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To delineate the relationship of obesity to airflow obstruction (AO) and respiratory symptoms in adults without a previous diagnosis of chronic obstructive pulmonary disease (COPD).
We analyzed data for potential referents recruited to be healthy controls for an ongoing study of COPD. The potential referents had no prior diagnosis of COPD or healthcare utilization attributed to COPD in the 12 months prior to recruitment. Subjects completed a structured interview and a clinical assessment including body mass index, spirometry, Six Minute Walk Test (SMWT) and the Short Performance Physical Battery (SPPB). We used multiple regression analyses to test the associations of obesity (BMI≥30kg/m2) and smoking with AO (FEV1/FVC ratio<0.7). We also tested the association of obesity with respiratory symptoms and impaired functional capacity (SPPB, SMWT), adjusting for AO.
Of 371 subjects (aged 40–65), 69 (19%) manifested AO. In multivariate analysis, smoking was positively associated with AO (per 10 pack-years, OR 1.24; 95% CI: 1.04 – 1.49), while obesity was negatively associated with AO (OR 0.54; 95% CI: 0.30 – 0.98). Obesity was associated with increased odds of reporting dyspnea on exertion (OR 3.6; 95% CI: 2.0 – 6.4), productive cough (OR 2.5; 95% CI: 1.1 – 6.0), and with decrements in SMWT distance (−67 ± 9meters; 95% CI: −84 to −50m) and SPPB score (OR 1.9; 95% CI: 1.1 – 3.5). None of these outcomes were associated with AO.
Although AO and obesity are both common among adults without an established COPD diagnosis, obesity, but not AO, is linked to a higher risk of reporting dyspnea on exertion, productive cough, and poorer functional capacity.
Based on epidemiologic studies, the prevalence of spirometry-defined chronic obstructive pulmonary disease (COPD) ranges from 10–15%; among cigarette smokers, COPD prevalence increases to 25%.1–3 Furthermore, it has been estimated that approximately two-thirds of individuals with COPD remain undiagnosed.1 However, the clinical relevance of such airflow limitation remains a matter of debate. Many individuals with airflow obstruction identified through population-based testing are asymptomatic, while others have respiratory complaints consistent with COPD.1,2,4 Other factors beyond smoking are likely to contribute to both spirometry-defined obstruction and the presence of respiratory symptoms. Among these, obesity may be particularly relevant.
Obesity has been long recognized as a major contributor to respiratory symptoms and exercise limitations, independent of airflow obstruction.5 The potential interrelationships among obesity, airflow obstruction, and respiratory symptoms or functional limitations are complex. Increased body mass index (BMI) is clearly linked to a greater asthma risk;6–9 in contrast, lower BMI appears to be linked to smoking-related emphysema.6,10 While increased BMI has been shown to have a negative association with COPD in most studies,5,6,10–12 at least one investigation has reported an association in the opposite direction.13 Obesity is known to cause a restrictive ventilatory deficit, but the combination of obesity and COPD has been linked to a mixed obstructive and restrictive ventilatory defect.14 The net result thus makes it difficult to parse out the contributions from obesity and airflow obstruction in symptomatic individuals.15 Thus, it is not surprising the primary care provider faces the dilemma of how to evaluate and treat abnormal respiratory symptoms in obese patients, especially in the presence of concomitant airflow obstruction.16
Prior studies of obesity and airflow obstruction have included spirometry, but have not brought objective exercise capacity assessments to bear in addressing this difficult question.2,4–6,8,9,14 As part of a larger study of COPD, we utilized data from a pool of referents recruited expressly because they lacked a known clinical diagnosis of COPD. Some of the otherwise eligible referents, however, were found to have airflow obstruction on spirometry. Because all potential referent subjects, both with and without airflow obstruction, completed structured symptom questionnaires and physical assessments, we were able to use these data to examine the interrelationships among lung function, obesity, respiratory symptoms, and exercise limitations in a population without a COPD diagnosis.
The Function, Living, Outcomes and Work (FLOW) study of COPD is an ongoing, prospective cohort study of adults recruited from the Kaiser Permanente Medical Care Program (KMPCP), an integrated health care organization in northern California.17–20 The KPMCP population has previously been shown to be well representative of the regional population.21 At the time of the original cohort formation, a referent group of subjects without COPD was concurrently recruited. Baseline recruitment and assessment have been described in detail previously.17 In brief, subjects aged 40–65 were screened from KPMCP for the presence of both healthcare utilization and medication dispensation consistent with COPD. The referent group was comprised of subjects matched for age, sex, and race-ethnicity whose medical records lacked the presence of any ICD-9 diagnosis codes for COPD (chronic bronchitis , emphysema , or COPD ) for any health care utilization within KPMCP in the 12 months prior to recruitment. Utilization based on ICD-9 diagnosis codes for other respiratory complaints, including asthma, was not an exclusion criterion. All FLOW baseline participants, both those with COPD and referents, completed structured telephone interviews and underwent a research clinic visit that included spirometric measurement and other physical assessments.
