Predictors of asthma‐related quality of life in a large cohort of asthmatics: A cross‐sectional study in a secondary care center

Abstract Background In recent decades, asthma‐related quality of life questionnaires have joined objective clinical indicators as important outcome measures. In this study, we sought to investigate the predictors of asthma‐related quality of life in a large cohort of patients recruited from a secondary care center. Methods We conducted a cross‐sectional study on asthmatics (N = 1301) recruited from the Liège University Hospital asthma clinic (Belgium). After performing a descriptive analysis highlighting the distribution of scores from the Mini Asthma Quality of Life Questionnaire (Mini AQLQ) and its four dimensions (symptoms, activity limitation, emotional function, and environmental stimuli), we did multiple regression analysis to identify the independent predictors of AQLQ. Results Multiple regression beta analysis showed that AQLQ and its four dimensions were primarily associated with asthma control (p < 0.0001 in all instances). Female gender was associated with a lower score for the AQLQ's activity and environmental dimensions (p < 0.05 for both), while current smokers had a higher score on the AQLQ's environmental dimension (p < 0.0001). The burden of asthma treatment was associated with a lower score for the AQLQ's emotional (p < 0.05) and environmental (p < 0.05) dimensions. BMI was associated with a lower score in the AQLQ's activity dimension (p < 0.0001), while the opposite was true for the FeNO test (p < 0.0001). Sputum neutrophils were inversely related to the score for the AQLQ's symptom dimension (p < 0.05), whereas post‐bronchodilator FEV1 showed a positive relationship for that same dimension (p < 0.05). Conclusion Asthma control is the main predictor of AQLQ score and impacts all its dimensions, but demographic, functional, and airway inflammatory parameters may also influence some dimensions of the AQLQ.


| INTRODUCTION
Asthma is defined by the Global Initiative of Asthma (GINA) as a heterogeneous disease, usually characterized by chronic airway inflammation. It is diagnosed by a history of respiratory symptoms such as wheezing, shortness of breath, chest tightness, and cough that vary over time and in intensity, together with variable expiratory airflow limitation. 1 This disease is a growing burden in terms of morbidity, health care costs, and health-related quality of life (HRQL). 2 In recent decades, as a result of a paradigm shift towards patient-centered care, subjective measures of HRQL in asthma have become important outcomes alongside objective clinical outcomes. 3 International asthma treatment guidelines have therefore evolved to include improvement in patients' HRQL through long-term control of the disease, minimizing symptoms, and improving physical, psychological, and social functioning. 4 Unravelling the predictors of asthma-related quality of life is important for understanding the disease and its treatment, and should provide meaningful information about the impact of the disease and its treatment on patients' perceived health. 5 Many previous studies have explored factors associated with asthma-related quality of life. 6 They showed that the main factors were asthma control, 7-9 gender, [10][11][12][13] age, 9,13 body mass index (BMI), 12,14 education, 9 and sociodemographic parameters. 11,13 Some studies have also shown a weak univariate relationship between HRQL and lung function as measured by forced expiratory volume in 1 s (FEV 1 ). 13,15 As for the inflammatory component, some studies have explored the relationship between asthma-related quality of life and the fraction of exhaled nitric oxide (FeNO), with contrasting results. [16][17][18] To the best of our knowledge, no studies have explored the impact of granulocytic sputum cell count on asthma-related quality of life, although airway inflammation is included in the definition of asthma.
In this study we assessed the predictors of asthma-related quality of life in a large cohort of asthmatics who were well characterized with respect to demographic features, lung function, and systemic and airway inflammatory parameters.

