Cluster Analysis on Longitudinal Data of Patients With Adult-Onset Asthma
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Original Article Cluster Analysis on Longitudinal Data of Patients with Adult-Onset Asthma Pinja Ilmarinen, PhD a , Leena E. Tuomisto, MD, PhD a , Onni Niemelä, MD, PhD b,c , Minna Tommola, MD a , Jussi Haanpää, MSc d , and Hannu Kankaanranta, MD, PhD a,e Seinäjoki and Tampere, Finland What is already known about this topic? Many phenotypes of asthma have been identified in previous cluster analyses, mostly on the basis of cross-sectional data with limited inclusion of patients. Some studies have provided short-term 1to 3-year prognosis for the phenotypes. What does this article add to our knowledge? This is the first study that reports long-term 12-year prognosis for clusters of adult-onset asthma starting from diagnosis. We report different disease prognoses for smoking, obesity-related, female, atopic, and nonrhinitic asthma. How does this study impact current management guidelines? Information on long-term outcome of asthma can be used to inform and motivate patients. We show the poorest outcome and the most unmet needs in the therapy of smoking and obesity-related asthma, suggesting need for special guidance. BACKGROUND: Previous cluster analyses on asthma are based on cross-sectional data. OBJECTIVE: To identify phenotypes of adult-onset asthma by using data from baseline (diagnostic) and 12-year follow-up visits. METHODS: The Seinäjoki Adult Asthma Study is a 12-year follow-up study of patients with new-onset adult asthma. K-means cluster analysis was performed by using variables from baseline and follow-up visits on 171 patients to identify phenotypes. RESULTS: Five clusters were identified. Patients in cluster 1(n[38) were predominantly nonatopic males with moderate smoking history at baseline. At follow-up, 40% of these patients had developed persistent obstruction but the number of patients with uncontrolled asthma (5%) and rhinitis (10%) was the lowest. Cluster 2 (n [19) was characterized by older men with heavy smoking history, poor lung function, and persistent obstruction at baseline. At follow-up, these patients were mostly uncontrolled (84%) despite daily use of inhaled corticosteroid (ICS) with add-on therapy. Cluster 3 (n [50) consisted mostly of nonsmoking females with good lung function at diagnosis/ follow-up and well-controlled/partially controlled asthma at follow-up. Cluster 4 (n [25) had obese and symptomatic patients at baseline/follow-up. At follow-up, these patients had several comorbidities (40% psychiatric disease) and were treated daily with ICS and add-on therapy. Patients in cluster 5(n[39) were mostly atopic and had the earliest onset of asthma, the highest blood eosinophils, and FEV 1 reversibility at diagnosis. At follow-up, these patients used the lowest ICS dose but 56% were well controlled. CONCLUSIONS: Results can be used to predict outcomes of patients with adult-onset asthma and to aid in development of a Department of Respiratory Medicine, Seinäjoki Central Hospital, Seinäjoki, Finland b Department of Laboratory Medicine, Seinäjoki Central Hospital, Seinäjoki, Finland c University of Tampere, Tampere, Finland d Department of Clinical Physiology, Seinäjoki Central Hospital, Seinäjoki, Finland e Department of Respiratory Medicine, University of Tampere, Tampere, Finland This study was supported by the Finnish Anti-Tuberculosis Association Foundation (Helsinki, Finland), the Tampere Tuberculosis Foundation (Tampere, Finland), the Jalmari and Rauha Ahokas Foundation (Helsinki, Finland), the Research Foundation of the Pulmonary Diseases (Helsinki, Finland), the Competitive State Research Financing of the Expert Responsibility Area of Tampere University Hospital (Tampere, Finland), and the Medical Research Fund of Seinäjoki Central Hospital (Seinäjoki, Finland). None of the sponsors had any involvement in the planning and execution of this study or in the writing of this article. Conflicts of interest: P. Ilmarinen has received lecture fees from MundiPharma. L. E. Tuomisto has received lecture fees from Mundipharma and has received travel support from Takeda, Chiesi, and Orion. M. Tommola has received lecture fees from AstraZeneca and GlaxoSmithKline. H. Kankaanranta has received lecture and consultancy fees and travel support from Almirall, AstraZeneca, and Boehringer Ingelheim; has received consultancy fees from Chiesi Pharma AB; has received lecture and consultancy fees from GlaxoSmithKline, Leiras-Takesa, and Novartis; has received lecture fees from MSD, Mundipharma, Medith, Resmed Finland, and Orion Pharma; has received travel support from Intermune; and has received consultancy fees from Roche. The rest of the authors declare that they have no relevant conflicts of interest. Received for publication October 25, 2016; revised January 3, 2017; accepted for publication January 31, 2017. Available online April 25, 2017. Corresponding author: Pinja Ilmarinen, PhD, Department of Respiratory Medicine, Seinäjoki Central Hospital, Hanneksenrinne 7, Seinäjoki 60220, Finland. E-mail: pinja.ilmarinen@epshp.fi. 2213-2198 Ó2017 The Authors. Published by Elsevier Inc. on behalf of the American Academy of Allergy, Asthma & Immunology. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). http://dx.doi.org/10.1016/j.jaip.2017.01.027 967
