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Original Research |

Does Age Impact the Obese Asthma Phenotype?Does Age Impact the Obese Asthma Phenotype?: Longitudinal Asthma Control, Airway Function, and Airflow Perception Among Mild Persistent Asthmatics FREE TO VIEW

Jason E. Lang, MD; Jobayer Hossain, PhD; Anne E. Dixon, BMBCh, FCCP; David Shade, JD; Robert A. Wise, MD, FCCP; Stephen P. Peters, MD, PhD, FCCP; John J. Lima, PharmD; for the American Lung Association-Asthma Clinical Research Centers*
Author and Funding Information

From the Division of Pulmonology, Allergy and Immunology (Dr Lang), and the Center for Pharmacogenomics and Translational Research (Drs Lang and Lima), Nemours Children’s Clinic, Jacksonville, FL; The Center for Pediatric Research (Dr Hossain), Alfred I. DuPont Hospital of Children, Wilmington, and the Department of Food and Resource Economics (Dr Hossain), University of Delaware, Newark, DE; Pulmonary and Critical Care Medicine (Dr Dixon), University of Vermont College of Medicine, Burlington, VT; Johns Hopkins University School of Medicine (Mr Shade and Dr Wise), Baltimore, MD; and Wake Forest University School of Medicine (Dr Peters), Winston-Salem, NC.

Correspondence to: Jason E. Lang, MD, Division of Pulmonology, Allergy and Immunology, Center for Pharmacogenomics and Translational Research, Nemours Children’s Clinic, 807 Children’s Way, Jacksonville, FL 32207; e-mail: jelang@nemours.org

a

BMI percentile.

b

Raw BMI.

Standardized correlation coefficients are listed for nonobese and obese participants.

Data are presented as mean (SD) for quantitative variables and No. (%) for categorical variables, unless indicated otherwise. P value was determined by χ2, analysis of variance, or Kruskal Wallis test. ACQ = Asthma Control Questionnaire; ACQm = Asthma Control Questionnaire-modified (does not contain FEV1 component); ASUI = Asthma Symptoms Utility Index; BD = postbronchodilator; EBC = exhaled breath condensate; FSC = flutacisone salmeterol combination; PFvar = peak flow variability.

a

Defined as presence of allergic rhinitis, eczema, or food allergy.

b

Reporting that allergic rhinitis made asthma worse.

Data are presented as least square mean (SE). Nonobese group includes lean and overweight participants. See Table 3 legend for expansion of abbreviations.

No difference means that there was no significant statistical or clinical difference between obese and nonobese asthmatics for a given outcome by age group. See Table 3 legend for expansion of abbreviations.

a

Airflow perception describes the ratio between asthma symptoms (measured by ACQm) and PFvar.

*

A complete list of the American Lung Association Asthma Clinical Research Centers can be found in e-Appendix 1.

Funding/Support: This study was supported by an unrestricted grant from GlaxoSmithKline, which also supplied drugs and placebos for the parent trial, and a grant from the American Lung Association.

Reproduction of this article is prohibited without written permission from the American College of Chest Physicians (http://www.chestpubs.org/site/misc/reprints.xhtml).


A complete list of the American Lung Association Asthma Clinical Research Centers can be found in e-Appendix 1.

A complete list of the American Lung Association Asthma Clinical Research Centers can be found in e-Appendix 1.

Funding/Support: This study was supported by an unrestricted grant from GlaxoSmithKline, which also supplied drugs and placebos for the parent trial, and a grant from the American Lung Association.

Funding/Support: This study was supported by an unrestricted grant from GlaxoSmithKline, which also supplied drugs and placebos for the parent trial, and a grant from the American Lung Association.

Reproduction of this article is prohibited without written permission from the American College of Chest Physicians (http://www.chestpubs.org/site/misc/reprints.xhtml).

Reproduction of this article is prohibited without written permission from the American College of Chest Physicians (http://www.chestpubs.org/site/misc/reprints.xhtml).


Chest. 2011;140(6):1524-1533. doi:10.1378/chest.11-0675
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Background:  The relationship between obesity and asthma remains inadequately defined. Studies about how obesity affects asthma control and lung function show conflicting results. Additional focus on the effect of age as a modifier may make clearer the interaction between obesity and asthma phenotype. We sought to use a diverse and well-phenotyped cohort of asthmatic patients to determine how age impacts the relationship between obesity and spirometry, peak flow variability, airflow perception, and asthma control.

Methods:  The characteristics of 490 patients with mild persistent asthma taken from 2,794 study visits from a prospective trial studying strategies of step-down therapy were included in this post hoc analysis. A longitudinal mixed-effect model was used to determine if age affects the relationship between obesity and asthma characteristics, including spirometry, asthma control, airway pH, and perception of airflow changes.

Results:  The effect of obesity on asthma outcomes changes with age and gender. Obese 6- to 11-year-old children had the largest reduction in lung function but reported relatively fewer asthma symptoms than did similar nonobese asthmatics. Obese 12- to 17-year-olds showed a trend toward greater airflow obstruction and asthma symptoms compared with nonobese asthmatics. Adults in general displayed few obesity-related alterations in asthma phenotype. Female gender among 12- to 17- and 18- to 44-year-olds was associated with greater obesity-related asthma impairment.

Conclusions:  Age is a significant effect modifier on the relationship between obesity and asthma phenotype. With increasing age, the influence of obesity on the asthma phenotype is generally reduced. The asthma phenotype may be most impacted by obesity among children and women.

Trial registry:  ClinicalTrials.gov; No.: NCT00156819; URL: www.clinicaltrials.gov

Figures in this Article

Obesity increases the risk of new asthma19 and may complicate the asthma phenotype. Greater asthma impairment is reported among obese adults and children1017; however, some well-phenotyped asthma cohorts have failed to demonstrate significant obesity-related differences.1820 Whether obesity alters innate disease severity, airflow obstruction, and temporal airflow stability remains poorly understood.17,21,22 In addition, exhaled breath condensate (EBC) pH, asthma symptom scores, peak expiratory flow variability (PFvar), and the relationship between PFvar and symptom scores (airflow perception) have not been studied extensively among lean and obese asthmatics across a broad age range. Because age is pertinent to both the natural progression of asthma and current management guidelines, age may also be an important covariate in the interaction between obesity and asthma phenotype that may have confounded past studies.

We hypothesized that age is an important modifier of obesity’s impact on asthma. Few reports have evaluated how age and obesity interact and influence the perception of airway function. One report showed similar dyspnea reporting among lean and obese adult asthmatics following comparable bronchoconstriction.23 Others have shown increased asthma impairment among obese adults despite similar objective lung function.24,25 However, little is known about how obesity affects airway perception in the reporting of asthma symptoms in children. We present the analysis of an asthma trial involving a large, diverse cohort, ranging in age from 6 to 76 years from 19 US asthma centers, which assessed whether age impacts the association between obesity and asthma phenotype.