Referent recruitment in the FLOW study is shown in Figure 1. We originally identified 2021 potential referents of whom 1558 were eligible for inclusion in the study; 649 (42% of the eligible subject pool) went on to complete structured interviews. Of these 649 subjects, 373 (57%) completed the research clinic visit. The differences between those who did and did not complete research clinic visits are shown in Table 1. There were no statistical differences in age, sex, or race-ethnicity by research clinic visit follow-up status. Those with examination data were more likely to be never smokers, have higher educational attainment, and more likely to be in the extremes of household income.
Subjects completed structured telephone interviews prior to their research clinic visit. Interviews included sociodemographics, medical history, health status and symptoms, direct tobacco exposure, and secondhand smoke exposure. Specifically in terms of health status, we queried subjects regarding detailed respiratory symptoms including dyspnea with exertion and productive cough. The structured interview also included the Medical Outcomes Survey Short Form (SF-12) instrument.22 We assessed global health status using the single SF-12 item, “In general, would you say your health is excellent, very good, good, fair or poor?” For our analyses, we dichotomized responses to this global self-rated health item as Excellent/Very Good compared to Good/Fair/Poor status. There have been multiple approaches analyzing 5-level scale global self-rated health items, including comparing the two uppermost levels to the lower three response options, as we did here;23 other approaches include dichotomizing at a lower cut-point of fair to poor health,24,25 or studying multiple levels as separate indicator variables relative to either single extreme.26 We chose our cut-point (Excellent/Very Good compared to Good/Fair/Poor) to allow sufficient observations in each analytic cell for stability in model estimation.
To assess respiratory impairment, we performed spirometry according to American Thoracic Society (ATS) Guidelines.27,28 We used the EasyOne™ Frontline spirometer (ndd Medical Technologies, Chelmsford, MA), which meets ATS criteria. To calculate percent predicted values, including the lower limit of normal (LLN) for the forced expiratory volume in one second (FEV1) to forced vital capacity (FVC) ratio, we used predictive equations derived from NHANES III.29 We did not administer bronchodilators. Along with spirometry, we measured height and weight used to calculate body mass index (BMI). We defined obesity as a BMI ≥30 kg/m2.
All 373 subjects who completed the research visit performed spirometry. Of these, 71 (19%; 95% CI: 15 – 23%) were found to have airflow obstruction, defined by a ratio of FEV1 to FVC of less than 0.7. These subjects had been excluded from all previous referent group analyses in the principal FLOW analyses (see Figure 1). We defined these 71 subjects as having airflow obstruction without known COPD. We excluded two subjects among the 71 because in their structured interviews they reported a physician’s diagnosis of COPD, despite this not being reflected in the KPMCP electronic databases.
Of the 69 subjects with airflow obstruction, 44 subjects (64%) had an FEV1/FVC ratio below the LLN; two of the 302 in the non-airflow obstruction group as defined by the fixed ratio were nonetheless also below the LLN, yielding a total of 46 out of 371 subjects (12.4%) with LLN-defined obstruction.
Because the referent recruitment strategy did allow for asthma, we reviewed the KPMCP electronic databases within 12 months of the initial recruitment date to identify those with health care utilization for asthma and bronchodilator or inhaled corticosteroid medication dispensing. There were 18 such subjects among the 69 remaining referents with airflow obstruction. This information was used to perform sensitivity analyses (see Statistical Analysis, below). Excluding these, the remaining 51 persons among a reduced total of 353 yields a prevalence estimate for undiagnosed airflow obstruction of 14% (95% CI: 11 – 19%).
We measured submaximal exercise performance using the Six Minute Walk Test (SMWT) consistent with the ATS protocol.30–32 We measured lower extremity function using the Short Physical Performance Battery (SPPB).33–35 This battery includes three performance measures, each scored from one to four points (maximum = 12 points). The standing balance test asks subjects to maintain their feet in a side-by-side, semi-tandem stand, or tandem stand for 10 seconds. A test of walking speed requires subjects to walk four meters at normal pace; participants are assigned a score from one to four based on the quartile of length of time needed to complete the test. The chair stand test, a measure of lower extremity extensor muscle strength, measures the time required for the subject to stand up and sit down from a chair five times with arms folded across the chest; scores from one to four are assigned based on the quartile of time taken to complete the task. The summary performance combining all three measures has excellent inter-observer reliability as well as predictive validity.33–35 In this analysis, subjects were dichotomized into two groups: those subjects in the lowest quintile (SPPB score <11) whom we defined as poor lower extremity functioning, compared to all others (SPPB score ≥11).