Abbreviations used ACOSAsthma-COPD overlap syndrome ACTAsthma control test AQ20Airways Questionnaire 20 BDBronchodilator COPDChronic obstructive pulmonary disease FVCForced vital capacity ICSInhaled corticosteroid Max 0-2.5 - Maximum lung function during the first 2.5 years after diagnosis (and start of anti-inflammatory therapy) SAASSeinäjoki Adult Asthma Study personalized therapy (NCT02733016 at ClinicalTrials. gov). Ó2017 The Authors. Published by Elsevier Inc. on behalf of the American Academy of Allergy, Asthma & Immunology. This is an open access article under the CC BYNC-ND license (http://creativecommons.org/licenses/by-ncnd/4.0/). (J Allergy Clin Immunol Pract 2017;5:967-78) Key words: Asthma; Adult-onset; Late-onset; Cluster analysis; Phenotypes; Follow-up; Longitudinal; Smoking; Obesity; Early-onset Adultor late-onset asthma has been suggested to be a distinctive phenotype of asthma. 1,2 Patients with adult-onset asthma have lesser allergic processes, lower lung function despite shorter duration of disease, and more often a pronounced eosinophilic inflammation without evidence of T H 2eassociated inflammation when compared with patients with childhoodonset asthma. 1 These findings suggest that adult-onset asthma is more heterogeneous when compared with childhood-onset asthma. In previous studies, subphenotypes of adult-onset asthma such as eosinophil-predominant, mild to moderate well-controlled, obesity-related, smoking, and severe obstructive asthma have been proposed. 3,4 To identify phenotypes of asthma, unsupervised hierarchical cluster analyses have been carried out. However, the cluster analyses have mostly been based on cross-sectional data on patients with mixed duration of asthma. 2,4-6 Asthma is known as a disease with a high degree of variability, making one time point a fragile basis for cluster analysis. Furthermore, no information on the diagnostic phase has been included in the previous analyses. In addition, many previous analyses have clustered patients with severe asthma, 7,8 leaving milder forms with less attention. Some studies have involved short follow-ups (1-3 years). 6,7,9,10 In a previous prospective longitudinal analysis of severe asthma, the clusters did not show cluster-specific disease courses regarding outcome of asthma, suggesting a potential limitation in the way of performing current cluster analyses. 9 In addition to the natural disease variability, many factors such as therapy, lifestyle, and comorbidities may modify the disease course. Reliability of the results of a cluster analysis would be increased by including clinical data from several time points of the disease follow-up into the analysis. Here, we used a long-term follow-up approach to construct phenotypes of adult-onset asthma by carrying out a cluster analysis with inclusion of variables from diagnosis to a 12-year follow-up visit. This approach provides novel insights into the phenotypes of asthma with prognostic significance. METHODS Patients and study design The present study was part of the Seinäjoki Adult Asthma Study (SAAS), which is a prospective, single-center (Seinäjoki Central Hospital, Seinäjoki, Finland), 12-year follow-up study of a cohort of consecutive white patients having new-onset asthma diagnosed at adult age (15 years). SAAS has been registered on ClinicalTrials.gov with ID NCT02733016. Institutional permissions (TU1114 and LET) were obtained and the participants gave written informed consent to the study protocol approved by the Ethics Committee of Tampere University Hospital, Tampere, Finland (R12122). The protocol, inclusion and exclusion criteria, and the background data of SAAS have been published elsewhere. 11 Briefly, asthma was diagnosed by a specialized respiratory physician during the period 1999 to 2002 on the basis of typical clinical symptoms and confirmed by objective lung function measurements. 11,12 The main diagnostic features of asthma in each cluster are presented in Table E1 in this article’s Online Repository at www.jaci-inpractice.org. Smokers and patients with comorbidities were not excluded. After diagnosis, the patients were treated and monitored in specialized or primary care as required. The total cohort consisted of 257 patients and 203 patients completed the follow-up visit (mean follow-up time, 12.2 years; range, 10.8-13.9 years). At 12-year follow-up visit, asthma status and disease control, comorbidities, and medication were evaluated using structured questionnaires and lung function was measured. Data on asthma-related visits to health care and hospitalizations were also collected from primary care, occupational health care, private clinics, and hospitals. After excluding those with missing data, 171 patients with adult-onset asthma remained in the cohort for cluster analysis (Figure 1). Lung function, comorbidities, inflammatory parameters, and other clinical measurements Lung function was measured with a spirometer according to international recommendations. 