Details of the main study design have been published elsewhere.26 All participants signed written informed consents. The study was approved by the Nemours Florida institutional review board (03-016) and by all other American Lung Association Asthma Clinical Research Centers network institutional review boards. We included data from 2,794 study visit encounters involving 490 participants aged 6 to 76 years with mild persistent asthma randomized into a 16-week multicenter asthma trial assessing three step-down therapies.26

Data Collected

We analyzed demographics, daily peak flows, exhaled breath pH, spirometry, the Asthma Control Questionnaire (ACQ) score, and the Asthma Symptoms Utility Index (ASUI) of 494 participants.26 Spirometry was analyzed at randomization and again at 2, 4, 8, 12, and 16 weeks following randomization. PFvar was calculated for each 10-day block immediately preceding visits 3 to 8. We compared symptom questionnaires scores with PFvar taken during the corresponding time period: PFvar = (PFmax − PFmin)/PFmean, where PFmax is peak expiratory flow maximum, PFmin is peak expiratory flow minimum, and PFmean is peak expiratory flow mean.

Asthma symptoms were assessed using patient reporting from the ACQ and ASUI. The ACQ includes six questions that assess symptom control over the previous week, and it considers FEV1. We calculated a modified ACQ score (ACQm) excluding FEV1 to create a score based solely on patient subjective reporting. Participants were classified as underweight, normal weight, overweight, or obese based on their BMI (those aged 18-76 years) or BMI percentile (those aged 6-17 years) taken at enrollment and according to standard Centers for Disease Control classification (Table 1). For all statistical analyses, participants were grouped as either obese or nonobese. Weight and BMI were reassessed at the completion of the trial to adjust for any significant weight changes. The underweight participants (n = 4) were dropped from analyses.

Table Graphic Jump Location
Table 1 —BMI
a 

BMI percentile.

b 

Raw BMI.

Scores on the ACQ range from 0 to 6, with a higher score indicating worse asthma control; the minimal clinically important difference (MID) is 0.5. Scores on the ACQm range from 0 to 6, with a higher score indicating worse asthma control; the MID is unknown. Scores on the ASUI range from 0 to 1, with a higher score indicating better asthma control; the MID is unknown, but a difference of 0.3 is suggested to distinguish between mild to moderate and moderate to severe asthma.

Data Analysis

Baseline data were summarized by age groups. The χ2 test was used for comparing categorical variables between groups, whereas analysis of variance or Kruskal-Wallis tests were used to compare quantitative variables between groups. A longitudinal linear mixed-effect model with random intercept and an unstructured within-subject correlation structure for repeated measurements were used to determine obesity and its joint effects with age, gender, and atopy on asthma symptom reporting after controlling for the effects of race and treatment response. BMI group, age, gender, race, atopy, and interactions of BMI with age, gender, and atopy were used in the model. A longitudinal mixed-effect model with random intercept and an unstructured within-subject correlation structure for repeated measurements over study visits was used to determine the relationship between quantitative subjective vs objective asthma measures after stratifying to obese and nonobese groups and controlling for age, gender, and race. The study visit was used as a repeated variable to account for within-subject correlation for repeated measurements over visits during the study period in all these longitudinal mixed-effect models. We assessed the relationship between the ACQm (score of patient’s symptoms reported from a 7-day period) and the PFvar from the same period, represented by the standardized coefficients in Table 2.

Table Graphic Jump Location
Table 2 —Relationship Between Lung Function and Symptom Reporting: Peak Flow Variability vs Modified Asthma Control Score

Standardized correlation coefficients are listed for nonobese and obese participants.

Analyses were stratified by age groups. We collected data from > 95% of the potential visits. We had near-complete data on participant demographics. Missing data existed for EBC pH (19% of participants) and daily peak flows (≈ 13% of total participant-days). We used mixed-model analyses that handled “missingness” using both Missing Completely at Random and Missing at Random mechanisms. The mixed-model analysis procedure we used can accommodate this type of missingness with a correct specification of a model for means and covariances. We used caution in picking an appropriate model for means and covariances. More information on the handling of missing data is available in e-Appendix 2. SAS, version 9.1.2 (SAS Institute Inc; Cary, North Carolina) and SPSS, version 17.0 (SPSS Inc; Chicago, Illinois) were used. All tests were two-tailed at a level of significance of 0.05.

Baseline Characteristics

The baseline characteristics at randomization of 490 children, adolescents, and adults are shown by age group (Table 3). Approximately 60% of participants were between 18 and 44 years of age. Adults (18 to 76 years old) tended to be female, whereas younger participants were more likely to be male (Table 3). There was a significantly higher prevalence of blacks (50%) among children than among adolescents (34%) and adults (24%). Overweight/obese prevalence increased with advancing age among the four age groups (Fig 1). Increasing age saw a significant decline in FEV1 % predicted, FEV1 postbronchodilator improvement, FEV1/FVC, and PFvar in unadjusted analysis. Adults (aged 18-76 years) had significantly worse asthma symptom scores compared with 6- to 17-year-olds (Table 3, P < .03 for ACQ, ACQm, and ASUI). No correlations existed between age group and atopy or exhaled breath pH.

Table Graphic Jump Location
Table 3 —Baseline Participant Characteristics by Age Groups

Data are presented as mean (SD) for quantitative variables and No. (%) for categorical variables, unless indicated otherwise. P value was determined by χ2, analysis of variance, or Kruskal Wallis test. ACQ = Asthma Control Questionnaire; ACQm = Asthma Control Questionnaire-modified (does not contain FEV1 component); ASUI = Asthma Symptoms Utility Index; BD = postbronchodilator; EBC = exhaled breath condensate; FSC = flutacisone salmeterol combination; PFvar = peak flow variability.

a 

Defined as presence of allergic rhinitis, eczema, or food allergy.

b 

Reporting that allergic rhinitis made asthma worse.

Figure Jump LinkFigure 1. Percentage of participants who were overweight or obese by age group. Overweight or obese includes participants with randomization visit BMI > 85th percentile for children and adolescents (age groups 6-11 years and 12-17 years, respectively), or BMI > 25 for adults (age groups 18-44 and 45-76 years). Obese participants had a randomization visit BMI > 95th percentile for children and adolescents (age groups 6-11 and 12-17 years, respectively), or BMI > 30 for adults (age groups 18-44 and 45-76 years).Grahic Jump Location

An unadjusted analysis was also performed, comparing baseline characteristics among BMI groups. Normal BMI participants had slightly greater FEV1 % predicted and FEV1/FVC compared with overweight and obese participants, whereas there were no BMI-related associations with asthma control, bronchodilator responsiveness, PFvar, EBC pH, or atopy (data not shown).

Impact of Age on the Relationship Between Obesity and Asthma Outcomes

Using a longitudinal multivariate mixed-effect model without age stratification, we saw no clinically significant differences in lung function, bronchodilator responsiveness, PFvar, EBC pH, or asthma control between BMI groups (data not shown). We noted a statistical interaction between age in years and BMI (age × BMI) for FEV1/FVC (P < .001) and PFvar (P = .007). Statistical interaction effects (age × BMI) were weaker for FEV1 (P = .159) and ACQm (P = .279). Other interaction models using age in years and obesity group (age × obesity group) yielded similar results.

