Cluster analysis identifies characteristic phenotypes of asthma with accelerated lung function decline

The effect of aerobic exercise training on asthma control in postmenopausal women (ATOM): a randomized controlled pilot study

Objective

To evaluate if high-intensity interval training three times weekly for 12 weeks improves asthma control in overweight, postmenopausal women with uncontrolled, late-onset asthma.

Methods

The reported study is a randomized clinical pilot study (www.clinicaltrials.gov; NCT03747211) that compared 12 weeks of high-intensity interval training (spinning) with usual care. The five-question Asthma Control Questionnaire (ACQ-5) was used as primary outcome. Secondary measures included systemic inflammation and inflammation of the airways, body composition, and cardiac function during exercise.

Results

We included 12 women with asthma (mean age 65 years (SD 6); mean body mass index 30 kg/m2 (SD 2)) from whom eight were randomized to exercise and four to control. Baseline ACQ-5 was 1.95 (SD 0.53) in the control group and 2.03 (0.54) in the exercise group. Patients had a mean blood eosinophil level of 0.16 × 109cells/L (SD 0.07) and a mean fraction of exhaled nitric oxide of 23 ppb (SD 25). Mixed models showed that participants in the exercise group reduced their ACQ-5 by 0.55 points (95%CI -1.10 to -0.00; P = 0.08) compared with the control group. The exercise group significantly reduced their mean body fat percentage (-2.7%; 95%CI -4.5 to -0.8; P = 0.02), fat mass (-2.8 kg; 95%CI -5.1 to -0.4; P = 0.044) and android fat mass (-0.33 kg; 95%CI -0.60- -0.06; P = 0.038). In analyses of cardiac measures, we saw no significant effects on right ventricular function (fractional area change), diastolic function or left ventricular function.

Conclusions

Although changes in ACQ-5 were slightly insignificant, these preliminary findings indicate that aerobic exercise training can be used as a means to improve asthma control in overweight, postmenopausal women with asthma.

Asthma clustering methods: a literature-informed application to the children's health study data

OBJECTIVE

The heterogeneity of asthma has inspired widespread application of statistical clustering algorithms to a variety of datasets for identification of potentially clinically meaningful phenotypes. There has not been a standardized data analysis approach for asthma clustering, which can affect reproducibility and clinical translation of results. Our objective was to identify common and effective data analysis practices in the asthma clustering literature and apply them to data from a Southern California population-based cohort of schoolchildren with asthma.

METHODS

As of January 1, 2020, we reviewed key statistical elements of 77 asthma clustering studies. Guided by the literature, we used 12 input variables and three clustering methods (hierarchical clustering, k-medoids, and latent class analysis) to identify clusters in 598 schoolchildren with asthma from the Southern California Children's Health Study (CHS).

RESULTS

Clusters of children identified by latent class analysis were characterized by exhaled nitric oxide, FEV1/FVC, FEV1 percent predicted, asthma control and allergy score; and were predictive of control at two year follow up. Clusters from the other two methods were less clinically remarkable, primarily differentiated by sex and race/ethnicity and less predictive of asthma control over time.

CONCLUSION

Upon review of the asthma phenotyping literature, common approaches of data clustering emerged. When applying these elements to the Children's Health Study data, latent class analysis clusters-represented by exhaled nitric oxide and spirometry measures-had clinical relevance over time.

Identification of New Clusters from Labeled Data Using Mixture Models

Nowadays attempts to segment classes or groups are often found in various fields. Especially, one of emerging issues in biological and medical areas is identification of new subtypes of biological samples or patients. For the identification, we often need to find new subtypes from known classes. In such cases, we usually use clustering techniques. However, usual clustering methods could mix up the labels of the known classes in clustering outcomes and it might lead to wrong interpretation for the identified clusters. Also, they do not use the information about known classes. Thus, this study proposes a Gaussian mixture model-based approach for identifying new clusters from known classes while it maintains them. The performance of the proposed model is verified through simulations and it is applied to a breast cancer data set.

Data-Driven Cluster Analysis of Oxidative Stress Indexes in relation to Vitamin D Level, Age, and Metabolic Control in Patients with Type 2 Diabetes on Metformin Therapy