13 The following were the lung function measurement points: (1) baseline (time of asthma diagnosis), (2) the maximum prebronchodilator FEV 1 (Pre-BD FEV 1 ) during the first 2.5 years after diagnosis (Max 0-2.5 ) (and after start of anti-inflammatory therapy), and (3) 12-year follow-up. 14 Detailed information on determination of lung function, inflammatory parameters, and comorbidities can be found in this article’s Online Repository at www.jaci-inpractice.org. Asthma control was assessed according to the Global Initiative for Asthma 2010 report. 15 Patients filled out the Airways Questionnaire 20 (AQ20) at baseline visit and AQ20 and asthma control test (ACT) questionnaires at the followup visit. The AQ20 is a short and simple well-validated questionnaire to measure and quantify disturbances in the airway-specific quality of life. 16 ACT is a widely used patient self-administered tool for identifying those with poorly controlled asthma. 17 Variable selection Input variables for the cluster analysis were selected on the basis of factor analysis (see Table E2 in this article’s Online Repository at www.jaci-inpractice.org). Basic and clinical variables included in factor analysis were chosen to cover as wide a range as possible from diagnosis to 12-year follow-up visit and are further discussed in this article’s Online Repository at www.jaci-inpractice.org. Cluster analysis and discriminant analysis Cluster analysis was carried out by using a 2-step process. First, Ward hierarchical cluster analysis was performed for preevaluation of J ALLERGY CLIN IMMUNOL PRACT JULY/AUGUST 2017 968 ILMARINEN ET AL
the number of clusters. Then, K-means analysis was carried out by using the prespecified number of clusters (5). Stepwise discriminant analysis was performed to identify variables discriminating between the prespecified clusters. Statistically significant results were expected for most of the comparisons because the objective of the cluster analysis was to differentiate the participants into distinct phenotypes of adult-onset asthma. Other statistical analyses Continuous data are expressed as mean SD or median and interquartile range. Group comparisons were performed by 1-way analysis of variance with the Tukey post hoc test, the KruskalWallis test, or the chi-square test. Statistical analyses were performed by using SPSS software, version 22 (IBM Corporation, Armonk, NY) and MATLAB, version 8.6 (Mathworks, Natick, Mass). RESULTS Patients’characteristics Characteristics of the total cohort at baseline and follow-up are presented in Table E3 in this article’s Online Repository at www. jaci-inpractice.org. Patients were mostly females (58.5%) and nonatopic (63.5%), with age at asthma onset ranging from 15 to 77 years. At diagnosis, most patients were steroid-naive. At the 12-year follow-up point, 78.9% were daily users of inhaled corticosteroid (ICS) and 50.9% were daily users of ICS and add-on medication. Cluster analysis By performing Ward hierarchical and K-means cluster analyses, 5 clusters were identified. The basic characteristics of these clusters are shown in Figure 2. Cluster 1 was characterized by low prevalence (10.5%) of rhinitis (nonrhinitic asthma), whereas cluster 2 had the highest smoking history (smoking asthma). Cluster 3 consisted mainly (98%) of women (female asthma), and most patients in cluster 4 were obese from diagnosis to 12-year follow-up visit (obesity-related asthma). Cluster 5 mostly included atopic patients with the earliest onset of asthma (earlyonset atopic adult asthma). Basic and clinical characteristics of the clusters at baseline and at follow-up are presented in Tables I-IV. There were no major differences in the main diagnostic features between groups (see Table E1 in this article’s Online Repository at www.jaci-inpractice.org). Cluster 1: Nonrhinitic asthma. Cluster 1 (n ¼38 [22.2%]) was characterized by lack of rhinitis, male predominance (60.5%), onset of asthma at middle age, and second highest smoking history (Figure 2). Proportion of patients with permanent bronchial obstruction (post-BD FEV 1 /forced vital capacity [FVC] <0.7) increased from 10.8% to 39.5% from diagnosis to 12-year follow-up visit. This cluster also showed the highest weight gain, with the proportion of obese patients increasing from 18.4% to 39.5%. Asthma was uncontrolled in only 5.3% of the patients, even though most (55.9%) were treated with low-dose ICS or no daily ICS (Table I). Cluster 1 showed moderate loss of FEV 1 during the follow-up and the lowest use of health care (Figures 3 and 4). Patients had 1 comorbidity on average at the 12-year follow-up visit, the most prevalent being chronic obstructive pulmonary disease (COPD) and hypertension (Table IV). Cluster 2: Smoking asthma. Cluster 2 was the smallest cluster (n ¼19 [11.1%]) and was predominated by older males. Almost 80% of patients had smoking history and 44.4% showed bronchial obstruction at diagnosis and could be characterized as having asthma-COPD overlap syndrome (ACOS). 