When we stratified by the four age groups (Table 4), we saw distinct age-related obesity and gender effects on several asthma outcomes. We noted relatively few obesity-related differences among 18- to 44- and 45- to 76-year-olds. Obesity did not significantly impact lung function decline with advancing age (Fig 2) (FEV1 % predicted, P = .103; FEV1/FVC, P = .307). Among obese 18- to 44-year-olds, we saw a reduced FEV1 % predicted driven mainly by a statistical interaction between obesity and female gender. In addition, obese 18- to 44-year-old women (but not men) reported worse asthma symptoms than did similar nonobese women (ACQm, P = .019; ACQ, P = .016; ASUI, P = .057). Obese 12- to 17-year-old female patients (but not male) also displayed worse asthma symptoms (ACQ, P = .008, ACQm, P = .02) and a trend toward reduced FEV1 % predicted (P = .078) compared with gender-matched nonobese adolescents. Obese asthmatic 6- to 11-year-olds (both genders) had significantly reduced FEV1 % predicted (P = .02) and FEV1/FVC (P = .001) compared with age-matched nonobese asthmatics. Obese 6- to 11-year-olds also reported fewer asthma symptoms compared with age-matched nonobese asthmatics. This obesity effect on reduced asthma symptoms was driven primarily by a statistical interaction with gender; obese male patients reported significantly fewer symptoms than did nonobese male patients (P < .05), whereas there was no obesity-related difference among 6- to 11-year-old female patients.

Table Graphic Jump Location
Table 4 —Longitudinal Lung Function and Asthma Control by Age Group and Obesity Status

Data are presented as least square mean (SE). Nonobese group includes lean and overweight participants. See Table 3 legend for expansion of abbreviations.

Figure Jump LinkFigure 2. A, Raw data points for FEV1 vs age among adults aged 18 to 76 years during visits 3 to 8, with fitted regression of mean predicted FEV1 % on age, adjusting for race, treatment, gender, and atopy. Regression coefficient describing change in FEV1 % predicted for change in age was 0.174 (P = .009) for obese adults vs −0.109 (P = .013) for nonobese adults. There was no rate difference in age-related FEV1 decline among obese vs nonobese asthmatics (P for difference in regression coefficients = .374). B, Raw data points for FEV1/FVC vs age among adults aged 18 to 76 years during visits 3 to 8, with fitted regression of mean predicted FEV1/FVC on age, adjusting for race, treatment, gender, and atopy. Regression coefficients describing change in FEV1/FVC for change in age was −0.007 (P = .103) for obese adults vs −0.0011 (P < .001) for nonobese adults. There was no statistical difference in age-related FEV1/FVC decline among obese vs nonobese asthmatics (P for difference in regression coefficients = .307). / denotes zero suppression.Grahic Jump Location

PFvar between obese and nonobese participants was similar within both adult age groups. We noted a trend toward reduced PFvar among the obese vs nonobese 6- to 11-year-olds (P = .09). The direction of this trend appeared to reverse among the adolescent age group (Table 4). No differences were seen in three symptom scores (ACQ, ACQm, ASUI) between obese and nonobese participants in the two adult age groups. A reduction in asthma symptoms was noted consistently in 6- to 11-year-old obese patients with asthma from all three validated scores (ACQ, ASUI, ACQm). This was not seen among obese 12- to 17-year-olds, in whom there was a trend toward a higher mean ACQ score, suggesting greater symptom reporting (P = .09, two-sided).

Impact of Obesity on the Relationship Between Lung Function and Symptom Reporting

We evaluated the relationship between PFvar and the corresponding symptom reporting (ACQm) from the same time period. The degree of the asthma-symptoms-to-PFvar relationship (termed “airflow perception”) was compared between obese and nonobese participants in the four age groups (Table 2). The extent and direction of the relationships are quantified in Table 2 by standardized coefficient. We evaluated how obesity status affects the relationship between fluctuations in airflow and the resulting symptoms over the same period. Among 45- to 76-year-olds, there was no significant relationship between PFvar and asthma symptom reporting (ACQm) in either obese (−1.014, P = .313) or nonobese (−0.743, P = .459) participants. However, among the three younger age groups, both obese and nonobese participants displayed a significant positive relationship between PFvar and ACQm (an increase in PFvar corresponds to an increase in symptom reporting). For the three younger groups, there was also a statistically significant difference in the degree of the relationship between the obese and nonobese; however, the direction of association was age dependent. Among 18- to 44-year-olds, the standardized coefficient between PFvar and ACQm was significantly greater among nonobese than among obese participants (5.45 vs 3.38, P < .001). However, in the pediatric population, the direction of this relationship was reversed. Among 6- to 11-year-olds and 12- to 17-year-olds, the standardized coefficients between PFvar and ACQm were significantly greater among the obese population than among the nonobese (Table 2).

A similar analysis was conducted comparing subjective vs objective asthma measures using FEV1 % with the ACQm. Changes in FEV1 % did not correlate as well with symptom control as did PFvar, particularly among the obese (data not shown). Table 5 summarizes the major obesity-related asthma findings by age group.

Table Graphic Jump Location
Table 5 —Obesity-Related Characteristics in Asthma Phenotype by Age Group

No difference means that there was no significant statistical or clinical difference between obese and nonobese asthmatics for a given outcome by age group. See Table 3 legend for expansion of abbreviations.

a 

Airflow perception describes the ratio between asthma symptoms (measured by ACQm) and PFvar.

Obesity is a risk factor for new asthma across a broad age range15,27 and may complicate asthma control.17,22,28 After assessing lung function, breath pH, airflow lability, symptom control, and airflow perception among obese and nonobese participants, we conclude that age is an important effect modifier in the obesity-asthma relationship and, along with gender, is a primary covariate determining the obese-asthma phenotype.

In summary, young children appeared to be most affected by obesity in terms of lung function, and were the only group with evidence of obesity-related airflow obstruction. Paradoxically, obese young children, primarily boys, reported reduced asthma symptoms. Obesity, however, may be most problematic among adolescents. Despite a limited power, the obese adolescent group consistently showed a tendency for worse lung function, PFvar, and symptom reporting compared with nonobese adolescents. Maintaining a healthy weight may be most important during the adolescent years. Obese adults between 18 and 44 years of age (of both genders) had significantly reduced FEV1 % predicted compared with comparable leans. Although obese asthmatics may be at greater risk of asthma attacks,11,29 our data suggest that these attacks do not appear to result from greater airway contractility or lability. There was also a significant association between PFvar and symptom reporting in all age groups except the 44- to 76-year-olds. Obesity was an important effect-modifier that interacted with age in this PFvar-ACQm association. Both pediatric obese groups were more sensitive to changes in peak flows than were their comparable pediatric nonobese groups (greater airflow perception), whereas the young-adult obese group had reduced airway perception.