Statistical analysis was performed using STATA software, version 11 (StataCorp LP, College Station, TX). Differences in descriptive characteristics by research clinic status and by presence or absence of airflow obstruction were tested by chi square or Wilcoxon rank sum test. We used multivariable logistic regression analysis to study smoking and obesity in relation to the presence of AO, controlling for age, sex, and race-ethnicity. Logistic regression was also used to analyze the relationship between AO and obesity as independent predictors of respiratory symptoms, self-reported health status, and performance on the SPPB, adjusted for age, sex, and race-ethnicity. We also tested the association of an airflow obstruction-obesity interaction term included in the same models. We used multivariable linear regression to test airflow obstruction and obesity as independent predictors of SMWT distance, controlling for the same covariates (SMWT distance measurements were missing for two subjects, both without obstruction). We compared median percent predicted forced expiratory flow over 25% to 75% of expired volume (FEF25–75) between the obese and non-obese groups using the Wilcoxon rank sum test. For key outcomes, additional sensitivity analyses were performed redefining obstruction based on LLN and, separately, excluding those subjects with asthma in the FEV1 /FVC ratio<0.7 defined obstruction group. Hosmer-Lemeshow tests were performed to confirm the goodness of fit in our logistic regression models (p>0.15 for all models tested).
Compared to others in the referent cohort, the 69 subjects with airflow obstruction were more likely to be non-Latino white, older, and less likely to be obese (Table 2). There were 147 total subjects with obesity (40%; 95% CI: 35 – 45%); among non-obese subjects, only one had a BMI ≤18.5 kg/m2. Subjects with obstruction also had a higher prevalence of ever smoking, although this difference was not statistically significant (57% vs. 48%; p=0.08). Among ever smokers, those with obstruction reported significantly higher lifetime cumulative smoking (27 vs. 19 pack-years; p=0.02). There were no significant differences between those with and without obstruction in sex, educational attainment, secondhand smoke exposure or annual household income.
By definition, those with airflow obstruction manifested abnormal lung function, with a median FEV1/FVC ratio of 0.67 (see Appendix Table 1). Their median FEV1 was 83% of predicted, with a lower 10th percentile range value of 62% predicted. The median FEF25–75 was 56% predicted, with a lower 10th percentile range value of 33% predicted. The median percent predicted for FEF25–75 (25th–75th percentile) among the non-obese subjects was 90% (68 – 113%); among the obese the median FEF25–75 was 93% (66 – 125%); there was no statistically significant difference by obesity status for FEF25–75 percent predicted (p=0.36). Comparing the obese to the non-obese, the mean FVC percent predicted was lower in subjects with obesity (91.3 ± 1.4% vs. 96.2 ± 1.0%; p<0.01).
After adjusting for age, sex, and race-ethnicity, each 10 pack-year increase in cumulative smoking was associated with a 25% increased odds of airflow obstruction (OR 1.24; 95% CI 1.04 – 1.49) [Table 3]. Notably, in the same multivariate model, those who were obese were half as likely to manifest obstruction (OR 0.54; 95% CI 0.30 – 0.98). Using the LLN-based definition, obesity remained negatively associated with obstruction, but less strongly and not significantly so (OR 0.68; 95% CI: 0.35 – 1.3; p=0.26); the association of smoking with obstruction was similar to the previous analysis (per 10 pack-years, OR 1.37; p<0.01). Re-estimating these associations excluding the 18 subjects with asthma from the airflow obstruction group yielded similar, albeit strengthened, results (per 10 pack-years OR 1.34; 95% CI: 1.1–1.6; obesity OR 0.29; 95% CI: 0.13–0.63).
In multiple logistic regression analyses, controlling for age, sex, race-ethnicity, and obesity, airflow obstruction was not associated with productive cough, dyspnea on exertion, self-reported good/fair/poor health, or lower SPPB score (Table 4; top portion). In the same multivariate model, however, obesity was associated with more than a twofold increased risk of productive cough (OR 2.5; 95% CI: 1.1 – 6.0), and more than a three-fold increased risk of dyspnea on exertion (OR 3.6; 95% CI: 2.0 – 6.4). In addition, obesity was associated with a markedly higher likelihood of self-reported good/fair/poor health (OR 4.5; 95% CI: 2.8 – 7.3) and a higher likelihood of poor lower extremity functioning (lowest quintile SPPB score OR 1.9; 95% CI: 1.03 – 3.3). No significant obstruction-obesity interaction (p>0.4 in all cases) was observed for any of the outcomes for which both factors manifested increased point estimates of risk (cough, dyspnea, and good/fair/poor health). In an alternative analysis adjusting for obesity, there was no statistically significant association between LLN-defined obstruction and dyspnea on exertion (OR 1.4; 95% CI 0.6 to 3.1; p=0.4).