18 However, the smoking cluster did not differ from other clusters on the basis of diffusing capacity. The patients had poor lung function, high symptoms, and uncontrolled asthma despite 94.7% being under daily ICS therapy, 73.4% with moderate-to high-dose ICS, and 78.9% with long-acting b 2 agonist at follow-up. Even though lung function significantly improved after start of therapy, the annual decline in FEV 1 was steep (78 mL on average) from the maximum point of lung function to the 12-year follow-up visit (Figure 4;Table II). This was the only group with no decrease in blood eosinophils or symptoms from diagnosis to follow-up (Figure 4). The patients had 3 comorbidities on average (Table IV) and frequent health care use (Figure 3). Of the patients, 36.8% had been hospitalized for asthma (Figure 3,B) and this cluster accounted for 35.8% of all hospital treatment periods. Cluster 3: Female asthma. This group was the largest (n ¼ 50 [29.2%]) and consisted of women with a wide range in the age of asthma onset. Cluster 3 contained more (44%) patients with normal weight (body mass index <25) compared with other clusters and showed the lowest smoking history. Forty percent reported being symptomatic already during childhood even though they were not diagnosed as having asthma. Pre-BD FEV 1 was normal (>80% predicted) in 78% at diagnosis and in 90% at follow-up, and the annual decline in FEV 1 was the lowest (31 mL). Blood eosinophils were the second highest and the AQ20 symptom score was at a rather moderate level of 6 out of 20 at diagnosis, and both reduced during the follow-up (Figure 4). Even though lung function measured by spirometry and inflammation were within normal range and 78% were undergoing ICS treatment at the 12-year follow-up visit, 64% of the patients were partially controlled or uncontrolled and health care use was relatively high (Table I;Figure 3). Female cluster accounted for 29.0% of all asthma-related visits. Cluster 4: Obesity-related asthma. This cluster (n ¼25 [14.6%]) mostly contained nonatopic females with the oldest age Years 1999-2002 Baseline visit Patients with new diagnosis of asthma (age ≥ 15 years) were included n = 260 Patients excluded n = 32 missing information on input variables of cluster analysis Years 2012-2013 12-year follow-up visit n = 203 Res p onse rate=79 % Patients excluded n = 1 consent withdrawn n = 2 childhood asthma Patients lost during follow-up n = 22 dead n = 9 could not be reached n = 5 significant comorbidities n = 18 other reasons Final study population n = 171 Visits to healthcare Hospitalizations FIGURE 1. Flow chart of the study. J ALLERGY CLIN IMMUNOL PRACT VOLUME 5, NUMBER 4 ILMARINEN ET AL 969
of asthma onset. On average, patients were obese from diagnosis to the 12-year follow-up point without gaining more weight during this time. The patients were multimorbid at follow-up (Table IV), with the most prevalent comorbidities being hypertension, diabetes, and psychiatric diseases. Asthma was uncontrolled in 48% of the patients at follow-up even though 92% were undergoing ICS treatment, 50% used high-dose ICS, and 72% were on add-on medication (Table I). At diagnosis, 44% showed pre-BD FEV 1 of more than 80% predicted. Lung function improved on start of therapy and remained relatively stable throughout follow-up (Figure 4,A). Respiratory symptoms, use of oral steroids, and health care use were all at high levels (Figures 3 and 4). Cluster 5: Early-onset atopic adult asthma. This group was the second largest (n ¼39 [22.8%]) and consisted of the youngest patients with mean age of asthma onset at 33 11 years. Almost half had suffered from respiratory symptoms during childhood, 59% were atopic, and 90% had nonallergic or allergic rhinitis (Table I). Of this cluster, 59% showed preBD FEV 1 of more than 80% at diagnosis and 84% could be reversed to FEV 1 of more than 80% predicted by BD. Reversibility was in general the highest. Lung function initially showed a good response to steroids, even though the loss of lung function after the maximum point was also the second steepest. At diagnosis, these patients showed the highest blood eosinophils (Table III), which reduced until the 12-year followup visit (Figure 4). Asthma was controlled in 56% of the subjects and use of medication was the lowest, because 56% were using low-dose ICS or no medication and only 17.9% were treated by long-acting b 2 agonist (Table I). Use of steroid bursts was infrequent and use of health care was among the lowest (Figure 3). Validation For validation, we carried