| Study design, setting, and participants
This was a cross-sectional study on asthma patients recruited from the Liege University Hospital Asthma Clinic (Belgium) between 2003 and 2019. In accordance with the GINA, the asthma diagnosis was based on the presence of typical symptoms (wheezing, breathlessness, chest tightness, and cough) combined with an FEV 1 that increased at least 12% over baseline and was at least 200 ml after inhalation of 400 μg salbutamol and/or a provocative concentration of methacholine causing a 20% drop in FEV 1 ≤16 mg/ml. We excluded patients under the age of 18 years and those not clinically diagnosed with asthma who failed to meet the reversibility or bronchial hyperresponsiveness criteria. Then, from the patients with asthma, we selected those who completed two asthma-related patient-reported outcome measures (PROMs), namely the Asthma Control Test (ACT) 19 and the Mini Asthma Quality of Life Questionnaire (Mini AQLQ) 20 on their first visit to the asthma clinic. The sample size was 1301 asthmatics (Figure 1).

| Variables
All the variables used in this study were entered into the asthma clinic database during routine visits.

| Asthma-related quality of life (dependent variable)
Asthma-related quality of life was measured using the Mini Asthma Quality of Life Questionnaire (Mini AQLQ). This scientificallyvalidated questionnaire has been translated into more than 20 languages and is intended for adults with asthma. It includes 15 items divided into four different dimensions: symptoms (5 items), activity limitation (4 items), emotional function (3 items) and environmental stimuli (3 items). The 15 items are scored on a seven-point Likert scale; the score for the questionnaire as a whole and for the individual dimensions are simply averages of the responses to the questions within them. 20 Seven is the highest score in terms of asthma-related quality of life. The minimal clinically important difference-the smallest difference in a quality of life score that the patient perceives as clinically important 21 -is 0.5 for Mini AQLQ.

| Independent variables
We selected different variables from our database that might influence asthma-related quality of life. We gathered them into two main groups: patient demographic characteristics and disease characteristics.  19,22 Atopy was defined by one positive IgE test (>0.35 kU/L) to one or more common aeroallergens (grass pollen, tree pollen, cat, dog, molds, and house dust mite). Lung function testing was performed by spirometry (Spiro bank; MIR, Rome, Italy). A post-bronchodilator (reversibility) test was done for each patient, irrespective of their baseline FEV 1 and FEV 1 /forced vital capacity (FVC) ratio, as a standard procedure. The best of three consecutive spirometry readings was used, as recommended by the European Respiratory Society.
Patients were administered 400 μg of inhaled salbutamol via a metered-dose inhaler (Ventolin) and a spacer (Volumatic), one puff at a time into the spacer, and spirometry was performed again 15 min later. 23 Patients with baseline FEV 1 ≥ 70% predicted were given a methacholine challenge test, as previously described. 23 Using tidal breathing, the subjects inhaled successive quadrupling methacholine concentrations from 0.06 mg/ml to 16 mg/ml for one minute each;  Aerocrine, Solna, Sweden). Sputum induction and processing were performed as previously described. 24 The success rate of sputum induction and analysis in our asthma clinic was 78% (1013 of 1301 asthma patients). C-reactive protein (CRP), fibrinogen, blood eosinophils, and neutrophil counts were determined by routine laboratory analysis at Liège University Hospital.

| Statistical analysis
The normality of the distribution of the quantitative data was evaluated numerically by comparing mean and median and graphically using a histogram and quantile-quantile plot. The Shapiro-Wilk test for normality was used to complete this assessment. Quantitative variables were summarized accordingly using median and interquartile range (P25-P75), while counts and percentages were calculated for qualitative variables. The number and percentage of missing values were also reported. The associations between the quantitative variables and the AQLQ and its subscales (symptom dimension, activity dimension, emotional dimension, and environmental dimension) were first determined using the Spearman correlation coefficient. We further analyzed the determinants of AQLQ and its four subscales by multiple regression analysis. Based on the skewed and bounded-that is, values restricted to the interval between 1 and 7 -structure of the outcome variable, a beta regression model was considered. To do that, the outcome values were rescaled to the unit interval using the following transformation y^* = ([y (n-1)+0.5])/n where n is the sample size. 25 In this study, the best of the several models fitted was selected using the Akaike information criterion (AIC), with a lower AIC value indicating a better fit. All statistical modeling was done using R statistical software at a significance level of 0.05.