Recent advances in vitamin D research indicate that patients with type 2 diabetes mellitus (T2DM) are suffering from vitamin D deficiency and increased oxidative stress to a variable extent, which could produce different health impacts for each individual. The novel multivariate statistical method applied in the present study allows metabolic phenotyping of T2DM individuals based on vitamin D status, metabolic control, and oxidative stress status in order to identify effectively different subtypes in our type 2 DM study population. Data-driven statistical cluster analysis was performed with 95 patients with T2DM, treated with metformin. Clusters were based on 12 variables-age, disease duration, vitamin D level, insulin, fasting glycemia (FG), glycated hemoglobin (HbA1c), high-density and low-density lipoprotein, total cholesterol (TC), triglycerides (TG), body mass index (BMI), and triglycerides/glucose index (TYG). The analysis revealed four unique clusters which differed significantly in terms of vitamin D status, with a mean 25 (OH) D level in cluster 1 (57.84 ± 11.46 nmol/L) and cluster 4 (53.78 ± 22.36 nmol/L), falling within the insufficiency range. Cluster 2 had the highest mean level of 25 (OH) D (84.55 ± 22.66 nmol/L), indicative of vitamin D sufficiency. Cluster 3 had a mean vitamin D level below 50 nmol/L (49.27 ± 16.95), which is considered deficient. Patients in the vitamin D sufficient cluster had a significantly better glycemic and metabolic control as well as a lower level of lipid peroxidation compared to other clusters. The patients from the vitamin D sufficient cluster also had a significantly higher level of vitamin D/MPO, vitamin D/XO, vitamin D/MDA, vitamin D/CAT, and vitamin D/TRC than that in the vitamin deficient and insufficient clusters. The vitamin D deficient cluster included significantly younger patients and had a significantly lower level of AOPP/TRC and albumin/TRC than the vitamin D sufficient cluster. The evidence from our cluster analysis in the context of separated T2DM demonstrates beneficial effects of optimal vitamin D status on metabolic control and oxidative stress in T2DM patients. Older T2DM patients require higher vitamin D levels in order to achieve good metabolic control and favorable antioxidant protection. Since protein damage is more pronounced in these patients, adding water-soluble antioxidant in addition to higher doses of vitamin D should be considered.

Classification of non-acute bronchial asthma according to allergy and eosinophil characteristics: a retrospective study

BACKGROUND

Investigating the endotypes of the different asthma phenotypes would help disease monitoring, prognosis determination, and improving asthma management standardization. This study aimed to classify asthma into four endotypes according to the allergic and eosinophilic characteristics and explore the phenotypes (clinical characteristics, pulmonary functions, and fractional expired nitric oxide (FeNO)) of each endotype.

METHODS

This retrospective study included non-acute asthma patients treated at the First Hospital of Shanxi Medical University (05/2016-01/2018). The patients were classified into the eosinophilic allergic, eosinophilic non-allergic, non-eosinophilic allergic, and non-eosinophilic non-allergic asthma endotypes. Serum sIgE, lung function, FeNO, and induced sputum cytology were tested and compared among groups.

RESULTS

Of the 171 included patients, 22 had eosinophilic allergic asthma, 17 had eosinophilic non-allergic asthma, 66 had non-eosinophilic allergic asthma, and 66 had non-eosinophilic non-allergic asthma. Lung function measurements (FEV1%, FEF25%, FEF50%, FEF75%, and FEF25-75%) showed that airway dysfunction was worse in eosinophilic non-allergic asthma than in the other three endotypes (all P < 0.001). In allergic asthma patients, eosinophilic asthma had worse airway dysfunction than non-eosinophilic asthma (all P < 0.05). Similar results were found in non-allergic asthma (all P < 0.01). The FeNO levels in eosinophilic allergic asthma were higher than in eosinophilic non-allergic and non-eosinophilic non-allergic asthma (both P = 0.001).

CONCLUSIONS

FeNO can objectively reflect eosinophilic airway inflammation in asthma. Endotypic classification of asthma patients regarding the allergic and eosinophilic characteristics is conducive to the effective management of patients with asthma.

Effect of aerobic exercise training on asthma control in postmenopausal women (the ATOM-study): protocol for an outcome assessor, randomised controlled trial

INTRODUCTION

Late-onset asthma in postmenopausal women is characterised by poor disease control with daily symptoms and reduced quality of life despite treatment with inhaled antiasthma therapies. These patients represent a phenotype that is characterised by low eosinophilic airway inflammation, severe symptoms, moderate obesity and poor response to inhaled antiasthma therapies, which highlights the need of identification of alternative treatment strategies. Thus, this study aims to evaluate if regular high-intensity aerobic exercise improves symptom control in postmenopausal women with asthma.

METHODS AND ANALYSIS

This is an ongoing randomised controlled trial planning to enrol 40 postmenopausal women with late-onset asthma. Participants are randomised 1:1 either to supervised exercise training (spinning) three times per week for 12 weeks or to usual care. The primary outcome is change from baseline to follow-up in the Asthma Control Questionnaire. Secondary outcomes are changes in markers of systemic inflammation, airway inflammation, body composition and right ventricular function of the heart.