Excluding those in the obstruction group who had asthma (n=18; bottom portion of Table 4), we found similar effects. There was no discernible association between airflow obstruction and productive cough, dyspnea on exertion, self-reported good/fair/poor health, or lowest quintile SPPB score. Obesity remained statistically associated with dyspnea on exertion (OR 3.3; 95% CI 1.8–6.1), self-reported good/fair/poor health (OR 4.3; 95% CI 2.6–7.1), and poor lower extremity functioning (OR 1.9; 95% CI 1.1–3.5), while the relationship with productive cough was no longer statistically significant (OR 2.3, 95% CI 0.9–5.8; p=0.09).
The mean SMWT distance for the entire group (n=369) was 522 ± 90 meters (m). In multiple linear regression analysis, adjusting for age and sex, the presence of obesity was associated with a 67 ± 9m decrement in SMWT distance (95% CI −84 to −50m; p<0.001). In contrast, airflow obstruction was associated with a non-statistically significant increase of 5 ± 12m (95% CI −19 to +29; p>0.7).
In this study, among adults enrolled in an integrated health care organization, airflow obstruction among those without a known COPD diagnosis was not associated with a discernible increased likelihood of subjective respiratory symptoms, poorer self-reported health status, or decrements in functioning including the Short Physical Performance Battery (SPPB) and distance walked in 6 minutes (SMWT).
Notably, obesity was associated with a lower likelihood of airflow obstruction as defined by an FEV1/FVC ratio <0.7, an effect that has been seen in prior cross-sectional studies of previously undiagnosed airflow obstruction.5,10–12 This stands in contrast to those with established COPD, where a positive association between COPD and increased BMI has been reported,13 an effect that was previously reported by our group among those with diagnosed COPD in the study arm of the FLOW cohort not included in this analysis.36 The mechanism(s) of any protective effect of obesity on the presence of non-clinically diagnosed airflow obstruction remain to be elucidated. Clearly, there appears to be a difference between the relationship of obesity in non-clinically diagnosed airflow obstruction and clinically diagnosed COPD. Once COPD has been clinically diagnosed, there is a subset of patients who evolve to manifest lower BMI and have worse outcomes overall.37–40 Therefore, persons with concomitant clinically diagnosed COPD and obesity may reflect a survival bias leading to a higher proportion of patients with established COPD who are obese. Nonetheless, extrapolating from this sample to persons with non-clinically diagnosed airflow obstruction without clinical disease would be overly speculative at this time.
Obesity, but not airflow obstruction, was associated with productive cough, dyspnea on exertion, and self-assessed poorer health status. In addition, SPPB was impaired and SMWT distance was reduced in the presence of obesity. These associations are consistent with the existing literature documenting a convincing link between obesity and poorer functional status.41–43 Furthermore, the lack of association of decreased lung function to either respiratory symptoms or subjective quality of life decrements seen in our study is also well established in the existing literature. Many previous studies have shown the relatively weak association between decrements in lung function per se and decreased health-related qualify of life or decreased functional status.44–47 Our study re-confirms this observation, suggesting that obesity is more strongly associated with the increased respiratory symptoms and decreased functional capacity that were manifested in our cohort.
In our cohort, obesity was less strongly and not significantly associated with obstruction as defined by the lower limit of normal (LLN) of the FEV1/FVC ratio and we observed no significant obesity-associated difference in the median FEF25–75. Redefining airflow obstruction based on the LLN reduced the prevalence of abnormality in this cohort (12.4% vs. 19%). Although our power to detect significant associations was compromised by the restricted numbers with obstruction thus defined, the point estimate of the obesity risk for obstruction was similar and in the direction of less likely odds (0.7 compared to 0.5), as was the weak association of obstruction with dyspnea on exertion (1.4 compared to 1.2).
Taken together, our results suggest that among those without previously diagnosed COPD, airflow obstruction, per se, is not contributing to respiratory symptoms, functional capacity, or self-rated global health status in a statistically significant or clinically meaningful way. Rather, we found that obesity in this group was playing a larger and more meaningful explanatory role. Thus, it may be possible that interventions intended to improve dyspnea may achieve greater impact by reducing obesity rather than by overly focusing on labeling airflow obstruction detected through screening as COPD and treating this with medications.