| Ethics
This study was approved by the Liège University Hospital ethics committee. Signed informed consent was obtained from patients as soon as they entered the asthma clinic. They agreed to allow their clinical data and the health outcomes they reported in the routine setting to be used for research purposes.

| Characteristics of the study population
The demographic characteristics of the study population are presented in Table 1. The majority of subjects were female (60%). Neversmokers represented 54% of our population, while ex-smokers and current smokers accounted for 26% and 20% of the population, respectively. Atopy was found in a slim majority of patients (54%).
The median BMI was 26 kg/m 2 , with an interquartile range (IQR) from 23-30, meaning that 25% of our population was obese. The vast majority of asthma patients were receiving medical treatment for asthma at the time of the visit (84%); the types of treatment are detailed in Table 1. Thirty-seven percent of patients (440/1195) reported an exacerbation in the 12 months prior to the visit.  (2) Abbreviations: BMI, Body Mass Index; ICS, Inhaled corticosteroids; IQR, interquartile range; LABA, long-acting beta agonists; LAMA, long-acting antimuscarinics; LTRA, leukotriene receptor antagonists; n, number of patients; OCS, oral corticosteroids; SABA, short acting beta agonists.

Variable Median (IQR)/Percentage (n) Percentage (number) of missing values
The median AQLQ score was 4.53 with a non-Gaussian distribution, as shown in Figure 2. Likewise, the distributions of the different AQLQ dimensions were nonparametric ( Figure 2). Asthma control, lung function, and inflammatory characteristics are shown in Table 2. For asthma control, the median ACT was 15 with an IQR of 11-20, indicating that 25% of the cohort had well-controlled asthma (ACT ≥ 20).

| Correlation between asthma-related quality of life and demographic and disease characteristics
The correlation between demographic and clinical parameters and the AQLQ variable and its four subscales are reported in Table 3.
ACT was strongly positively correlated with global AQLQ score (r = 0.81) and its four subscales. To a lesser extent, FEV 1 pre (%), FEV 1 post (%), FVC pre (%), and FVC post (%) were positively correlated with global AQLQ score and its four subscales. BMI was weakly and inversely correlated with global AQLQ score and its symptoms and activity subscales. All of the inflammatory parameters were inversely correlated with global AQLQ score except for FeNO and total serum IgE, which displayed no correlation. Those two inflammatory parameters did, however, show a significant relationship with two of the subscales: FeNO was positively associated with the activity dimension and total serum IgE was negatively associated with the emotional dimension. Among the inflammatory parameters, blood neutrophils exhibited the strongest relationships.

| Multivariate beta regression
The results of multivariate beta regression analysis are presented in Table 4. ACT was the only determinant displaying significant association with global AQLQ (p < 0.0001) and its four subscales (p < 0.0001).
None of the other independent variables was associated with global