ETHICS AND DISSEMINATION

The study is approved by the Ethics Committee in the Capital Region of Denmark nr. H-18028966 and the Danish Data Protection Agency nr. VD-2019-59. The methods used in the study are well known and have a low risk with a chance of substantial improvement in disease control in this patient group. Results are planned to be published in an international peer-reviewed medical journal regardless of outcome.

TRIAL REGISTRATION NUMBER

NCT03747211.

A Systematic Review of Asthma Phenotypes Derived by Data-Driven Methods

Classification of asthma phenotypes has a potentially relevant impact on the clinical management of the disease. Methods for statistical classification without a priori assumptions (data-driven approaches) may contribute to developing a better comprehension of trait heterogeneity in disease phenotyping. This study aimed to summarize and characterize asthma phenotypes derived by data-driven methods. We performed a systematic review using three scientific databases, following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) criteria. We included studies reporting adult asthma phenotypes derived by data-driven methods using easily accessible variables in clinical practice. Two independent reviewers assessed studies. The methodological quality of included primary studies was assessed using the ROBINS-I tool. We retrieved 7446 results and included 68 studies of which 65% (n = 44) used data from specialized centers and 53% (n = 36) evaluated the consistency of phenotypes. The most frequent data-driven method was hierarchical cluster analysis (n = 19). Three major asthma-related domains of easily measurable clinical variables used for phenotyping were identified: personal (n = 49), functional (n = 48) and clinical (n = 47). The identified asthma phenotypes varied according to the sample’s characteristics, variables included in the model, and data availability. Overall, the most frequent phenotypes were related to atopy, gender, and severe disease. This review shows a large variability of asthma phenotypes derived from data-driven methods. Further research should include more population-based samples and assess longitudinal consistency of data-driven phenotypes.

Challenges of Clustering Multimodal Clinical Data: Review of Applications in Asthma Subtyping

Background

In the current era of personalized medicine, there is increasing interest in understanding the heterogeneity in disease populations. Cluster analysis is a method commonly used to identify subtypes in heterogeneous disease populations. The clinical data used in such applications are typically multimodal, which can make the application of traditional cluster analysis methods challenging.

Objective

This study aimed to review the research literature on the application of clustering multimodal clinical data to identify asthma subtypes. We assessed common problems and shortcomings in the application of cluster analysis methods in determining asthma subtypes, such that they can be brought to the attention of the research community and avoided in future studies.

Methods

We searched PubMed and Scopus bibliographic databases with terms related to cluster analysis and asthma to identify studies that applied dissimilarity-based cluster analysis methods. We recorded the analytic methods used in each study at each step of the cluster analysis process.

Results

Our literature search identified 63 studies that applied cluster analysis to multimodal clinical data to identify asthma subtypes. The features fed into the cluster algorithms were of a mixed type in 47 (75%) studies and continuous in 12 (19%), and the feature type was unclear in the remaining 4 (6%) studies. A total of 23 (37%) studies used hierarchical clustering with Ward linkage, and 22 (35%) studies used k-means clustering. Of these 45 studies, 39 had mixed-type features, but only 5 specified dissimilarity measures that could handle mixed-type features. A further 9 (14%) studies used a preclustering step to create small clusters to feed on a hierarchical method. The original sample sizes in these 9 studies ranged from 84 to 349. The remaining studies used hierarchical clustering with other linkages (n=3), medoid-based methods (n=3), spectral clustering (n=1), and multiple kernel k-means clustering (n=1), and in 1 study, the methods were unclear. Of 63 studies, 54 (86%) explained the methods used to determine the number of clusters, 24 (38%) studies tested the quality of their cluster solution, and 11 (17%) studies tested the stability of their solution. Reporting of the cluster analysis was generally poor in terms of the methods employed and their justification.

Conclusions

This review highlights common issues in the application of cluster analysis to multimodal clinical data to identify asthma subtypes. Some of these issues were related to the multimodal nature of the data, but many were more general issues in the application of cluster analysis. Although cluster analysis may be a useful tool for investigating disease subtypes, we recommend that future studies carefully consider the implications of clustering multimodal data, the cluster analysis process itself, and the reporting of methods to facilitate replication and interpretation of findings.

Risk factors for fixed airflow obstruction in children and adolescents with asthma: 4-Year follow-up

BACKGROUND

Asthma is a disease with reversible bronchoconstriction; however, some patients develop fixed airflow obstruction (FAO). Previous studies have reported the incidence and risk factors of FAO in adults; however, the corresponding factors in children remain poorly understood.

AIM

To evaluate the incidence and risk factors of FAO in children and adolescents with asthma.