When we assessed the effect of obesity among the entire cohort (without age stratification), we found that obesity had little impact on asthma outcomes. However, with age-group stratification, the longitudinal data revealed important BMI-related differences. Obese 6- to 11-year-olds of both genders had significantly reduced FEV1 % predicted and FEV1/FVC compared with nonobese children. Low-volume respiration (the “latch-effect”30) has been proposed in obesity-related lung impairment; however, this mechanism might be expected to lead to greater β-agonist response, which we did not see. Obese 6- to 11-year-olds had normal airway pH and fewer symptoms. These findings do not support the mechanisms of esophageal reflux or greater airway inflammation as factors in obesity-related lung impairment. These findings need to be interpreted with caution because measurement of airway acidification can be variably influenced by a host of collection and handling factors.31 Obesity may not act causally in early airflow obstruction, but rather, large body mass early in life may be associated with altered development patterns that include more-accelerated lung volume expansion with a relative delay in airway caliber. Obese boys, despite reduced lung function compared with leans, reported fewer symptoms, suggesting that early growth alterations could also impact disease perception. Further longitudinal studies that assess body mass, airway function, and symptom perception during development are needed.

Others have reported a reduction in asthma-related quality of life in obese children.25 Paradoxically, we found lower lung function, with fewer symptoms, in the 6- to 11-year-old obese children. This might be explained by reduced activity levels among obese children. A sedentary nature among some young asthmatic children could be an underlying mechanism for both obesity and reduced symptoms, which would lead to the paradoxical association that we saw in 6- to 11-year-olds. Alternatively, obese 6- to 11-year-old male patients could have inherently more stable disease, which is supported by the trend for reduced PFvar in 6- to 11-year-olds overall, resulting from an altered response to early viral infection or triggers such as environmental tobacco smoke, compared with their leaner age-matched counterparts. Obesity in this early age may impair adaptive responses to respiratory viruses, resulting in reduced asthma triggering. Obese animal models have shown impaired cytokine responses to influenza32 and delayed inflammatory cell recruitment to the lung.33 It is not clear why obesity increases the risk of new asthma in this young age,5,6 while being associated with reduced asthma symptoms. Obesity has been associated most strongly with nonatopic asthma, whereas our cohort was primarily of the atopic phenotype. It is possible that obesity among atopic asthmatics may ameliorate allergic airway inflammation and resulting symptoms.

Obese adolescent boys and girls, unlike the 6- to 11-year-olds, did not have significantly improved asthma control. We noted a gender and age interaction, in which obese adolescent girls had greater asthma symptoms compared with nonobese girls. The degree of obesity-related airflow obstruction was even greater in obese adolescents (compared with 6- to 11-year-olds), but did not reach statistical significance. Among girls only, FEV1 % predicted was reduced in the obese. We and others have reported airflow obstruction among obese asthmatics.3436 Weight loss as an asthma therapy in children deserves study, and these data suggest that adolescents especially should be encouraged to take a multidisciplinary approach to asthma control when obesity is present.

Among adults, obesity-related differences in asthma control were absent. The ACRC previously reported few differences between obese and lean asthmatic adults.19 The current analysis saw only a reduced FEV1 % predicted (primarily among female patients) and PFvar-asthma symptom association (both genders) among 18- to 44-year-olds. These findings were absent in 45- to 76-year-olds. We theorized that obesity may accelerate age-related lung function decline, but this was not the case.

Past studies have shown an inconsistent statistical interaction between gender and obesity regarding asthma risk.35,9,37,38 For adults, there may be a greater risk of incident asthma among obese woman than among obese men.39 In youths, the age of obesity onset appears to be important. Cohort studies in younger children do not show a consistent gender risk pattern. However, after puberty, adolescent girls appear to be more consistently at risk of new obesity-related asthma.4,5,9 Fewer studies involving well-characterized asthmatics have addressed the obesity-gender interaction on phenotype characteristics. In our cohort, despite substantially limited power, we found that female gender, especially among the 12- to 17-year-old and 18- to 44-year-old age groups, was associated with greater obesity-related asthma impairment. Tantisira et al40 found no gender-obesity interaction affecting asthma symptoms in the Childhood Asthma Management Program cohort involving 5- to 12-year-olds, but did see a reduction in FEV1/FVC, mainly in boys, and greater airway responsiveness, mainly in girls. Ross and colleagues18 studied 4- to 18-year-old asthmatics and did not find a statistical interaction between obesity and gender on either asthma control or lung function.

To our knowledge, this was the first study to assess obesity-gender effect modification on asthma phenotype across a broad age range. Although our study involved highly phenotyped asthmatics, the analysis was seriously hampered by power in its ability to assess multiple interactions. Larger studies, involving well-phenotyped asthma cohorts, are needed to untangle the age-specific effects of gender and obesity on asthma.

This is also the first study, to our knowledge, that defines the impact of obesity and age on PFvar. A positive standardized coefficient suggests a direct relationship between PFvar and asthma symptom reporting. Obese children and adolescents had a significantly greater direct relationship compared with the nonobese, suggesting that the obese may have a more acute sensitivity to airflow alterations, which may contribute to greater symptom reporting and poorer asthma control reported previously among this group.11,13,15 Age modifies the PFvar-symptom score relationship. Obese 18- to 44-year-olds displayed a significantly reduced standardized coefficient compared with nonobese adults, suggesting that obesity during early adulthood may be associated with an altered perception of airflow. Data on how obesity impacts symptom reporting, adjusted for objective lung impairment, are scarce. Reports of greater asthma severity in the obese appear to stem primarily from greater symptom reporting and health-care use.14,15,29 Our data suggest that, at least among adolescents, poor asthma control reported among the obese may be, in part, the result of a heightened perception of airflow perturbations.

A limitation of this study is the inclusion of only mild persistent asthmatics. Although the majority of persistent asthmatics have mild disease, patients with moderate to severe disease consume a large portion of asthma-related health resources. This study describes how obesity impacts patients of a broad age range having mild-persistent disease during a relatively stable step-down period. However, this study cannot comment directly on the impact of obesity and age on moderate-severe disease. It is also important to note that our findings are the result of a post hoc analysis of data from a multicenter clinical trial not specifically designed to assess the effects of age and obesity on asthma outcomes. Another limitation is the use of PFvar. PFvar is a far-from-perfect objective marker of airway function. Although peak flow monitoring is recommended as a potential component of daily self-management,41 its use has fallen out of favor with many experts because of challenges with adherence and variability, especially with young children. However, it nonetheless gives us some objective measure of day-to-day airway functioning during the exact period of time for which the patient was reporting his or her symptoms. Lastly, this study was limited by power. In order to complete a thorough analysis of the modifying effects of age and gender on the impact of obesity on asthma outcomes, a study must be both large and diverse. Although we were able to show some significant effects related to age and gender in this moderately sized asthma trial, some of the subgroups (eg, obese 12- to 17-year-old female patients) were small. Multiple comparisons involving small groups are susceptible to both type 1 (false-positive) and type 2 (false-negative) error, and are limited in their ability to fully assess statistical interactions and confounding effects. Nevertheless, our data suggest that there is not a universal “obese asthma” phenotype, and that gender and age are important covariates. These data strongly suggest that in order to define the impact of obesity on the asthma phenotype, large cohorts involving children, adolescents, and older adults of diverse demographic backgrounds must undergo further study.

