Subtypes of Adult-Onset Asthma at the Time of Diagnosis: A Latent Class Analysis.

INTRODUCTION: Only a few previous studies have investigated the subtypes of adult-onset asthma. No previous study has assessed whether these subtypes are different between men and women, or whether these subtypes have different risk factors. METHODS: We applied latent class analyses to the Finnish Environment and Asthma Study population, including 520 new cases of adult-onset asthma. We formed subtypes separately between women and men and analyzed the following determinants as potential predictors for these subtypes: age, body mass index, smoking, and parental asthma. RESULTS: Among women, the subtypes identified were: 1. Moderate asthma, 2. Cough-variant asthma, 3. Eosinophilic asthma, 4. Allergic asthma, and 5. Difficult asthma. Among men, the subtypes were: 1. Mild asthma, 2. Moderate asthma, 3. Allergic asthma, and 4. Difficult asthma. Three of the subtypes were similar among women and men: Moderate, Allergic, and Difficult asthma. In addition, women had two distinct subtypes: Cough-variant asthma, and Eosinophilic asthma. These subtypes had different risk factor profiles, e.g., heredity was important for Eosinophilic and Allergic asthma (RR for Both parents having asthma in Eosinophilic 3.55 (1.09 to 11.62)). Furthermore, smoking increased the risk of Moderate asthma among women (RR for former smoking 2.21 (1.19 to 4.11)) and Difficult asthma among men but had little influence on Allergic or Cough-variant asthma. Conclusion: This is an original investigation of the subtypes of adult-onset asthma identified at the time of diagnosis. These subtypes differ between women and men, and these subtypes have different risk factor profiles. These findings have both clinical and public health importance for the etiology, prognosis, and treatment of adult-onset asthma.

HLA-G in asthma and its potential as an effective therapeutic agent

Objective: Asthma is a heterogeneous disease. Severity of asthma and sensitivity to medications vary across asthma subtypes. Human leukocyte antigen (HLA)-G has a wide range of functions in normal and pathological physiology. Due to its powerful immune function, HLA-G participates in the pathogenesis of different asthma phenotypes by regulating the activity and function of various immune cells. The mechanism of HLA-G in asthma is not fully clear, and there is no consensus on its mechanism in asthma. Further studies are needed to explore the role of HLA-G in different phenotypes of human asthma. Methods: Observational study. Results: HLA-G is an important immunomodulatory factor in asthma. Studies have found different levels of HLA-G in patients with different asthma subtypes and healthy controls, but other studies have come to the opposite conclusion. Conclusion: We speculate that further study on the mechanism of HLA-G in asthma pheno-types may explain some of the contradictions in current studies. Findings should provide information regarding the potential of HLA-G as a novel target for asthma diagnosis and treatment (AU)

HLA-G in asthma and its potential as an effective therapeutic agent.

OBJECTIVE: Asthma is a heterogeneous disease. Severity of asthma and sensitivity to medications vary across asthma subtypes. Human leukocyte antigen (HLA)-G has a wide range of functions in normal and pathological physiology. Due to its powerful immune function, HLA-G participates in the pathogenesis of different asthma phenotypes by regulating the activity and function of various immune cells. The mechanism of HLA-G in asthma is not fully clear, and there is no consensus on its mechanism in asthma. Further studies are needed to explore the role of HLA-G in different phenotypes of human asthma. METHODS: Observational study. RESULTS: HLA-G is an important immunomodulatory factor in asthma. Studies have found different levels of HLA-G in patients with different asthma subtypes and healthy controls, but other studies have come to the opposite conclusion. CONCLUSION: We speculate that further study on the mechanism of HLA-G in asthma pheno-types may explain some of the contradictions in current studies. Findings should provide information regarding the potential of HLA-G as a novel target for asthma diagnosis and treatment.

Racial/ethnic differences in eligibility for asthma biologics among pediatric populations.