METHOD

Observational and prospective cohort study with a 4-year follow-up of clinically stable patients with asthma (from 6-8 years old). Anthropometric data, history of asthma, number of hospitalizations, frequent exacerbations, asthma severity, asthma control, inhaled corticosteroid dose, atopy, and lung function were analyzed as potential risk factors for FAO. FAO was defined by a ratio of the forced expiratory volume in the first second to the forced vital capacity below the lower limit of normal, even after inhaled and oral corticosteroid treatment.

RESULTS

Four hundred and twenty-eight patients were recruited, and 358 were analyzed. The FAO incidence in children and adolescents with asthma was 9.5% (n = 34), starting at 10 years of age. Age, body mass index, hospitalizations for asthma, bronchodilator response, frequent exacerbations, length of exacerbations, and asthma severity were associated with FAO. Frequent exacerbations (odds ratio [OR] = 4.0; 95% confidence interval [CI] = 1.3-11.7) and asthma severity categorized as steps 4 to 5 (OR = 3.5; 95% CI = 1.6-7.6) remained risk factors.

CONCLUSIONS

Frequent exacerbations and asthma severity are the risk factors for FAO in children and adolescents with asthma.

Elevated eosinophils, IL5 and IL8 in induced sputum in asthma patients with accelerated FEV1 decline

BACKGROUND

Some patients with asthma present with accelerated lung function decline. This phenomenon is mostly associated with severe exacerbations and with poor asthma control.

OBJECTIVE

Our aim was to detect the extent of FEV1 decline in patients with mild asthma and to discriminate clinical, functional and inflammatory factors associated with accelerated FEV1 decline.

METHODS

We recruited 50 patients with mild asthma for pulmonary function testing and induced sputum sampling 12-15 years after the initial diagnosis. In 33 patients, from whom sputum of a good quality was obtained, inflammatory cells were counted and concentrations of cytokines IL-2, IL-4, IL-5, IL-8, IL-10, IFN-γ, angiogenin and VEGF in the sputum were measured by cytometric bead array.

RESULTS

Eighteen of 33 patients presented with accelerated FEV1 decline of more than 30 ml/year, with a mean (SEM) of 43.2 (3.9) ml/year, compared to 15 control patients with a FEV1 decline of 14.4 (2.1) ml/year. In the accelerated FEV1 decline group, we found elevated sputum levels of IL5 with a median (IQR) of 1.8 (0.4-3.2) pg/ml vs. 0.2 (0.1-1.2) pg/ml, p = 0.04; IL8 with a mean (SEM) of 1503 (194) pg/ml vs. 938 (177) pg/ml, p = 0.04; and eosinophils with a median (IQR) of 223 (41-1020) cells/μl vs. 39 (1-190) cells/μl, p = 0.03. No significant differences in other measured parameters were detected between the two groups.

CONCLUSION

Elevated sputum eosinophils, IL5 and IL8, which have a potential to stimulate airway remodelling, might be a useful non-invasive biomarkers and therapeutic targets of accelerated FEV1 decline in asthma patients.

Distinct asthma phenotypes with low maximal attainment of lung function on cluster analysis

OBJECTIVE

Asthma is a heterogeneous disease with varying clinical presentations, severity and ability to achieve asthma control. The present study aimed to characterize clinical phenotypes of asthma in an Indian cohort of subjects using a cluster analysis approach.

METHODS

Patients with confirmed asthma (N = 100) and at least 6-months of follow-up data, identified by retrospective chart review, were included in this study. Demographics, age at disease onset, disease duration, body mass index, serial spirometry and allergen sensitization were assessed. Asthma control was assessed prospectively using Global Initiative for Asthma and Asthma Control Test. R version 3.4.3 was used for statistical analysis. Ward's minimum-variance hierarchical clustering method was performed using an agglomerative (bottom-up) approach. To compare differences between clusters, analysis of variance using Kruskal-Wallis test (continuous variables) and chi-square test (categorical variables) was used.

RESULTS

Cluster analysis of 100 treatment-naive patients with asthma identified four clusters. Cluster 1, (N = 40), childhood onset of disease, normal body weight, equal gender distribution and achieved normal lung function. Cluster 2 (N = 16) included adolescent disease-onset, obese, majority males and had poor attainment of maximum lung functions. Cluster 3 (N = 20) were older, late-onset of disease, obese, majority male and had poor attainment of maximum lung function. Cluster 4 (N = 24) had adult-onset of disease, obese, predominantly female and achieved normal lung function.

CONCLUSIONS

In an Indian cohort of well-characterized patients with asthma, cluster analysis identified four distinct clinical phenotypes of asthma, two of which had poor attainment of maximum lung functions.