Overall, this study shows that the effects of obesity on lung function and asthma scores change with age. The greatest effects may occur in childhood and women and may be mediated by alterations in growth and airflow perception, rather than by airway inflammation or instability.

Author contributions: Dr Lang vouches for the integrity of the data and the accuracy of the data analysis.

Dr Lang: contributed to the conception and design of the study; acquisition, analysis, and interpretation of data; and drafting of the submitted manuscript.

Dr Hossain: contributed to the analysis and interpretation of data and critical revision of the manuscript for important intellectual content.

Dr Dixon: contributed to the design of the study, acquisition of data, and critical editing of the manuscript for important intellectual content.

Mr Shade: contributed to the design of the study, acquisition of data, and critical editing of the manuscript for important intellectual content.

Dr Wise: contributed to the design of the study, acquisition of data, drafting of the submitted manuscript, and critical revision of the manuscript for important intellectual content.

Dr Peters: contributed to the main clinical trial on which this analysis was based, analysis and interpretation of data for the current study, drafting of the submitted manuscript, and critical revision of the manuscript for important intellectual content.

Dr Lima: contributed to the conception and design of the study; acquisition, analysis, and interpretation of data; drafting of the submitted manuscript; and critical revision of the manuscript for important intellectual content.

Financial/nonfinancial disclosures: The authors have reported to CHEST the following conflicts of interest: Dr Wise receives consulting fees from GlaxoSmithKline, Boehringer Ingleheim, Pfizer, Merck, Novartis, AstraZeneca, Intermune, Medimmune Dey, and research support from GlaxoSmithKline, Boehringer Ingleheim, and Forest. Dr Peters receives consulting fees from AstraZeneca, Discovery, Ception Therapeutics, Genentech, Merck, Novartis, Sanofi-Aventis, and Sepracor, and lecture fees from Merck, AstraZeneca, Genentech, and Novartis. Drs Lang, Hossain, Dixon, Shade, and Lima have reported that no potential conflicts of interest exist with any companies/organizations whose products or services may be discussed in this article.

Additional information: The e-Appendixes can be found in the Online Supplement at http://chestjournal.chestpubs.org/content/140/6/1524/suppl/DC1.

Role of sponsors: GlaxoSmithKline had no role in the design of the study, the collection and analysis of the data, or in the preparation of the manuscript. The Data Coordinating Center of the American Lung Association Asthma Clinical Research Centers was involved in the collection and analysis of the parent study; for the current study, the Data Coordinating Center of the American Lung Association Asthma Clinical Research Centers did not have a role in the statistical design. However, all authors, except Dr Hossain, are members of the American Lung Association Asthma Clinical Research Centers. No employees of the American Lung Association were involved with study design, data collection/analysis or manuscript preparation.

Author contributions: Dr Lang vouches for the integrity of the data and the accuracy of the data analysis.

Dr Lang: contributed to the conception and design of the study; acquisition, analysis, and interpretation of data; and drafting of the submitted manuscript.

Dr Hossain: contributed to the analysis and interpretation of data and critical revision of the manuscript for important intellectual content.

Dr Dixon: contributed to the design of the study, acquisition of data, and critical editing of the manuscript for important intellectual content.

Mr Shade: contributed to the design of the study, acquisition of data, and critical editing of the manuscript for important intellectual content.

Dr Wise: contributed to the design of the study, acquisition of data, drafting of the submitted manuscript, and critical revision of the manuscript for important intellectual content.

Dr Peters: contributed to the main clinical trial on which this analysis was based, analysis and interpretation of data for the current study, drafting of the submitted manuscript, and critical revision of the manuscript for important intellectual content.

Dr Lima: contributed to the conception and design of the study; acquisition, analysis, and interpretation of data; drafting of the submitted manuscript; and critical revision of the manuscript for important intellectual content.

Financial/nonfinancial disclosures: The authors have reported to CHEST the following conflicts of interest: Dr Wise receives consulting fees from GlaxoSmithKline, Boehringer Ingleheim, Pfizer, Merck, Novartis, AstraZeneca, Intermune, Medimmune Dey, and research support from GlaxoSmithKline, Boehringer Ingleheim, and Forest. Dr Peters receives consulting fees from AstraZeneca, Discovery, Ception Therapeutics, Genentech, Merck, Novartis, Sanofi-Aventis, and Sepracor, and lecture fees from Merck, AstraZeneca, Genentech, and Novartis. Drs Lang, Hossain, Dixon, Shade, and Lima have reported that no potential conflicts of interest exist with any companies/organizations whose products or services may be discussed in this article.

Additional information: The e-Appendixes can be found in the Online Supplement at http://chestjournal.chestpubs.org/content/140/6/1524/suppl/DC1.

Role of sponsors: GlaxoSmithKline had no role in the design of the study, the collection and analysis of the data, or in the preparation of the manuscript. The Data Coordinating Center of the American Lung Association Asthma Clinical Research Centers was involved in the collection and analysis of the parent study; for the current study, the Data Coordinating Center of the American Lung Association Asthma Clinical Research Centers did not have a role in the statistical design. However, all authors, except Dr Hossain, are members of the American Lung Association Asthma Clinical Research Centers. No employees of the American Lung Association were involved with study design, data collection/analysis or manuscript preparation.

ACQ

Asthma Control Questionnaire

ACQm

Asthma Control Questionnaire-modified

ASUI

Asthma Symptoms Utility Index

EBC

exhaled breath condensate

MID

minimal clinically important difference

PFvar

peak expiratory flow variability

Nystad W, Meyer HE, Nafstad P, Tverdal A, Engeland A. Body mass index in relation to adult asthma among 135,000 Norwegian men and women. Am J Epidemiol. 2004;16010:969-976 [CrossRef] [PubMed]
 
Camargo CA Jr, Weiss ST, Zhang S, Willett WC, Speizer FE. Prospective study of body mass index, weight change, and risk of adult-onset asthma in women. Arch Intern Med. 1999;15921:2582-2588 [CrossRef]
 
Gilliland FD, Berhane K, Islam T, et al. Obesity and the risk of newly diagnosed asthma in school-age children. Am J Epidemiol. 2003;1585:406-415 [CrossRef]
 
Castro-Rodríguez JA, Holberg CJ, Morgan WJ, Wright AL, Martinez FD. Increased incidence of asthmalike symptoms in girls who become overweight or obese during the school years. Am J Respir Crit Care Med. 2001;1636:1344-1349
 
Gold DR, Damokosh AI, Dockery DW, Berkey CS. Body-mass index as a predictor of incident asthma in a prospective cohort of children. Pediatr Pulmonol. 2003;366:514-521 [CrossRef]
 
Mannino DM, Mott J, Ferdinands JM, et al. Boys with high body masses have an increased risk of developing asthma: findings from the National Longitudinal Survey of Youth (NLSY). Int J Obes (Lond). 2006;301:6-13 [CrossRef]
 