A particular strength of this analysis is in the systematic characterization of symptoms and the objective measures of physiologic functioning (pulmonary function, SMWT, and SPPB) in a sample recruited from a large integrated health care organization generalizable to other primary care settings. In addition, for our analysis we dichotomized BMI (obese vs. non-obese) rather than treating this as a linear variable. Even though this may result in some loss of statistical power, obesity so defined is the accepted and more clinically relevant measure. Moreover, including non-smokers as well as smokers in our study group is more relevant to the mixed general primary care population. By the same token, symptoms were not used as a screening criterion for entry into the study; limiting spirometric screening to only symptomatic populations is likely to introduce selection bias, both in regard to airflow obstruction and obesity.
Limitations of this analysis include its relatively narrow adult age range, modest sample size, geographic concentration in northern California and the absence of post-bronchodilator measurements of lung function. Subjects were intentionally chosen within a specific age range (40–65) because the focus of the parent FLOW study was disability among persons of working age; this may limit generalizability to older populations where airflow obstruction may be more prevalent and may have differing relationships to obesity. Some of the associations we observed did not achieve statistical significance, but might have done so with larger study numbers. Nonetheless, other weak associations, such as the change in SMWT linked to obstruction, are in the opposite direction to an adverse effect and do not suggest a larger study size alone would have led to an adverse effect being observed. Selection of referents may have contributed a source of bias in our study; overall we were able to recruit 649 subjects from 1558 eligible (42%), with 373 subjects participating (57% of the total recruited). Comparing individuals who participated in our study to those who did not, however, revealed a higher incidence of current or former smoking in the non-participation group. Had these non-participants been included in our study, this likely would have increased the incidence of AO, given the observed relationship between smoking and AO.
We studied subjects recruited from northern California; this geographic limitation should be kept in view when generalizing to other regions. We did not administer bronchodilators prior to lung function assessment, which may have falsely labeled some subjects with airflow obstruction who may have had reversible obstruction; this is particularly relevant because a subset of the subjects with obstruction were likely to have asthma. We addressed this issue by carrying out sensitivity analyses for all key models excluding such subjects. These demonstrated similar associations. We recognize the possibility that persons with obstruction who did not carry a prior diagnosis of asthma (and thus were not excluded from sensitivity analyses based on that diagnosis) nonetheless had that condition, which could influence the results seen in our study. Our study primarily focused on obstruction defined by an FEV1/FVC ratio <0.7. The LLN approach, which we tested secondarily, may be more precise, but the fixed ratio approach is far more widely used and better understood in general primary care settings.48,49 Finally, in the setting of concomitant obesity and airflow obstruction, obesity may lower the FVC, which can appear to “normalize” the FEV1/FVC ratio in the presence of a decreased FEV1. This effect, to the extent operative, would tend to misclassify those with obstruction and concomitant obesity into the non- airflow obstruction group, making obesity appear to be less frequent in airflow obstruction. Of note, the FVC as a percent predicted, indeed was lower among obese compared to non-obese subjects. Were misclassified obstruction to have accounted for the relationship between obesity and respiratory symptoms in our cohort, however, we would have expected a strong association between increased BMI and abnormal FEF25–75, which was not the case.
In summary, although airflow obstruction is relatively common in adults without an established COPD diagnosis and even though this impairment is related to cumulative smoking history, it may not be associated with respiratory symptoms or functional status. In contrast, obesity is negatively associated with the presence of obstruction and is strongly linked both to symptoms and functional impairment, even taking airflow obstruction into account. Strategies to manage respiratory symptoms and functional impairment among those with airflow limitation should consider the contribution of obesity to such abnormalities.
This work was supported by the research awards from the Flight Attendant’s Medical Research Institute, the University of California Tobacco-Related Diseases Research Program and the National Heart, Lung, and Blood Institute at the National Institutes of Health.
Dr. Zutler has no conflicts of interest to disclose. Dr. Singer has no conflicts of interest to disclose. Dr. Omachi has no conflicts of interest to disclose. Dr. Eisner has no conflicts of interest to disclose. Dr. Iribarren has no conflicts of interest to disclose. Dr. Katz has no conflicts of interest to disclose. This research was supported by the Flight Attendant’s Medical Research Institute (FAMRI Bland Lane Center for Excellence in Secondhand Smoke) and the University of California Tobacco-Related Diseases Research Program (17RT-0101), and the National Heart, Lung, and Blood Institute at the National Institutes of Health (5T32HL7185-34).