Phenotypes of atopic dermatitis identified by cluster analysis in early childhood

Atopic dermatitis is a chronic, relapsing, inflammatory skin disease that usually appears in early childhood and develops into a heterogeneous disease during childhood. The clinical course and treatment for atopic dermatitis can differ according to its phenotype and/or endotype. This study aimed to identify clinical phenotypes of atopic dermatitis in early childhood. Data were obtained from 572 children under 3 years of age with atopic dermatitis. Cluster analysis applied to 11 variables, and we identified four clusters of atopic dermatitis. Children in cluster A (n = 141) had early-onset atopic dermatitis with high blood eosinophil counts, serum total immunoglobulin E and rates of sensitization to food allergens. Children in cluster B (n = 218) had early-onset atopic dermatitis with low blood eosinophil counts, serum total immunoglobulin E and rates of sensitization to both food and inhalant allergens. Children in cluster C (n = 53) had early-onset atopic dermatitis with high C-reactive protein levels and white blood cell counts. Children in cluster D (n = 160) had middle-onset atopic dermatitis with high serum total immunoglobulin E and rates of sensitization to inhalant allergens. Cluster A had the highest Scoring for Atopic Dermatitis and transepidermal water loss values. Age at onset, age at diagnosis, white blood cell count, eosinophil count, C-reactive protein and serum total immunoglobulin E level were the strongest predictors of cluster assignment. Analysis of these six variables alone resulted in correct classification of 95.5% of the subjects. These results support the heterogeneity of atopic dermatitis, even in early childhood.

A Cluster Analysis of Bronchial Asthma Patients with Depressive Symptoms

Objective Whether or not depression affects the control or severity of asthma is unclear. We performed a cluster analysis of asthma patients with depressive symptoms to clarify their characteristics. Methods Multiple medical institutions in Niigata Prefecture, Japan, were surveyed in 2014. We recorded the age, disease duration, body mass index (BMI), medications, and surveyed asthma control status and severity, as well as depressive symptoms and adherence to treatment using questionnaires. A hierarchical cluster analysis was performed on the group of patients assessed as having depression. Results Of 2,273 patients, 128 were assessed as being positive for depressive symptoms [DS(+)]. Thirty-three were excluded because of missing data, and the remaining 95 DS[+] patients were classified into 3 clusters (A, B, and C). The patients in cluster A (n=19) were elderly, had severe, poorly controlled asthma, and demonstrated possible adherence barriers; those in cluster B (n=26) were elderly with a low BMI and had no significant adherence barriers but had severe, poorly controlled asthma; and those in cluster C (n=50) were younger, with a high BMI, no significant adherence barriers, well-controlled asthma, and few were severely affected. The scores for depressive symptoms were not significantly different between clusters. Conclusion About half of the patients in the DS[+] group had severe, poorly controlled asthma, and these clusters were able to be distinguished by their Adherence Starts with Knowledge (ASK)-12 score, which reflects adherence barriers. The control status and severity of asthma may also be related to the age, disease duration, and BMI in the DS[+] group.

Phenotypic analysis of asthma in Japanese athletes

BACKGROUND

Asthma in athlete populations such as Olympic athletes has various pathogeneses. However, few reports are available on the features of asthma in the athlete population in clinical practice. In this study, we focused on classifying asthma in Japanese athlete population.

METHODS

We performed a cluster analysis of data from pulmonary function tests and clinical biomarkers before administering inhaled corticosteroids (ICS) therapy in athlete population of individuals diagnosed with asthma (n = 104; male, 76.9%; median age, 16.0 years), based on respiratory symptoms and positive data on methacholine provocation tests. We also compared backgrounds, sports types, and treatments between clusters.

RESULTS

Three clusters were identified. Cluster 1 (32%) comprised athletes with a less atopic phenotype and normal pulmonary function. Cluster 2 (44%) comprised athletes with a less atopic phenotype and lower percent predicted forced expiratory volume in 1 s (%FEV₁) values, despite less symptomatic state. Cluster 3 (24%) comprised athletes with a strong atopic phenotype such as high eosinophil count in the blood and total serum immunoglobulin E level. After treatment with ICS or ICS plus long-acting β-adrenergic receptor agonist for 6-12 months, %FEV₁ values were significantly improved in Cluster 2 athletes, whereas Cluster 3 athletes had a significant decrease in the fraction of exhaled nitric oxide compared to pretreatment values.

CONCLUSIONS

These data suggest three clusters exist in Japanese athlete population with asthma. Between the clusters, the characteristics differed with regard to symptoms, atopic features, and lower %FEV₁ values. The pathogeneses between clusters may vary depending on the inflammation type and airway hyperresponsiveness.

Identification and validation of asthma phenotypes in Chinese population using cluster analysis

BACKGROUND

Asthma is a heterogeneous airway disease, so it is crucial to clearly identify clinical phenotypes to achieve better asthma management.

OBJECTIVE

To identify and prospectively validate asthma clusters in a Chinese population.