Taveras EM, Rifas-Shiman SL, Camargo CA Jr, et al. Higher adiposity in infancy associated with recurrent wheeze in a prospective cohort of children. J Allergy Clin Immunol. 2008;1215:1161-1166 [CrossRef]
 
Chinn S, Rona RJ. Can the increase in body mass index explain the rising trend in asthma in children? Thorax. 2001;5611:845-850 [CrossRef]
 
Guerra S, Wright AL, Morgan WJ, Sherrill DL, Holberg CJ, Martinez FD. Persistence of asthma symptoms during adolescence: role of obesity and age at the onset of puberty. Am J Respir Crit Care Med. 2004;1701:78-85 [CrossRef]
 
Luder E, Melnik TA, DiMaio M. Association of being overweight with greater asthma symptoms in inner city black and Hispanic children. J Pediatr. 1998;1324:699-703 [CrossRef]
 
Belamarich PF, Luder E, Kattan M, et al. Do obese inner-city children with asthma have more symptoms than nonobese children with asthma? Pediatrics. 2000;1066:1436-1441 [CrossRef]
 
Cassol VE, Rizzato TM, Teche SP, et al. Obesity and its relationship with asthma prevalence and severity in adolescents from southern Brazil. J Asthma. 2006;431:57-60 [CrossRef]
 
Michelson PH, Williams LW, Benjamin DK, Barnato AE. Obesity, inflammation, and asthma severity in childhood: data from the National Health and Nutrition Examination Survey 2001-2004. Ann Allergy Asthma Immunol. 2009;1035:381-385 [CrossRef]
 
Mosen DM, Schatz M, Magid DJ. Camargo CA Jr. The relationship between obesity and asthma severity and control in adults. J Allergy Clin Immunol. 2008;1223:507-511 e6. [CrossRef]
 
Taylor B, Mannino D, Brown C, Crocker D, Twum-Baah N, Holguin F. Body mass index and asthma severity in the National Asthma Survey. Thorax. 2008;631:14-20 [CrossRef]
 
Vortmann M, Eisner MD. BMI and health status among adults with asthma. Obesity (Silver Spring). 2008;161:146-152 [CrossRef]
 
Forno E, Lescher R, Strunk R, Weiss S, Fuhlbrigge A, Celedón JC. Childhood Asthma Management Program Research Group Childhood Asthma Management Program Research Group Decreased response to inhaled steroids in overweight and obese asthmatic children. J Allergy Clin Immunol. 2011;1273:741-749 [CrossRef]
 
Ross KR, Hart MA, Storfer-Isser A, et al. Obesity and obesity related co-morbidities in a referral population of children with asthma. Pediatr Pulmonol. 2009;449:877-884 [CrossRef]
 
Dixon AE, Shade DM, Cohen RI, et al; American Lung Association-Asthma Clinical Research Centers American Lung Association-Asthma Clinical Research Centers Effect of obesity on clinical presentation and response to treatment in asthma. J Asthma. 2006;437:553-558 [CrossRef]
 
Sutherland ER, Lehman EB, Teodorescu M. Wechsler ME; National Heart, Lung, and Blood Institute’s Asthma Clinical Research Network. Body mass index and phenotype in subjects with mild-to-moderate persistent asthma. J Allergy Clin Immunol. 2009;1236:1328-1334 [CrossRef]
 
Camargo CA Jr, Boulet LP, Sutherland ER, et al. Body mass index and response to asthma therapy: fluticasone propionate/salmeterol versus montelukast. J Asthma. 2010;471:76-82 [CrossRef]
 
Peters-Golden M, Swern A, Bird SS, Hustad CM, Grant E, Edelman JM. Influence of body mass index on the response to asthma controller agents. Eur Respir J. 2006;273:495-503 [CrossRef]
 
Lessard A, Turcotte H, Cormier Y, Boulet LP. Obesity and asthma: a specific phenotype? Chest. 2008;1342:317-323 [CrossRef]
 
Pianosi PT, Davis HS. Determinants of physical fitness in children with asthma. Pediatrics. 2004;1133 pt 1:e225-e229 [CrossRef]
 
van Gent R, van der Ent CK, Rovers MM, Kimpen JL, van Essen-Zandvliet LE, de Meer G. Excessive body weight is associated with additional loss of quality of life in children with asthma. J Allergy Clin Immunol. 2007;1193:591-596 [CrossRef]
 
Peters SP, Anthonisen N, Castro M, et al; American Lung Association Asthma Clinical Research Centers American Lung Association Asthma Clinical Research Centers Randomized comparison of strategies for reducing treatment in mild persistent asthma. N Engl J Med. 2007;35620:2027-2039 [CrossRef]
 
Beuther DA, Weiss ST, Sutherland ER. Obesity and asthma. Am J Respir Crit Care Med. 2006;1742:112-119 [CrossRef]
 
Sutherland ER, Goleva E, Strand M, Beuther DA, Leung DY. Body mass and glucocorticoid response in asthma. Am J Respir Crit Care Med. 2008;1787:682-687 [CrossRef]
 
Wen XJ, Balluz L, Mokdad A. Do obese adults have a higher risk of asthma attack when exposed to indoor mold? A study based on the 2005 Behavioral Risk Factor Surveillance System. Public Health Rep. 2009;1243:436-441
 
Fredberg JJ, Inouye D, Miller B, et al. Airway smooth muscle, tidal stretches, and dynamically determined contractile states. Am J Respir Crit Care Med. 1997;1566:1752-1759
 
Antus B, Barta I, Kullmann T, et al. Assessment of exhaled breath condensate pH in exacerbations of asthma and chronic obstructive pulmonary disease: a longitudinal study. Am J Respir Crit Care Med. 2010;18212:1492-1497 [CrossRef]
 
Smith AG, Sheridan PA, Harp JB, Beck MA. Diet-induced obese mice have increased mortality and altered immune responses when infected with influenza virus. J Nutr. 2007;1375:1236-1243
 
Smith AG, Sheridan PA, Tseng RJ, Sheridan JF, Beck MA. Selective impairment in dendritic cell function and altered antigen-specific CD8+ T-cell responses in diet-induced obese mice infected with influenza virus. Immunology. 2009;1262:268-279 [CrossRef]
 
Chu YT, Chen WY, Wang TN, Tseng HI, Wu JR, Ko YC. Extreme BMI predicts higher asthma prevalence and is associated with lung function impairment in school-aged children. Pediatr Pulmonol. 2009;445:472-479 [CrossRef]
 
Lang JE, Feng H, Lima JJ. Body mass index-percentile and diagnostic accuracy of childhood asthma. J Asthma. 2009;463:291-299 [CrossRef]
 
Spathopoulos D, Paraskakis E, Trypsianis G, et al. The effect of obesity on pulmonary lung function of school aged children in Greece. Pediatr Pulmonol. 2009;443:273-280 [CrossRef]
 
