoke has many of the same constituents as tobacco smoke (28) the exact composition differs depending on the source of the fuel, the efficiency of combustion, and the relative humidity. Although the particle size can be similar in both tobacco and biomass smoke (29, 30), differences in chemical composition could lead to different pathophysiological processes. Second, the age of onset of biomass exposure is different than tobacco smoke. Biomass exposure in rural villages can begin in utero, and individuals are often exposed throughout their lives with women and girls receiving the 19  largest cumulative exposures. Biomass exposure is also associated with multiple acute respiratory infections in children (29). The early exposure and repeated respiratory infections may alter the structure and function of the airway walls beginning at an early age, and may predispose biomass smoke-exposed individuals to a different COPD phenotype as adults compared to tobacco smokers who may begin smoking at an older age. Third, there are possible differences in the inhalation pattern of those exposed to biomass versus tobacco smoke. Individuals inhaling biomass smoke would use a consistent tidal breathing pattern. Conversely, cigarette smokers usually smoke in a two-phase pattern – first the smoke is drawn into the mouth without direct inhalation into the lungs, then there is pause, and finally the smoke is inhaled into the lungs with an additional volume of air (30). Average inhalation volumes have been measured at approximately twenty-five percent of vital capacity, which is twice that of the average tidal volume (30). The larger inhalation volume in cigarette smokers compared to those exposed to biomass smoke may draw smoke more deeply into the lungs and may increase the deposition of the tobacco smoke in the lung parenchyma, leading to an emphysema-predominant COPD phenotype.   Limitations There is the possibility that ethnicity contributed to our observations of increased airway disease in the biomass smoke group and increased emphysema in the tobacco smoke group. Although all our participants were currently residing in Mexico City, many women with biomass smoke exposure had lived in rural communities at the time of exposure. In México, women in rural communities are often of indigenous descent whereas women born in urban communities 20  often have Spanish ancestry. Therefore the predisposition to a COPD phenotype could be contributed to by different genetic susceptibilities. It is difficult to separate the contributions of ethnicity to our results.  It is unlikely we could identify an indigenous rural population that was not exposed to biomass smoke to act as a comparison group.  In addition, rural versus urban women would have differing exposures to other factors, including outdoor air pollution, nutrition, and health care, which could affect the development of COPD. The women who were recruited to this study attended the national hospital for low-income residents and therefore the current socioeconomic status between the groups was likely similar, nevertheless we cannot assess the impact of other environmental and behavioral factors on the development of distinct COPD phenotypes.  Finally, we assessed the CT characteristics and did not have pathology specimens to corroborate our findings. Finally, biomass exposure from other sources may lead to a different pulmonary presentation.  Conclusion To our knowledge, this is the first study that has shown differences in COPD phenotypes in living women with biomass versus tobacco smoke exposure using both computer-aided and expert evaluation of MDCT.  Results from this study should motivate further investigation into the complex interaction between different smoke exposures and the pathophysiology of COPD.    Acknowledgments The authors wish to acknowledge Anh-Toan Tran, Lauren Wierenga, and Natasha Krowchuk for technical assistance with the CT analysis program and data management.   