METHODS

Two hundred eighty-four patients were consecutively recruited and 18 sociodemographic and clinical variables were collected. Hierarchical cluster analysis was performed by the Ward method followed by k-means cluster analysis. Then, a prospective 12-month cohort study was used to validate the identified clusters.

RESULTS

Five clusters were successfully identified. Clusters 1 (n = 71) and 3 (n = 81) were mild asthma phenotypes with slight airway obstruction and low exacerbation risk, but with a sex differential. Cluster 2 (n = 65) described an "allergic" phenotype, cluster 4 (n = 33) featured a "fixed airflow limitation" phenotype with smoking, and cluster 5 (n = 34) was a "low socioeconomic status" phenotype. Patients in clusters 2, 4, and 5 had distinctly lower socioeconomic status and more psychological symptoms. Cluster 2 had a significantly increased risk of exacerbations (risk ratio [RR] 1.13, 95% confidence interval [CI] 1.03-1.25), unplanned visits for asthma (RR 1.98, 95% CI 1.07-3.66), and emergency visits for asthma (RR 7.17, 95% CI 1.26-40.80). Cluster 4 had an increased risk of unplanned visits (RR 2.22, 95% CI 1.02-4.81), and cluster 5 had increased emergency visits (RR 12.72, 95% CI 1.95-69.78). Kaplan-Meier analysis confirmed that cluster grouping was predictive of time to the first asthma exacerbation, unplanned visit, emergency visit, and hospital admission (P < .0001 for all comparisons).

CONCLUSION

We identified 3 clinical clusters as "allergic asthma," "fixed airflow limitation," and "low socioeconomic status" phenotypes that are at high risk of severe asthma exacerbations and that have management implications for clinical practice in developing countries.

Asthma and the Risk of Rheumatoid Arthritis: An Insight into the Heterogeneity and Phenotypes of Asthma

Asthma is traditionally regarded as a chronic airway disease, and recent literature proves its heterogeneity, based on distinctive clusters or phenotypes of asthma. In defining such asthma clusters, the nature of comorbidity among patients with asthma is poorly understood, by assuming no causal relationship between asthma and other comorbid conditions, including both communicable and noncommunicable diseases. However, emerging evidence suggests that the status of asthma significantly affects the increased susceptibility of the patient to both communicable and noncommunicable diseases. Specifically, the impact of asthma on susceptibility to noncommunicable diseases such as chronic systemic inflammatory diseases (e.g., rheumatoid arthritis), may provide an important insight into asthma as a disease with systemic inflammatory features, a conceptual understanding between asthma and asthma-related comorbidity, and the potential implications on the therapeutic and preventive interventions for patients with asthma. This review discusses the currently under-recognized clinical and immunological phenotypes of asthma; specifically, a higher risk of developing a systemic inflammatory disease such as rheumatoid arthritis and their implications, on the conceptual understanding and management of asthma. Our discussion is divided into three parts: literature summary on the relationship between asthma and the risk of rheumatoid arthritis; potential mechanisms underlying the association; and implications on asthma management and research.

Phenotypes of asthma in low-income children and adolescents: cluster analysis

Objective

Studies characterizing asthma phenotypes have predominantly included adults or have involved children and adolescents in developed countries. Therefore, their applicability in other populations, such as those of developing countries, remains indeterminate. Our objective was to determine how low-income children and adolescents with asthma in Brazil are distributed across a cluster analysis.

Methods

We included 306 children and adolescents (6-18 years of age) with a clinical diagnosis of asthma and under medical treatment for at least one year of follow-up. At enrollment, all the patients were clinically stable. For the cluster analysis, we selected 20 variables commonly measured in clinical practice and considered important in defining asthma phenotypes. Variables with high multicollinearity were excluded. A cluster analysis was applied using a twostep agglomerative test and log-likelihood distance measure.

Results

Three clusters were defined for our population. Cluster 1 (n = 94) included subjects with normal pulmonary function, mild eosinophil inflammation, few exacerbations, later age at asthma onset, and mild atopy. Cluster 2 (n = 87) included those with normal pulmonary function, a moderate number of exacerbations, early age at asthma onset, more severe eosinophil inflammation, and moderate atopy. Cluster 3 (n = 108) included those with poor pulmonary function, frequent exacerbations, severe eosinophil inflammation, and severe atopy.

Conclusions

Asthma was characterized by the presence of atopy, number of exacerbations, and lung function in low-income children and adolescents in Brazil. The many similarities with previous cluster analyses of phenotypes indicate that this approach shows good generalizability.