BACKGROUND: Asthma is a heterogeneous disease. Clinical blood parameters differ by race/ethnicity and are used to distinguish asthma subtypes and inform therapies. Differences in subtypes may explain population-specific trends in asthma outcomes. However, these differences in racial/ethnic minority pediatric populations are unclear. OBJECTIVE: We investigated the association of blood parameters and asthma subtypes with asthma outcomes and examined population-specific eligibility for biologic therapies in minority pediatric populations. METHODS: Using data from 2 asthma case-control studies of pediatric minority populations, we performed case-control (N = 3738) and case-only (N = 2743) logistic regressions to quantify the association of blood parameters and asthma subtypes with asthma outcomes. Heterogeneity of these associations was tested using an interaction term between race/ethnicity and each exposure. Differences in therapeutic eligibility were investigated using chi-square tests. RESULTS: Race/ethnicity modified the association between total IgE and asthma exacerbations. Elevated IgE level was associated with worse asthma outcomes in Puerto Ricans. Allergic asthma was associated with worse outcomes in Mexican Americans, whereas eosinophilic asthma was associated with worse outcomes in Puerto Ricans. A lower proportion of Puerto Ricans met dosing criteria for allergic asthma-directed biologic therapy than other groups. A higher proportion of Puerto Ricans qualified for eosinophilic asthma-directed biologic therapy than African Americans. CONCLUSIONS: We found population-specific associations between blood parameters and asthma subtypes with asthma outcomes. Our findings suggest that eligibility for asthma biologic therapies differs across pediatric racial/ethnic populations. These findings call for more studies in diverse populations for equitable treatment of minority patients with asthma.

Systems Approaches to Treatment Response to Imatinib in Severe Asthma: A Pilot Study.

There is an acute need for advances in pharmacologic therapies and a better understanding of novel drug targets for severe asthma. Imatinib, a tyrosine kinase inhibitor, has been shown to improve forced expiratory volume in 1 s (FEV1) in a clinical trial of patients with severe asthma. In a pilot study, we applied systems biology approaches to epithelium gene expression from these clinical trial patients treated with imatinib to better understand lung function response with imatinib treatment. Bronchial brushings from ten imatinib-treated patient samples and 14 placebo-treated patient samples were analyzed. We used personalized perturbation profiles (PEEPs) to characterize gene expression patterns at the individual patient level. We found that strong responders-patients with greater than 20% increase in FEV1-uniquely shared multiple downregulated mitochondrial-related pathways. In comparison, weak responders (5-10% FEV1 increase), and non-responders to imatinib shared none of these pathways. The use of PEEP highlights its potential for application as a systems biology tool to develop individual-level approaches to predicting disease phenotypes and response to treatment in populations needing innovative therapies. These results support a role for mitochondrial pathways in airflow limitation in severe asthma and as potential therapeutic targets in larger clinical trials.

Latent-space embedding of expression data identifies gene signatures from sputum samples of asthmatic patients.

BACKGROUND: The pathogenesis of asthma is a complex process involving multiple genes and pathways. Identifying biomarkers from asthma datasets, especially those that include heterogeneous subpopulations, is challenging. Potentially, autoencoders provide ideal frameworks for such tasks as they can embed complex, noisy high-dimensional gene expression data into a low-dimensional latent space in an unsupervised fashion, enabling us to extract distinguishing features from expression data. RESULTS: Here, we developed a framework combining a denoising autoencoder and a supervised learning classifier to identify gene signatures related to asthma severity. Using the trained autoencoder with 50 hidden units, we found that hierarchical clustering on the low-dimensional embedding corresponds well with previously defined and clinically relevant clusters of patients. Moreover, each hidden unit has contributions from each of the genes, and pathway analysis of these contributions shows that the hidden units are significantly enriched in known asthma-related pathways. We then used genes that contribute most to the hidden units to develop a secondary random-forest classifier for directly predicting asthma severity. The feature importance metric from this classifier identified a signature based on 50 key genes, which are associated with severity. Furthermore, we can use these key genes to successfully estimate FEV1/FVC ratios across patients, via support-vector-machine regression. CONCLUSION: We found that the denoising autoencoder framework can extract meaningful patterns corresponding to functional gene groups and patient clusters from the gene expression of asthma patients.

Shared genetic and experimental links between obesity-related traits and asthma subtypes in UK Biobank.