21  References 1. Albalak R, Frisancho AR, Keeler GJ. Domestic biomass fuel combustion and chronic bronchitis in two rural Bolivian villages. Thorax 1999; 54: 1004-8. 2. Dennis RJ, Maldonado D, Norman S, Baena E,  Castano H, Martinez G, Velez, JR.  Wood smoke exposure and risk for obstructive airways disease among women. Chest 1996; 109(3 Suppl): 55S-6S. 3. Orozco-Levi M, Garcia-Aymerich J, Villar J, Ramirez-Sarmiento A, Anto JM, Gea J.  Wood smoke exposure and risk of chronic obstructive pulmonary disease.  Eur Respir J 2006; 27: 542-546.   4. Perez-Padilla R, Regalado J, Vedal S, Pare PD, Chapela R, Sansores R, Selman M. Exposure to biomass smoke and chronic airway disease in Mexican women: A case-control study. Am J Respir Crit Care Med 1996; 154: 701-6. 5. 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Small-airway obstruction and emphysema in chronic obstructive pulmonary disease. N Engl J Med 2011; 365(17): 1567-75. 25. Arslan M, Akkurt I, Egilmez H, Atalar M, Salk I. Biomass exposure and the high resolution computed tomographic and spirometric findings. Eur J Radiol 2004; 52(2): 192-9. 26. Kara M, Bulut S, Tas F, Akkurt I, Seyfikli Z. Evaluation of pulmonary changes due to biomass fuels using high-resolution computed tomography. Eur Radiol 2003; 13(10): 2372-7. 27. Nakano Y, Wong JC, de Jong PA, Buzatu L, Nagao T, Coxson HO, Elliott WM, Hogg JC, and Pare PD. The prediction of small airway dimensions using computed tomography. Am J Respir Crit Care Med 171: 142-146, 2005.    24   28. Bruce N, Perez-Padilla R, Albalak R. The health effects of indoor air pollution exposure in developing countries. Geneva: World Health Organization, 2002. 29. Naeher LP, Brauer M, Lipsett M, et al. Woodsmoke health effects: a review. Inhal Toxicol 2007; 19(1): 67-106. 30. Bernstein D. A review of the influence of particle size, puff volume, and inhalation pattern on the deposition of cigarette smoke particles in the respiratory tract. Inhal Toxicol 2004; 16(10): 675-89.   25    Table 1.   Characteristics of Sample    Biomass Group Mean (SD) Tobacco Group Mean (SD)  p Patient Characteristics         n 21 22       Age, years 69·0 (6·3) 69·3 (5·5) 0·90      Tobacco smoke exposure –            pack years  0  32·6 (14·4)  n/a      Biomass smoke exposure –            hour-years  275·4 (101·0)  0  n/a      Height (cm) *median (IQR) 148·0  (6·0) 154·0  (3.0) 0.003*      Weight (kg) 61·5 (12.1) 61.9 (10.8) 0.92      BMI 28.5 (6.2) 26.8 (4.2) 0.29  Pre-bronchodilator Lung Function       FEV1  % predicted 46.7 (14.9) 50.3 (12.2) 0.38      FVC   % predicted 70.1 (16.3) 79.3 (12.8) 0.05      FEV1 /FVC 51.9 49.3 (10.3) 0.46 Postbronchodilator lung function       FEV1  % predicted 54.5 (14.9) 57.7 (12.3) 0.44      FVC   % predicted 80.6 (18.6) 89.0 (11.8) 0.08      FEV1 /FVC 53.2 (11.7) 50.4 (9.7) 0.39 Lung Volumes        RV % predicted 181 (67) 176 (56) 0.80     TLC % predicted 130 (27) 125 (21) 0.48     RV/TLC % 0.66 (0.08) 0.62 (0.09) 0.12 * non-parametric test; IQR = interquartile range; BMI=body mass index; FEV1 = forced expiratory volume, first second; FVC = forced vital  capacity   26   Table 2.  Symptoms, Quality of Life, Six Minute Walk Distance, and Arterial Blood Gases    Biomass Group Mean (SD) n = 21 Tobacco Group Mean (SD) n = 22  p MRC Dyspnea 1.4 (1.0) 1.2 (0.9) 0.73 St. George’s Respiratory Questionnaire (higher values = worse health status)         Symptoms   46 (28) 30 (18) 0.03      Activities 58 (20) 46 (19)   0.02*      Impact  34 (19) 24 (13)   0.10*      Total  42 (18) 33 (14) 0.07 Report of Cough or Sputum (from St. George’s Respiratory Questionnaire)         Report of cough (n, %) 18 (90%) n=20 16 (76%) n=21 0.23      Report of sputum (n, %) 18 (90%) n=20 15 (71%) n=21 0.13 Chronic Respiratory Questionnaire (lower values = worse health status)         Dyspnea 17 (10) 14 (8)   0.19*      Fatigue 19 (5) 19 (5) 0.96      Emotion 35 (8) 39 (8) 0.58      Control of disease (mastery) 21 (5) 24 (4)   0.04*      Total 92 (17) 93 (17) 0.74 Arterial Blood Gas         PaO2 49.3 (7.7) 52.5 (4) 0.11      PaCO2 36.9 (5.4) 33.1 (4) 0.01      pH 7.41 (0.03) 7.42 (0.04) 0.33      SaO2 % 82(8) 87 (4) 0.01 6MWD       SpO2 % at rest 89 (5) 92 (3) 0.07      SpO2 % at end of walk test 80 (9) 84 (4) 0.04      Distance (m) 306 (118) 307 (152) 0.98  * non-parametric test MRC = Medical Research Council; 6MWD = six minute walk distance; PaO2 = partial pressure of oxygen; PaCO2 = partial pressure of carbon dioxide; SaO2 % =  oxyhemoglobin saturation percent from arterial blood sample; SpO2 % = oxyhemoglobin saturation percent measured with a pulse oximeter;    27   Table 3 Emphysema and Small Airways Disease Measurements  Biomass Group Mean (SD) Tobacco Group Mean (SD)  p Emphysema Measurements         Radiologist Score of Emphysema 0.67 (0.80) 2.33 (1.53) 0.0001      % LAA < 950HU – inspiratory scan 19.28 (10.65) 27.07 (10.24) 0.02      D – size of emphysematous spaces 2.57 (0.56) 2.07 (0.33) 0.002 Airways Measurements         Radiologist Score of Air Trapping 2.60 (0.82) 1.52 (1.12) 0.006      % LAA < 856 HU – expiratory scan 54.00 (13.69) 56.60 (15.26) 0.57      Square Root of Wall Area at Lumen       Perimeter = 10mm 4.48 (0.34) 4.33 (4.17) 0.17      Airway Wall Area Percent 79.07 (3.61) 77.91 (3.76) 0.31      CT Lung Density Ratio 1.18 (0.17) 1.34 (0.19) 0.004 SD = standard deviation; HU = Hounsfield units; CT = computed tomography    28   Table 4.  Multivariable Models Predicting Emphysema and Airway Variables   Parameter Estimate 95% Confidence Interval p EMPHYSEMA    Radiologist Score of Emphysema Severity     Biomass Group     Age     FEV1 % predicted     Height  -1.555 0.035 -0.024 0.026  -2.466, -0.643 -0.032, 0.101 -0.054, 0.007 -0.053, 0.105  0.001 0.299 0.121 0.513 % LAA < 950HU – inspiratory scan      Biomass Group     Age     FEV1 % predicted     Height  -7.397 -0.177 -0.242 0.201  -14.657, -0.136 -0.731, 0.377 -0.486, 0.002 -0.407, 0.809  0.046 0.521 0.052 0.507 D – size of emphysematous spaces      Biomass Group     Age     FEV1 % predicted     Height  0.457 -0.008 0.012 -0.012  0.137, 0.777 -0.032, 0.017 0.001, 0.022 -0.039, 0.015  0.006 0.530 0.036 0.373 AIRWAY DISEASE    Radiologist Score of Air Trapping Severity     Biomass Group     Age     FEV1 % predicted     Height   0.868 -0.034 -0.012 -0.025   0.135, 1.601 -0.087, 0.019 -0.037, 0.013 -0.089, 0.038   0.022 0.200 0.322 0.421 % LAA < 856 HU – expiratory scan     Biomass Group     Age     FEV1 % predicted     Height  -0.952 0.053 -0.006 0.256  -12.006, 10.102 -0.774, 0.880 -0.367, 0.354 -0.661, 1.173  0.863 0.898  0.972 0.575 Square Root of Wall Area at Lumen       Perimeter = 10mm     Biomass Group     Age     FEV1 % predicted     Height   0.099 -0.006 -0.003 -0.007   -0.155, 0.354 -0.026, 0.013 -0.011, 0.006 -0.028, 0.015   0.435 0.528 0.490 0.522 Airway Wall Area Percent     Biomass Group     Age     FEV1 % predicted     Height  0.504 -0.061 0.025 -0.119  -2.171, 3.180 -0.266, 0.143 -0.065, 0.115 -0.343, 0.105  0.705 0.546 0.581 0.289   29   CT Lung Density Ratio     Biomass Group     Age     FEV1 % predicted     Height  -0.100 -0.002 0.002 0.010  -0.220, 0.027 -0.011, 0.008 -0.002, 0.006 0.000, 0.021  0.121 0.718 0.412 0.047 FEV1 = forced expiratory volume, first second; CT=computed tomography    30   Figure Legends  Figure 1.    Flowchart of Subject Recruitment  Figure 2. Inspiratory CT scans from a woman with COPD from the Tobacco group (A) and a woman of similar age and FEV1% predicted in the Biomass group (B). The image from the woman with tobacco exposure shows severe centrilobular emphysema throughout the upper lobe. The woman with biomass exposure has no obvious emphysematous spaces but her CT image shows airway wall thickening and patchy areas of decreased attenuation and vascularity suggestive of small airway disease. Expiratory scans from this woman showed air trapping 

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