Syk Regulates Neutrophilic Airway Hyper-Responsiveness in a Chronic Mouse Model of Allergic Airways Inflammation

Background

Asthma is a chronic inflammatory disease characterized by airways hyper-responsiveness (AHR), reversible airway obstruction, and airway inflammation and remodeling. We previously showed that Syk modulates methacholine-induced airways contractility in naïve mice and in mice with allergic airways inflammation. We hypothesize that Syk plays a role in the pathogenesis of AHR; this was evaluated in a chronic 8-week mouse model of house dust mite (HDM)-induced allergic airways inflammation.

Methods

We used the Syk^flox/flox//rosa26CreER^T2 conditional Syk knock-out mice to assess the role of Syk prior to HDM exposure, and treated HDM-sensitized mice with the Syk inhibitor, GSK143, to evaluate its role in established allergic airways inflammation. Respiratory mechanics and methacholine (MCh)-responsiveness were assessed using the flexiVent^® system. Lungs underwent bronchoalveolar lavage to isolate inflammatory cells or were frozen for determination of gene expression in tissues.

Results

MCh-induced AHR was observed following HDM sensitization in the Syk-intact (Syk^flox/flox) and vehicle-treated BALB/c mice. MCh responsiveness was reduced to control levels in HDM-sensitized Syk^del/del mice and in BALB/c and Syk^flox/flox mice treated with GSK143. Both Syk^del/del and GSK143-treated mice mounted appropriate immune responses to HDM, with HDM-specific IgE levels that were comparable to Syk^flox/flox and vehicle-treated BALB/c mice. HDM-induced increases in bronchoalveolar lavage cell counts were attenuated in both Syk^del/del and GSK143-treated mice, due primarily to decreased neutrophil recruitment. Gene expression analysis of lung tissues revealed that HDM-induced expression of IL-17 and CXCL-1 was significantly attenuated in both Syk^del/del and GSK143-treated mice.

Conclusion

Syk inhibitors may play a role in the management of neutrophilic asthma.

Identification of Asthma Subtypes Using Clustering Methodologies

Asthma is a heterogeneous disease comprising a number of subtypes which may be caused by different pathophysiologic mechanisms (sometimes referred to as endotypes) but may share similar observed characteristics (phenotypes). The use of unsupervised clustering in adult and paediatric populations has identified subtypes of asthma based on observable characteristics such as symptoms, lung function, atopy, eosinophilia, obesity, and age of onset. Here we describe different clustering methods and demonstrate their contributions to our understanding of the spectrum of asthma syndrome. Precise identification of asthma subtypes and their pathophysiological mechanisms may lead to stratification of patients, thus enabling more precise therapeutic and prevention approaches.

Phenotypes, Risk Factors, and Mechanisms of Adult-Onset Asthma

Asthma is a heterogeneous disease with many phenotypes, and age at disease onset is an important factor in separating the phenotypes. Genetic factors, atopy, and early respiratory tract infections are well-recognized factors predisposing to childhood-onset asthma. Adult-onset asthma is more often associated with obesity, smoking, depression, or other life-style or environmental factors, even though genetic factors and respiratory tract infections may also play a role in adult-onset disease. Adult-onset asthma is characterized by absence of atopy and is often severe requiring treatment with high dose of inhaled and/or oral steroids. Variety of risk factors and nonatopic nature of adult-onset disease suggest that variety of mechanisms is involved in the disease pathogenesis and that these mechanisms differ from the pathobiology of childhood-onset asthma with prevailing Th2 airway inflammation. Recognition of the mechanisms and mediators that drive the adult-onset disease helps to develop novel strategies for the treatment. The aim of this review was to summarize the current knowledge on the pathogenesis of adult-onset asthma and to concentrate on the mechanisms and mediators involved in establishing adult-onset asthma in response to specific risk factors. We also discuss the involvement of these mechanisms in the currently recognized phenotypes of adult-onset asthma.

Paving the COWpath: Learning and visualizing clinical pathways from electronic health record data

OBJECTIVE

Clinical pathways translate best available evidence into practice, indicating the most widely applicable order of treatment interventions for particular treatment goals. We propose a practice-based clinical pathway development process and a data-driven methodology for extracting common clinical pathways from electronic health record (EHR) data that is patient-centered, consistent with clinical workflow, and facilitates evidence-based care.

MATERIALS AND METHODS

Visit data of 1,576 chronic kidney disease (CKD) patients who developed acute kidney injury (AKI) from 2009 to 2013 are extracted from the EHR. We model each patient's multi-dimensional clinical records into one-dimensional sequences using novel constructs designed to capture information on each visit's purpose, procedures, medications and diagnoses. Analysis and clustering on visit sequences identify distinct types of patient subgroups. Characterizing visit sequences as Markov chains, significant transitions are extracted and visualized into clinical pathways across subgroups.