Beckett WS, Jacobs DR Jr, Yu X, Iribarren C, Williams OD. Asthma is associated with weight gain in females but not males, independent of physical activity. Am J Respir Crit Care Med. 2001;16411:2045-2050
 
Hancox RJ, Milne BJ, Poulton R, et al. Sex differences in the relation between body mass index and asthma and atopy in a birth cohort. Am J Respir Crit Care Med. 2005;1715:440-445 [CrossRef]
 
Beuther DA, Sutherland ER. Overweight, obesity, and incident asthma: a meta-analysis of prospective epidemiologic studies. Am J Respir Crit Care Med. 2007;1757:661-666 [CrossRef]
 
Tantisira KG, Litonjua AA, Weiss ST, Fuhlbrigge AL. Childhood Asthma Management Program Research Group Childhood Asthma Management Program Research Group Association of body mass with pulmonary function in the Childhood Asthma Management Program (CAMP). Thorax. 2003;5812:1036-1041 [CrossRef]
 
National Asthma Education and Prevention ProgramNational Asthma Education and Prevention Program Expert Panel Report 3 (EPR-3): Guidelines for the diagnosis and management of asthma-summary report 2007. J Allergy Clin Immunol. 2007;120suppl 5:S94-S138 [CrossRef]
 

Figures

Figure Jump LinkFigure 1. Percentage of participants who were overweight or obese by age group. Overweight or obese includes participants with randomization visit BMI > 85th percentile for children and adolescents (age groups 6-11 years and 12-17 years, respectively), or BMI > 25 for adults (age groups 18-44 and 45-76 years). Obese participants had a randomization visit BMI > 95th percentile for children and adolescents (age groups 6-11 and 12-17 years, respectively), or BMI > 30 for adults (age groups 18-44 and 45-76 years).Grahic Jump Location
Figure Jump LinkFigure 2. A, Raw data points for FEV1 vs age among adults aged 18 to 76 years during visits 3 to 8, with fitted regression of mean predicted FEV1 % on age, adjusting for race, treatment, gender, and atopy. Regression coefficient describing change in FEV1 % predicted for change in age was 0.174 (P = .009) for obese adults vs −0.109 (P = .013) for nonobese adults. There was no rate difference in age-related FEV1 decline among obese vs nonobese asthmatics (P for difference in regression coefficients = .374). B, Raw data points for FEV1/FVC vs age among adults aged 18 to 76 years during visits 3 to 8, with fitted regression of mean predicted FEV1/FVC on age, adjusting for race, treatment, gender, and atopy. Regression coefficients describing change in FEV1/FVC for change in age was −0.007 (P = .103) for obese adults vs −0.0011 (P < .001) for nonobese adults. There was no statistical difference in age-related FEV1/FVC decline among obese vs nonobese asthmatics (P for difference in regression coefficients = .307). / denotes zero suppression.Grahic Jump Location

Tables

Table Graphic Jump Location
Table 1 —BMI
a 

BMI percentile.

b 

Raw BMI.

Table Graphic Jump Location
Table 2 —Relationship Between Lung Function and Symptom Reporting: Peak Flow Variability vs Modified Asthma Control Score

Standardized correlation coefficients are listed for nonobese and obese participants.

Table Graphic Jump Location
Table 3 —Baseline Participant Characteristics by Age Groups

Data are presented as mean (SD) for quantitative variables and No. (%) for categorical variables, unless indicated otherwise. P value was determined by χ2, analysis of variance, or Kruskal Wallis test. ACQ = Asthma Control Questionnaire; ACQm = Asthma Control Questionnaire-modified (does not contain FEV1 component); ASUI = Asthma Symptoms Utility Index; BD = postbronchodilator; EBC = exhaled breath condensate; FSC = flutacisone salmeterol combination; PFvar = peak flow variability.

a 

Defined as presence of allergic rhinitis, eczema, or food allergy.

b 

Reporting that allergic rhinitis made asthma worse.

Table Graphic Jump Location
Table 4 —Longitudinal Lung Function and Asthma Control by Age Group and Obesity Status

Data are presented as least square mean (SE). Nonobese group includes lean and overweight participants. See Table 3 legend for expansion of abbreviations.

Table Graphic Jump Location
Table 5 —Obesity-Related Characteristics in Asthma Phenotype by Age Group

No difference means that there was no significant statistical or clinical difference between obese and nonobese asthmatics for a given outcome by age group. See Table 3 legend for expansion of abbreviations.

a 

Airflow perception describes the ratio between asthma symptoms (measured by ACQm) and PFvar.

References

Nystad W, Meyer HE, Nafstad P, Tverdal A, Engeland A. Body mass index in relation to adult asthma among 135,000 Norwegian men and women. Am J Epidemiol. 2004;16010:969-976 [CrossRef] [PubMed]
 
Camargo CA Jr, Weiss ST, Zhang S, Willett WC, Speizer FE. Prospective study of body mass index, weight change, and risk of adult-onset asthma in women. Arch Intern Med. 1999;15921:2582-2588 [CrossRef]
 
Gilliland FD, Berhane K, Islam T, et al. Obesity and the risk of newly diagnosed asthma in school-age children. Am J Epidemiol. 2003;1585:406-415 [CrossRef]
 
Castro-Rodríguez JA, Holberg CJ, Morgan WJ, Wright AL, Martinez FD. Increased incidence of asthmalike symptoms in girls who become overweight or obese during the school years. Am J Respir Crit Care Med. 2001;1636:1344-1349
 
Gold DR, Damokosh AI, Dockery DW, Berkey CS. Body-mass index as a predictor of incident asthma in a prospective cohort of children. Pediatr Pulmonol. 2003;366:514-521 [CrossRef]
 
Mannino DM, Mott J, Ferdinands JM, et al. Boys with high body masses have an increased risk of developing asthma: findings from the National Longitudinal Survey of Youth (NLSY). Int J Obes (Lond). 2006;301:6-13 [CrossRef]
 
Taveras EM, Rifas-Shiman SL, Camargo CA Jr, et al. Higher adiposity in infancy associated with recurrent wheeze in a prospective cohort of children. J Allergy Clin Immunol. 2008;1215:1161-1166 [CrossRef]
 
Chinn S, Rona RJ. Can the increase in body mass index explain the rising trend in asthma in children? Thorax. 2001;5611:845-850 [CrossRef]
 
Guerra S, Wright AL, Morgan WJ, Sherrill DL, Holberg CJ, Martinez FD. Persistence of asthma symptoms during adolescence: role of obesity and age at the onset of puberty. Am J Respir Crit Care Med. 2004;1701:78-85 [CrossRef]
 
Luder E, Melnik TA, DiMaio M. Association of being overweight with greater asthma symptoms in inner city black and Hispanic children. J Pediatr. 1998;1324:699-703 [CrossRef]
 
Belamarich PF, Luder E, Kattan M, et al. Do obese inner-city children with asthma have more symptoms than nonobese children with asthma? Pediatrics. 2000;1066:1436-1441 [CrossRef]
 