out K-means algorithm 10 times by the leave-one-out method to ensure stability and repeatability of the model. This method showed 94.4% repeatability. Discriminant analysis By using a stepwise method of discriminant analysis, 12 out of 17 variables were found to significantly discriminate between the clusters: the diagnostic variables were post-BD FEV 1 /FVC, FEV 1 reversibility, and maximal change in FEV 1 (from diagnosis to Max 0-2.5 ), whereas the follow-up variables included rhinitis, number of drugs in use to treat comorbidities, pre-BD FEV 1 , pack-years, body mass index, limitation of activities (none/any), and basic variables of sex, age at asthma onset, and symptoms of asthma for less than 16 years, of which rhinitis, post-BD FEV 1 / FVC, number of drugs in use to treat comorbidities, and sex were found to be the strongest discriminating variables. Duration of symptoms before diagnosis, blood eosinophils and neutrophils, reversibility at follow-up, and ACT score were not found as statistically significant discriminants. The percentage of correct classification on the basis of the 12 discriminating variables was 94.7% (data not shown). DISCUSSION In this study, we identified phenotypes of adult-onset asthma by using longitudinal data and basic and clinical variables ranging from the diagnostic phase to the 12-year follow-up visit. Our cohort included smokers and patients with comorbidities. The following 5 phenotypes were identified: (1) nonrhinitic controlled to partially controlled asthma with low use of medication and health care; (2) smoking asthma or ACOS with poor lung function, high symptoms, and high use of medication and health care; (3) female asthma with normal clinical parameters FIGURE 2. Basic characteristics of clusters. In A-F, C1 to C5 refer to the cluster numbers. Overall P values are shown. In Band F, the red lines shown are means. In D, means are shown. BMI, Body mass index; DG, diagnosis. J ALLERGY CLIN IMMUNOL PRACT JULY/AUGUST 2017 970 ILMARINEN ET AL
TABLE I. General features of clusters Features Cluster 1: nonrhinitic (n [38) Cluster 2: smoking (n [19) Cluster 3: female (n [50) Cluster 4: obese (n [25) Cluster 5: atopic (n [39) Pvalue between clusters Pvalue between clusters Baseline Follow-up Baseline Follow-up Baseline Follow-up Baseline Follow-up Baseline Follow-up Baseline Follow-up Demographic characteristics and anthopometrics Females, n (%) 15 (39.5) 2 (10.5)*49 (98)†16 (64) 18 (46.2) <.001 Age (y), mean SD 50 12 63 12 55 96694312†55 12†57 86983311†45 11†<.001 <.001 BMI (kg/m 2 ), mean SD 28.1 5.5z29.9 6.5 27.8 4.0 28.1 4.9 26.3 4.3 27.1 4.8 32.7 5.2†32.8 5.3zxjj 24.9 3.5 26.8 3.9 <.001 <.001 Obese (BMI >30), n (%) 7 (18.4) 15 (39.5) 8 (42.1)z7 (36.8) 7 (14.0) 13 (26.0) 17 (68.0)zjj{ 18 (72.0)†4 (10.3) 9 (23.1) <.001 .001 Smokers, n (%) 20 (52.6) 20 (52.6) 15 (78.9)jj 15 (78.9)jj 17 (34) 18 (36) 11 (44) 11 (44) 19 (48.7) 21 (53.8) .020 .027 Current smoker, n (%) 7 (18.4) 7 (18.4) 5 (26.3) 3 (15.8) 5 (10) 8 (16) 4 (16) 0 9 (23.1) 8 (20.5) .425 .225 Pack-years of smokers, median (IQR) 17 (12-23)jj 19 (15-29)zjj 29 (14-34)*zjj 33 (15-38)zjj 5 (2-10) 6 (2-18) 15 (10-20) 15 (10-25) 4 (3-7) 6 (3-15) <.001 <.001 Symptoms of asthma <16 y, n (%) 0 1 (5.3) 20 (40.0)*{1 (4.0) 19 (48.7)*x{ <.001 Symptoms of asthma before diagnosis (mo), median (IQR) 18 (9-60) 24 (11-36) 12 (9-36) 24 (24-60)zjj 12 (8-24) .008 Atopic, n (%) 9 (27.3) ND 5 (31.3) ND 19 (40.4) ND 2 (9.1) ND 22 (57.9)*ND .003 ND No. of positive SPT, median (IQR) 0 (0-1) ND 0 (0-2) ND 0 (0-2)*ND 0 (0-0) ND 1.5 (0-3)*x{ ND .001 ND Rhinitis, n (%) ND 4 (10.5)†ND 14 (73.7) ND 44 (88) ND 24 (96) ND 35 (89.7) ND <.001 Asthma control and quality of life AQ20 score, median (IQR) 5 (3-7) 2 (1-4) 8 (5-10) 8 (5-11)zjj{ 6 (4-10) 4 (2-6) 10 (7-13)zjj{ 7 (4-9)z{ 4 (2-9) 2 (1-5) <.001 <.001 ACT score, median (IQR) ND 23 (21-24) ND 20 (13-21)zjj{ ND 22 (19-24) ND 19 (15-22)z{ ND 23 (21-25) ND <.001 ACT score <20, n (%) ND 5 (13.2) ND 9 (47.4)z{ ND 13 (26.0) ND 15 (60.0)zjj{ ND 5 (12.8) ND <.001 Controlled, n (%) ND 16 (42.1) ND 2 (10.5) ND 18 (36) ND 4 (16) ND 22 (56.4)*xND <.001 Partly controlled, n (%) ND 20 (52.6)xND 1 (5.3) ND 21 (42)xND 9 (36) ND 11 (28.2) ND .007 Uncontrolled, n (%) ND 2 (5.3) ND 16 (84.2)zjj{ ND 11 (22){ND 12 (48)z{ ND 6 (15.4) ND <.001 Exacerbations Oral steroids, n (%)#2 (5.4) 4 (21.1) 6 (12.2) 7 (29.2) 2 (5.1) .026 Treatment Daily ICS user, n (%)** 1 (2.6) 27 (71.1) 1 (5.3) 18 (94.7) 6 (12.2) 39 (78) 2 (8.0) 23 (92) 2 (5.1) 28 (71.8) .479 .089 ICS dose,†† median (IQR) 900 (800-1600) 800 (400-1000) 800 (700-1600) 900 (700-1400) 800 (400-1000) 800 (575-1000) 1000 (800-1400) 1000 (475-1525) 800 (800-1600) 800 (400-800) .220 .163 Low/none ICS dose, n (%) ND 19 (55.9) ND 4 (26.7) ND 18 (40) ND 6 (30) ND 20 (55.6) ND .109 Medium ICS dose, n (%) ND 7 (20.6) ND 4 (26.7) ND 11 (24.4) ND 4 (20) ND 11 (30.6) ND .871 (continued) J ALLERGY CLIN IMMUNOL PRACT VOLUME 5, NUMBER 4 ILMARINEN ET AL 971