RESULTS

We identified 31 patient subgroups whose extracted clinical pathways provide insights on how patients' conditions and medication prescriptions may progress over time. We identify pathways that show typical disease progression, practices that are consistent with guidelines, and sustainable improvements in patients' health conditions. Visualization of pathways depicts the likelihood and direction of disease progression under varied contexts.

DISCUSSION AND CONCLUSIONS

Accuracy of EHR data and diversity in patients' conditions and practice patterns are critical challenges in learning insightful practice-based clinical pathways. Learning and visualizing clinical pathways from actual practice data captured in the EHR may facilitate efficient practice review by healthcare providers and support patient engagement in shared decision making.

Asthma-COPD overlap 2015: now we are six

BACKGROUND

The overlap between asthma and COPD is increasingly recognised. This review examines the new insights, treatment and remaining knowledge gaps for asthma-COPD overlap.

METHOD

A systematic literature review of cluster analyses of asthma and COPD was performed. Articles from 2009 to the present dealing with prevalence, morbidity and treatment of asthma-COPD overlap were identified and reviewed.

RESULTS

Asthma-COPD overlap was consistently recognised in studies using a variety of different study designs and sampling. The prevalence was approximately 20% in patients with obstructive airways diseases. Asthma-COPD overlap was associated with increased morbidity and possibly an increased mortality and comorbidity. There was evidence of a heterogeneous pattern of airway inflammation that included eosinophilic (in adult asthma), neutrophilic or mixed patterns (in severe asthma and COPD). Systemic inflammation was present in asthma-COPD overlap and resembled that of COPD. Within asthma-COPD overlap, there is evidence of different subgroups, and recognition using bronchodilator responsiveness has not been successful. Guidelines generally recommend a serial approach to assessment, with treatment recommendations dominated by an asthma paradigm. Research is needed into key clinical features that impact outcome, mechanisms and treatment approaches in asthma-COPD overlap. Identifying and treating disease components by multidimensional assessment shows promise.

CONCLUSIONS

Asthma-COPD overlap has drawn attention to the significant heterogeneity that exists within obstructive airway diseases. It should be replaced by novel approaches that identify and manage the components of this heterogeneity, such as multidimensional assessment and treatment. Future research is needed to test these novel and personalised approaches.

Patterns of Dupuytren disease in fingers: studying correlations with a multivariate ordinal logit model

BACKGROUND

Dupuytren disease affects fingers in a variable fashion. Knowledge about specific disease patterns (phenotype) based on location and severity of the disease is lacking.

METHODS

In this cross-sectional study, 344 primary affected hands with Dupuytren disease were physically examined. The Pearson correlation coefficient between the coexistence of Dupuytren disease in pairs of fingers was calculated, and agglomerative hierarchical clustering was applied to identify possible clusters of affected fingers. With a multivariate ordinal logit model, the authors studied the correlation on severity, taking into account age and sex, and tested hypotheses on independence between groups of fingers.

RESULTS

The ring finger was most frequently affected by Dupuytren disease, and contractures were seen in 15.1 percent of affected rays. The severity of thumb and index finger, middle and ring fingers, and middle and little fingers was significantly correlated. Occurrences in pairs of fingers were highest in the middle and ring fingers and lowest in the thumb and index finger. Correlation between the ring and little fingers and a correlation between fingers from the ulnar and radial sides could not be demonstrated.

CONCLUSIONS

Rays on the ulnar side of the hand are predominantly affected. The middle finger is substantially correlated with other fingers on the ulnar side, and the thumb and index finger are correlated; however, there was no evidence that the ulnar side and the radial side were correlated in any way, which suggests that occurrence on one side of the hand does not predict Dupuytren disease on the other side of the hand.

CLINCIAL QUESTION/LEVEL OF EVIDENCE

Risk, III.

Transforming growth factor β and severe asthma: a perfect storm

Asthma is a chronic inflammatory airway disease involving complex interplay between resident and infiltrative cells, which in turn are regulated by a wide range of host mediators. Identifying useful biomarkers correlating with clinical symptoms and degree of airway obstruction remain important to effective future asthma treatments. Transforming growth factor β (TGF-β) is a major mediator involved in pro-inflammatory responses and fibrotic tissue remodeling within the asthmatic lung. Its role however, as a therapeutic target remains controversial. The aim of this review is to highlight its role in severe asthma including interactions with adaptive T-helper cells, cytokines and differentiation through regulatory T-cells. Associations between TGF-β and eosinophils will be addressed and the effects of genetic polymorphisms of the TGF-β1 gene explored in the context of asthma. We highlight TGF-β1 as a potential future therapeutic target in severe asthma including its importance in identifying emerging clinical phenotypes in asthmatic subjects who may be suitable for individualized therapy through TGF-β modulation.