Cassol VE, Rizzato TM, Teche SP, et al. Obesity and its relationship with asthma prevalence and severity in adolescents from southern Brazil. J Asthma. 2006;431:57-60 [CrossRef]
 
Michelson PH, Williams LW, Benjamin DK, Barnato AE. Obesity, inflammation, and asthma severity in childhood: data from the National Health and Nutrition Examination Survey 2001-2004. Ann Allergy Asthma Immunol. 2009;1035:381-385 [CrossRef]
 
Mosen DM, Schatz M, Magid DJ. Camargo CA Jr. The relationship between obesity and asthma severity and control in adults. J Allergy Clin Immunol. 2008;1223:507-511 e6. [CrossRef]
 
Taylor B, Mannino D, Brown C, Crocker D, Twum-Baah N, Holguin F. Body mass index and asthma severity in the National Asthma Survey. Thorax. 2008;631:14-20 [CrossRef]
 
Vortmann M, Eisner MD. BMI and health status among adults with asthma. Obesity (Silver Spring). 2008;161:146-152 [CrossRef]
 
Forno E, Lescher R, Strunk R, Weiss S, Fuhlbrigge A, Celedón JC. Childhood Asthma Management Program Research Group Childhood Asthma Management Program Research Group Decreased response to inhaled steroids in overweight and obese asthmatic children. J Allergy Clin Immunol. 2011;1273:741-749 [CrossRef]
 
Ross KR, Hart MA, Storfer-Isser A, et al. Obesity and obesity related co-morbidities in a referral population of children with asthma. Pediatr Pulmonol. 2009;449:877-884 [CrossRef]
 
Dixon AE, Shade DM, Cohen RI, et al; American Lung Association-Asthma Clinical Research Centers American Lung Association-Asthma Clinical Research Centers Effect of obesity on clinical presentation and response to treatment in asthma. J Asthma. 2006;437:553-558 [CrossRef]
 
Sutherland ER, Lehman EB, Teodorescu M. Wechsler ME; National Heart, Lung, and Blood Institute’s Asthma Clinical Research Network. Body mass index and phenotype in subjects with mild-to-moderate persistent asthma. J Allergy Clin Immunol. 2009;1236:1328-1334 [CrossRef]
 
Camargo CA Jr, Boulet LP, Sutherland ER, et al. Body mass index and response to asthma therapy: fluticasone propionate/salmeterol versus montelukast. J Asthma. 2010;471:76-82 [CrossRef]
 
Peters-Golden M, Swern A, Bird SS, Hustad CM, Grant E, Edelman JM. Influence of body mass index on the response to asthma controller agents. Eur Respir J. 2006;273:495-503 [CrossRef]
 
Lessard A, Turcotte H, Cormier Y, Boulet LP. Obesity and asthma: a specific phenotype? Chest. 2008;1342:317-323 [CrossRef]
 
Pianosi PT, Davis HS. Determinants of physical fitness in children with asthma. Pediatrics. 2004;1133 pt 1:e225-e229 [CrossRef]
 
van Gent R, van der Ent CK, Rovers MM, Kimpen JL, van Essen-Zandvliet LE, de Meer G. Excessive body weight is associated with additional loss of quality of life in children with asthma. J Allergy Clin Immunol. 2007;1193:591-596 [CrossRef]
 
Peters SP, Anthonisen N, Castro M, et al; American Lung Association Asthma Clinical Research Centers American Lung Association Asthma Clinical Research Centers Randomized comparison of strategies for reducing treatment in mild persistent asthma. N Engl J Med. 2007;35620:2027-2039 [CrossRef]
 
Beuther DA, Weiss ST, Sutherland ER. Obesity and asthma. Am J Respir Crit Care Med. 2006;1742:112-119 [CrossRef]
 
Sutherland ER, Goleva E, Strand M, Beuther DA, Leung DY. Body mass and glucocorticoid response in asthma. Am J Respir Crit Care Med. 2008;1787:682-687 [CrossRef]
 
Wen XJ, Balluz L, Mokdad A. Do obese adults have a higher risk of asthma attack when exposed to indoor mold? A study based on the 2005 Behavioral Risk Factor Surveillance System. Public Health Rep. 2009;1243:436-441
 
Fredberg JJ, Inouye D, Miller B, et al. Airway smooth muscle, tidal stretches, and dynamically determined contractile states. Am J Respir Crit Care Med. 1997;1566:1752-1759
 
Antus B, Barta I, Kullmann T, et al. Assessment of exhaled breath condensate pH in exacerbations of asthma and chronic obstructive pulmonary disease: a longitudinal study. Am J Respir Crit Care Med. 2010;18212:1492-1497 [CrossRef]
 
Smith AG, Sheridan PA, Harp JB, Beck MA. Diet-induced obese mice have increased mortality and altered immune responses when infected with influenza virus. J Nutr. 2007;1375:1236-1243
 
Smith AG, Sheridan PA, Tseng RJ, Sheridan JF, Beck MA. Selective impairment in dendritic cell function and altered antigen-specific CD8+ T-cell responses in diet-induced obese mice infected with influenza virus. Immunology. 2009;1262:268-279 [CrossRef]
 
Chu YT, Chen WY, Wang TN, Tseng HI, Wu JR, Ko YC. Extreme BMI predicts higher asthma prevalence and is associated with lung function impairment in school-aged children. Pediatr Pulmonol. 2009;445:472-479 [CrossRef]
 
Lang JE, Feng H, Lima JJ. Body mass index-percentile and diagnostic accuracy of childhood asthma. J Asthma. 2009;463:291-299 [CrossRef]
 
Spathopoulos D, Paraskakis E, Trypsianis G, et al. The effect of obesity on pulmonary lung function of school aged children in Greece. Pediatr Pulmonol. 2009;443:273-280 [CrossRef]
 
Beckett WS, Jacobs DR Jr, Yu X, Iribarren C, Williams OD. Asthma is associated with weight gain in females but not males, independent of physical activity. Am J Respir Crit Care Med. 2001;16411:2045-2050
 
Hancox RJ, Milne BJ, Poulton R, et al. Sex differences in the relation between body mass index and asthma and atopy in a birth cohort. Am J Respir Crit Care Med. 2005;1715:440-445 [CrossRef]
 
Beuther DA, Sutherland ER. Overweight, obesity, and incident asthma: a meta-analysis of prospective epidemiologic studies. Am J Respir Crit Care Med. 2007;1757:661-666 [CrossRef]
 
Tantisira KG, Litonjua AA, Weiss ST, Fuhlbrigge AL. Childhood Asthma Management Program Research Group Childhood Asthma Management Program Research Group Association of body mass with pulmonary function in the Childhood Asthma Management Program (CAMP). Thorax. 2003;5812:1036-1041 [CrossRef]
 
National Asthma Education and Prevention ProgramNational Asthma Education and Prevention Program Expert Panel Report 3 (EPR-3): Guidelines for the diagnosis and management of asthma-summary report 2007. J Allergy Clin Immunol. 2007;120suppl 5:S94-S138 [CrossRef]
 
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