but relatively high use of health care; (4) obesity-related asthma with comorbidities, high symptoms, and high use of medication and health care; and (5) atopic well-controlled asthma with onset earlier in adulthood. Instead of characterizing phenotypes in one point of disease, we provide phenotypes based on 12-year follow-up data. Our results show both similarities to and differences from those of previous cluster analyses based on cross-sectional data with mixed duration of asthma. Most previous analyses have excluded smokers or heavy smokers and only few have identified smoking asthma. 6,19 Cluster A in the Cohort for Reality and Evolution of Adult Asthma in Korea (COREA) study 6 resembled our smoking cluster in many respects. However, in our study, the smoking cluster showed an annual decline in FEV 1 that was the steepest of all groups in contrast to the results of the COREA smoking cluster in 1-year follow-up. Many negative outcomes previously associated with smoking asthma were evident in our smoking cluster, including lower asthma-related quality of life (based on AQ20 score), frequent health care use, and severe/uncontrolled asthma. 20-22 Even though the patients in the smoking cluster typically show irreversible airflow limitation and comorbidity profiles resembling that of COPD, normal average diffusing capacity in each cluster suggests that the main diagnosis is asthma and not emphysema. In addition, the diagnosis of asthma was made by a respiratory specialist and each patient fulfilled the diagnostic criteria of asthma including objective lung function measurements showing bronchial variability. This smoking group, although being the smallest one, was responsible for more than a third of all asthma-related hospitalizations, highlighting the significance of this group to health care costs. Obviously, much efforts should be focused on advising patients to stop smoking as early as possible, even before onset of asthma and before asthma turns from a milder form to difficult-to-treat smoking asthma with high burden for both individual and health care. Obesity-related female-predominant asthma is a cluster identified in our study as well as in some previous studies, 2,5,7,10 and our results add by providing data on prognosis of the long-term obesity. Our results on this cluster support previous findings indicating frequent symptoms and exacerbations, high use of health care, high medication, and a nonatopic, noneosinophilic disease characteristic. This cluster is also prone to create a high burden to health care, as evidenced by the most frequent use of oral corticosteroids and of health care services. To further add on previous studies, we found the highest number of comorbidities in the obese cluster and especially a high prevalence of psychiatric comorbidity (40%). Consistently, in a previous study, the highest depression score was shown in the late-onset obese cluster in a cohort of severe asthma. 7 However, interaction between obesity, psychiatric diseases, and asthma requires further studies. 23,24 Recently, we showed that multimorbidity is associated with increased symptoms of asthma, which may be partly related to systemic inflammation in these patients. 25 Systemic inflammation has been associated with high steroid dose in the treatment of adult-onset asthma. 25 Therefore, multimorbidity and systemic inflammation may be relevant in turning asthma into symptomatic and steroid-resistant in obese patients. However, weight loss has resulted in improved symptoms, lung function, asthma control, and health status, 23 suggesting that it would benefit this subgroup. In addition to the obesity-related female-predominant group, we identified a nonobese cluster of females with good lung TABLE I. (Continued) Features Cluster 1: nonrhinitic (n [38) Cluster 2: smoking (n [19) Cluster 3: female (n [50) Cluster 4: obese (n [25) Cluster 5: atopic (n [39) Pvalue between clusters Pvalue between clusters Baseline Follow-up Baseline Follow-up Baseline Follow-up Baseline Follow-up Baseline Follow-up Baseline Follow-up High ICS dose, n (%) ND 8 (23.5) ND 7 (46.7) ND 16 (35.6) ND 10 (50)zND 5 (13.9) ND .022 LABA, n (%) ND 16 (42.1) ND 15 (78.9) ND 25 (50) ND 18 (72) ND 7 (17.9) ND <.001 LTRA, n (%) ND 5 (13.2) ND 1 (5.3) ND 8 (16) ND 11 (44)zjj ND 2 (5.1) ND <.001 Theophylline, n (%) ND 0 ND 0 ND 2 (4) ND 1 (4) ND 0 ND .419 LAMA, n (%) ND 0 ND 5 (26.3)zjj{ ND 0 ND 2 (8) ND 0 ND <.001 BMI, Body mass index; LABA, long-acting b 2 agonist; LTRA, leukotriene receptor antagonist; ND, not determined; SPT, skin prick test. *P<.05 vs cluster 4 at corresponding time point (baseline or follow-up). †P<.05 to all other clusters at corresponding time point (baseline or follow-up). zP<.05 vs cluster 5 at corresponding time point (baseline or follow-up). {P<.05 vs cluster 1 at corresponding time point (baseline or follow-up). xP<.05 vs cluster 2 at corresponding time point (baseline or follow-up). jjP<.05 vs cluster 3 at corresponding time point (baseline or follow-up). Paired comparison between baseline and follow-up is not shown. #At least 2 courses of oral steroids during the 2 previous years before the follow-up visit. **Baseline ICS users refer to those who used ICS daily before diagnosis. ††Baseline ICS dose is the starting dose at diagnosis. Low-dose ICS refers to 400 m g, medium-dose ICS to >400e800 m g, and high-dose ICS to >800 m g budesonide equivalents. 15 J ALLERGY CLIN IMMUNOL PRACT JULY/AUGUST 2017 972 ILMARINEN ET AL