| Relationship between asthma-related quality of life and asthma control
The level of asthma control as assessed by ACT was identified as the   activity limitation and environmental stimuli is perfectly in line with the study by Naleway et al. 10 Other studies reported that women with asthma experienced a lower HRQL 11,13,30,31 Some authors 30,32 believe that the relationship between gender and asthma-related quality of life is related to the higher prevalence of anxiety and depression among women compared to men. While our study found no gender differences in the AQLQ's emotional dimension, that dimension does not take anxiety and depression into account.
An earlier study reported that asthmatic smokers had a lower generic HRQL. 11 We were unable to confirm this finding when focusing solely on asthma-related quality of life. On the contrary, in the present study smokers had a higher asthma-related quality of life in the environmental domain than did non-smokers. Though this seems surprising, it can be explained by the composition and formulation of the AQLQ's environmental questions. Of the three questions that make up the AQLQ's environmental domain, one asks whether the patient has been bothered by cigarette smoke. It is very likely that most smokers responded "rarely or never" to this question.
It is worth noting, moreover, that the relationship between smoking and asthma-related quality of life is different than that between smoking and asthma control, where smoking has a clear detrimental effect on the level of control. 33 This illustrates the fact that although asthma control and asthma-related quality of life are strongly related, they reflect different dimensions of the disease.
Obesity is a major comorbidity in asthma, affecting up to 25%-50% of severe asthmatics, depending on the country. 34 In our study, BMI was negatively, though weakly, associated with the AQLQ activity limitation subscore. This means that the higher the asthma patient's BMI, the poorer his or her asthma-related quality of life in the activity dimension. This finding is consistent with another study by Lavoie et al. 14 and is readily understandable given the burden obesity places on everyday movement. It also demonstrates the importance of considering comorbidities when assessing asthmatics' quality of life.
Our study showed no association between either atopy-which was present in more than half of the asthma cohort-or total serum IgE and global asthma-related quality of life or any of its subscales. We therefore could not confirm that non-atopic asthma might have a greater negative impact of asthma-related quality of life, as found in a previous study. 35 In that study, over 70% of the cohort were atopic patients who were younger and had less severe disease; only half were being treated with ICS and the average FEV 1 was greater than 90% predicted. In our cohort, atopic patients had an average baseline FEV 1 of 84% predicted, and 59% were being treated with ICS/LABA (data not shown). We therefore believe that our population of atopic asthmatics had more severe disease than that of Ek et al. 35 Our data support the hypothesis that atopy is a prominent trait associated with asthma, but is not a determinant factor in worsening or improving quality of life.
Our data indicate that treatment burden has an impact on the emotional and environmental dimensions of asthma-related quality of life. Unlike patients who receive SABA alone as needed, the patients receiving maintenance treatment (ICS/LABA and/or LTRA and/ or LAMA)-and those receiving OCS, in particular-showed diminished quality of life. Presumably, the intensity of the pharmacological treatment makes the patients more aware of their fragility in anxiety-producing and stressful situations.

| Relationship between asthma-related quality of life and lung function
Confirming previous results, 8

| Relationship between asthma-related quality of life and inflammation
Airway inflammation is an essential component of asthma.  18 showed that AQLQ scores in patients with severe allergic asthma were lower in patients with FeNO ≥30 ppb than in patients with FeNO <30 ppb. None of these studies used multiple regression analysis, and FeNO may be affected by other factors likely to impact its effect on AQLQ in a univariate analysis, such as airway eosinophilia. We therefore believe that our statistical methods better reflect the true association between FeNO and quality of life. The reason why FeNO might positively affect the activity dimension of quality of life remains to be investigated, but it is worth noting that NO is a recognized mediator of vasodilation, 39 which is a physiological process of utmost importance in physical activity. While FeNO is usually considered to be the consequence of activation of inducible NO synthase due to local inflammation, 40 we cannot rule out the possibility that part of FeNO actually reflects an inherited propensity of the body to synthesize NO.

| Limitations of the study
The strength of this study lies in the size of the cohort, the use of validated PROMs (Mini AQLQ and ACT), and the inclusion of clinically well-characterized asthma patients; our study does, however, have several limitations. As this was a cross-sectional study, the cause and effect of these associations cannot be determined. Another limitation is the lack of sociodemographic data such as the occupation or education level, which are likely to influence asthma-related quality of life. 3,8 The strength of the relationship between asthma control and asthma-related quality of life might have been weaker, if asthma-related quality of life had been assessed by a different questionnaire that considered other quality-of-life dimensions. 41 Finally, our analysis did not include several comorbidities-such as rhinosinusitis or gastroesophageal reflux-known to impact asthma patients' quality of life. 35,42,43

| CONCLUSION
Asthma control is the main predictor of asthma-related quality of life and impacts all of its dimensions, but demographic, functional, and airway inflammatory parameters may also influence some aspects of asthma-related quality of life. Among the airway inflammatory parameters, FeNO emerged as the inflammatory factor most significantly associated with AQLQ, and surprisingly the relationship between FENO and the activity dimension was positive. Whatever the reason for this, our study shows that considering patient-reported outcomes might refine clinicians' views on an objective physiological parameter. The study also received support from a federal grant from the Belgian Government (EOS 0013618F).