BACKGROUND: Clinical and epidemiologic studies have shown that obesity is associated with asthma and that these associations differ by asthma subtype. Little is known about the shared genetic components between obesity and asthma. OBJECTIVE: We sought to identify shared genetic associations between obesity-related traits and asthma subtypes in adults. METHODS: A cross-trait genome-wide association study (GWAS) was performed using 457,822 subjects of European ancestry from the UK Biobank. Experimental evidence to support the role of genes significantly associated with both obesity-related traits and asthma through a GWAS was sought by using results from obese versus lean mouse RNA sequencing and RT-PCR experiments. RESULTS: We found a substantial positive genetic correlation between body mass index and later-onset asthma defined by asthma age of onset at 16 years or greater (Rg = 0.25, P = 9.56 × 10-22). Mendelian randomization analysis provided strong evidence in support of body mass index causally increasing asthma risk. Cross-trait meta-analysis identified 34 shared loci among 3 obesity-related traits and 2 asthma subtypes. GWAS functional analyses identified potential causal relationships between the shared loci and Genotype-Tissue Expression (GTEx) quantitative trait loci and shared immune- and cell differentiation-related pathways between obesity and asthma. Finally, RNA sequencing data from lungs of obese versus control mice found that 2 genes (acyl-coenzyme A oxidase-like [ACOXL] and myosin light chain 6 [MYL6]) from the cross-trait meta-analysis were differentially expressed, and these findings were validated by using RT-PCR in an independent set of mice. CONCLUSIONS: Our work identified shared genetic components between obesity-related traits and specific asthma subtypes, reinforcing the hypothesis that obesity causally increases the risk of asthma and identifying molecular pathways that might underlie both obesity and asthma.

Subtypes of asthma based on asthma control and severity: a latent class analysis.

BACKGROUND: Asthma subtyping is a complex new field of study. Usually both etiological and outcome factors of asthma have been used simultaneously for subtyping thus making the interpretation of the results difficult. Identification of subtypes of asthma based on questionnaire data only will be useful for both treatment of asthma and for research. Our objective was to identify asthma subtypes that capture both asthma control and severity based on easily accessible variables. METHODS: We applied latent class analysis for the 1995 adult asthmatics, 692 men and 1303 women, of the Northern Finnish Asthma Study (NoFAS). The classifying variables included use of asthma medication within the last 12 months, St. George's Respiratory Questionnaire score, and asthma-related healthcare use within the last 12 months. Covariates adjusted for included COPD, allergic rhinitis/allergic eczema, BMI, age and sex. All information was based on self-administered questionnaires. RESULTS: We identified four subtypes for women: Controlled, mild asthma (41% of participants); Partly controlled, moderate asthma (24%); Uncontrolled asthma, unknown severity (26%), and Uncontrolled, severe asthma (9%). For men we identified three subtypes: Controlled, mild asthma (31%); Poorly controlled asthma, unknown severity (53%); and Partly controlled, severe asthma (17%). For almost 96% of the subjects this subtyping was accurate. The covariates fitted in the model were based on clinical judgment and were good predictors of class membership. CONCLUSIONS: Our results show that it is possible to form meaningful and accurate asthma subtypes based on questionnaire data, and that separate classification should be applied for men and women.

Clinical asthma phenotyping: A trial for bridging gaps in asthma management.

Asthma is a common disease affecting millions of people worldwide and exerting an enormous strain on health resources in many countries. Evidence is increasing that asthma is unlikely to be a single disease but rather a series of complex, overlapping individual diseases or phenotypes, each defined by its unique interaction between genetic and environmental factors. Asthma phenotypes were initially focused on combinations of clinical characteristics, but they are now evolving to link pathophysiological mechanism to subtypes of asthma. Better characterization of those phenotypes is expected to be most useful for allocating asthma therapies. This article reviews different published researches in terms of unbiased approaches to phenotype asthma and emphasizes how the phenotyping exercise is an important step towards proper asthma treatment. It is structured into three sections; the heterogeneity of asthma, the impact of asthma heterogeneity on asthma management and different trials for phenotyping asthma.