TABLE II. Lung function of clusters (mean SD) Characteristic Cluster 1: nonrhinitic (n [38) Cluster 2: smoking (n [19) Cluster 3: female (n [50) Cluster 4: obese (n [25) Cluster 5: atopic (n [39) Pvalue between clusters Pvalue between clusters Baseline Follow-up Baseline Follow-up Baseline Follow-up Baseline Follow-up Baseline Follow-up Baseline Follow-up Lung function Pre-BD FEV 1 , %ref 82 15 85 14*53 18†63 19†90 12 96 13 78 13*79 16*81 14*86 12*<.001 <.001 Post-BD FEV 1 ,%ref 8616 89 14*60 18†67 20†94 13 98 13 83 14*81 16*z92 12 92 12 <.001 <.001 Pre-BD FVC, %ref 90 16 97 14 73 17*zx 90 15*95 12 103 14 84 13*88 14*93 15 98 14 <.001 <.001 Post-BD FVC, %ref 92 16 100 16 81 16*z93 14 96 13 103 14 86 12*z90 14*97 13 99 14 <.001 .007 Pre-BD FEV 1 /FVC, %ref 0.75 0.08*0.71 0.08*0.57 0.11†0.57 13†0.80 0.07 0.76 0.05 0.76 0.06 0.72 0.10 0.74 0.10*0.72 0.07 <.001 <.001 Post-BD FEV 1 /FVC, %ref 0.77 0.07*0.72 0.08*0.58 0.12†0.58 0.14†0.83 0.07 0.78 0.05 0.78 0.08 0.72 0.10*0.81 0.08 0.76 0.07 <.001 <.001 FEV 1 reversibility (mL) 166 137 134 147*267 251 142 172*139 142 47 69 144 211 62 104 407 204*xjj 195143*xjj <.001 <.001 FEV 1 reversibility (% change) 5.6 5.3 4.8 5.2*15.0 16.4*zxjj 6.8 7.7*5.9 7.8 1.8 2.8 6.2 8.8 3.2 5.3 14.2 13.6*xjj 6.6 5.6*<.001 <.001 Diffusing capacity DL CO /VA (%ref) 104 17 100 15 94 21 88 28 100 20 95 14 99 18 93 13 107 16 97 14 .155 .107 Annual change in lung function from Max 0-2.5 to follow-up{ D FEV 1 (mL/y) 49 32*78 54*31 24 46 30 59 40*<.001 D FVC (mL/y) 37 32 48 63 24 31 40 39 38 49 .173 Maximal change in FEV 1 ( D from diagnosis to Max 0-2.5 ) D FEV 1 (mL#) 266 365 972 899*zxjj 161 252 184 239 561 459*xjj <.001 DL CO , Diffusing capacity of the lung for carbon monoxide; ref, reference; VA, alveolar volume. *P<.05 vs cluster 3 at corresponding time point (baseline or follow-up). †P<.05 to all other clusters at corresponding time point (baseline or follow-up). zP<.05 vs cluster 5 at corresponding time point (baseline or follow-up). xP<.05 vs cluster 1 at corresponding time point (baseline or follow-up). jjP<.05 vs cluster 4 at corresponding time point (baseline or follow-up). Paired comparison between baseline and follow-up is not shown. {Annual change in FEV 1 or FVC from point of maximal lung function within 2.5 y after start of therapy to the 12-y follow-up visit. # D FEV 1 when deducting pre-BD FEV 1 value at diagnosis from pre-BD FEV 1 value at point of maximal lung function within 2.5 y from start of therapy; reflects early response to treatment. J ALLERGY CLIN IMMUNOL PRACT VOLUME 5, NUMBER 4 ILMARINEN ET AL 973
function and a wide range in the age of asthma onset. The previously defined clusters “least severe asthma with normal lung function,”“middle-age onset, female-dominant,”and “late-onset mild asthma” 6,7,19 show resemblance to our female cluster, including predominance of nonobese females, normal lung function, 6,7,19 and stable lung function in the short followups. 6,7 Low smoking history, low body mass index, low count of comorbidities, and high eosinophils at diagnosis may have affected the good overall prognosis of the female cluster. However, the health care use was relatively high in this group given that many clinical parameters were within normal range. This may be explained by females’stronger perception of symptoms and lower threshold to contact health care when compared with males. 26 In addition, female patients with similar severity of asthma as the corresponding male patients have shown better lung function but worse asthma-related quality of life. 26 In this cluster, lung function based on spirometry was more stable when compared with lung function in other groups, but peak expiratory flow follow-up might have shown variable obstruction and disease activity. In addition, whether parameters such as blood eosinophils or airway hyperresponsiveness better correlate to disease activity and symptoms remains unclear. In the female cluster, roughly half of the patients were at fertile age, and the other half at menopausal or postmenopausal age at asthma onset, suggesting that no uniform sex hormoneerelated mechanism explains the pathophysiology of asthma in this cluster. However, hormonal aspects probably play a significant role in the pathogenesis and course of the disease, 26-31 even though the mechanisms are likely multifactorial. Presence of symptoms at childhood in 40% of the patients also suggests the involvement of T H 2-related mechanisms and some overlap with cluster 5. Similar to previous findings, 32 most patients in our study had a coexisting allergic or nonallergic rhinitis. However, we also identified a nonatopic mild to moderate male-predominant asthma without rhinitis, which to our knowledge has not been reported previously. Despite the second highest smoking history, 40% prevalence of permanent bronchial obstruction, and obesity at follow-up, these patients had significantly better prognosis when compared with those in clusters 2 and 4. Rhinitis has been associated with more severe asthma, 32,33 suggesting that lack of rhinitis is a significant determinant associated with the favorable prognosis in this group. Male predominance, moderate smoking history, and the highest weight gain suggest that pathophysiological mechanisms in this cluster are related to those features. Asthma in obese men has been reported to be less often severe when compared with that in obese women 26 and obesity-related asthma may have important sex-specific differences concerning the mediators of the disease. 34,35 For example, in a recent study, no similar difference existed in systemic inflammation between nonobese and obese males as seen in females. 35 Lower level of systemic inflammation could also contribute to lesser number of comorbidities and better prognosis of asthma. Further studies are needed to evaluate the pathophysiological mechanisms in this cluster. Cluster 5 in our study fits well with the previous findings of the important role of age of onset in defining the phenotype. 1,3 Atopy, childhood symptoms, earliest onset of asthma, good steroid-responsiveness, and large FEV 1 reversibility support the conclusion that this cluster represents the traditional early-onset asthma but starting at early adulthood. In addition, the good prognosis strengthens the view. 36 A corresponding adult-onset TABLE III. Inflammatory biomarkers of clusters, median (IQR) Biomarker Cluster 1: nonrhinitic (n [38) Cluster 2: smoking (n [19) Cluster 3: female (n [50) Cluster 4: obese (n [25) Cluster 5: atopic (n [39) Pvalue between clusters Pvalue between clusters Baseline Follow-up Baseline Follow-up Baseline Follow-up Baseline Follow-up Baseline Follow-up Baseline Follow-up Blood eosinophils (10 9 /L) 0.20 (0.11-0.32) 0.14 (0.09-0.25) 0.20 (0.17-0.48) 0.23*(0.13-0.43) 0.30 (0.16-0.44) 0.16 (0.10-0.28) 0.22 (0.18-0.32) 0.13 (0.06-0.25) 0.38†(0.27-0.60) 0.20 (0.12-0.28) .008 .035 Total IgE (kU/L) 95 (28-278) 54 (23-159) 100 (70-552) 95 (24-315) 64 (27-147) 66 (21-138) 62 (22-125) 61 (24-118) 108 (56-409) 67 (30-383) .099 .464 Blood neutrophils (10 9 /L) ND 3.5 (2.9-4.7) ND 4.0 (3.4-4.7) ND 3.8 (2.5-5.2) ND 4.4 (3.2-5.3) ND 3.5 (2.9-4.0) ND .215 FENO (ppb) ND 10 (4-18) ND 10 (5-21) ND 11 (5-18) ND 8 (5-18) ND 16 (6-24) ND .364 FeNO, Fractional exhaled nitric oxide; IQR, interquartile range; ND, not determined. *P<.05 vs cluster 4 at corresponding time point (baseline or follow-up). †P<.05 vs cluster 1 at corresponding time point (baseline or follow-up). Paired comparison between baseline and follow-up is not shown. J ALLERGY CLIN IMMUNOL PRACT JULY/AUGUST 2017 974 ILMARINEN ET AL
TABLE IV. Comorbidities of clusters Comorbidity Cluster 1: nonrhinitic (n [38) Cluster 2: smoking (n [19) Cluster 3: female (n [50) Cluster 4: obese (n [25) Cluster 5: atopic (n [39) Pvalue between clusters Pvalue between clusters Baseline Follow-up Baseline Follow-up Baseline Follow-up Baseline Follow-up Baseline Follow-up Baseline Follow-up Hypertension, n (%) 4 (10.5) 9 (23.7) 4 (21.1) 13 (68.4)*†z3 (6.0) 7 (14.0) 12 (48.0)*†z19 (76.0)*†z2 (5.1) 6 (15.4) <.001 <.001 Diabetes, n (%) 0 4 (10.5) 0 7 (36.8)†z0 3 (6.0) 3 (12.0) 11 (44.0)*†z0 2 (5.1) <.001 <.001 Coronary heart disease, n (%) 1 (2.6) 5 (26.3)†z0 2 (8.0) 0 <.001 <.001 COPD, n (%) 2 (5.4) 9 (23.7)†8 (44.4)x10 (52.6)†zjj 0 1 (2.0) 0 2 (8.0) 1 (2.6) 3 (7.7) <.001 <.001 Any psychiatric disease, n (%) ND 3 (7.9) ND 1 (5.3) ND 5 (10) ND 10 (40)*†zND 3 (7.7) ND .001 Depression, n (%) ND 1 (2.6) ND 1 (5.3) ND 3 (6.0) ND 7 (28)*ND 2 (5.1) ND .004 Painful condition, n(%) ND 3 (7.9) ND 2 (10.5) ND 2 (4) ND 5 (20) ND 1 (2.6) ND .090 Treated dyspepsia, n(%) ND 1 (2.6) ND 2 (10.5) ND 4 (8) ND 6 (24) ND 1 (2.6) ND .020 Total no. of comorbidities, median (IQR) ND 1 (0-2)zND 3 (1-4)*†zND 0.5 (0-1) ND 3 (2.5-4)*†zND 0 (0-1) ND <.001 No. of drugs{, median (IQR) ND 1 (0-3) ND 3 (2-9)*†zND 1 (0-2) ND 6 (4-7)*†zND 0 (0-2) ND <.001 IQR, Interquartile range; ND, not determined. *P<.05 vs cluster 1 at corresponding time point (baseline or follow-up). †P<.05 vs cluster 3 at corresponding time point (baseline or follow-up). zP<.05 vs cluster 5 at corresponding time point (baseline or follow-up). xP<.05 vs all clusters at corresponding time point (baseline or follow-up). jjP<.05 vs cluster 4 at corresponding time point (baseline or follow-up). Paired comparison between baseline and follow-up is not shown. {Number of drugs in use to treat comorbidities. J ALLERGY CLIN IMMUNOL PRACT VOLUME 5, NUMBER 4 ILMARINEN ET AL 975