Asthma phenotyping, therapy, and prevention: what can we learn from systems biology?

The Role of Systems Biology in Deciphering Asthma Heterogeneity

Asthma is one of the most common and lifelong and chronic inflammatory diseases characterized by inflammation, bronchial hyperresponsiveness, and airway obstruction episodes. It is a heterogeneous disease of varying and overlapping phenotypes with many confounding factors playing a role in disease susceptibility and management. Such multifactorial disorders will benefit from using systems biology as a strategy to elucidate molecular insights from complex, quantitative, massive clinical, and biological data that will help to understand the underlying disease mechanism, early detection, and treatment planning. Systems biology is an approach that uses the comprehensive understanding of living systems through bioinformatics, mathematical, and computational techniques to model diverse high-throughput molecular, cellular, and the physiologic profiling of healthy and diseased populations to define biological processes. The use of systems biology has helped understand and enrich our knowledge of asthma heterogeneity and molecular basis; however, such methods have their limitations. The translational benefits of these studies are few, and it is recommended to reanalyze the different studies and omics in conjugation with one another which may help understand the reasons for this variation and help overcome the limitations of understanding the heterogeneity in asthma pathology. In this review, we aim to show the different factors that play a role in asthma heterogeneity and how systems biology may aid in understanding and deciphering the molecular basis of asthma.

Artificial Intelligence and Machine Learning in Chronic Airway Diseases: Focus on Asthma and Chronic Obstructive Pulmonary Disease

Chronic airway diseases are characterized by airway inflammation, obstruction, and remodeling and show high prevalence, especially in developing countries. Among them, asthma and chronic obstructive pulmonary disease (COPD) show the highest morbidity and socioeconomic burden worldwide. Although there are extensive guidelines for the prevention, early diagnosis, and rational treatment of these lifelong diseases, their value in precision medicine is very limited. Artificial intelligence (AI) and machine learning (ML) techniques have emerged as effective methods for mining and integrating large-scale, heterogeneous medical data for clinical practice, and several AI and ML methods have recently been applied to asthma and COPD. However, very few methods have significantly contributed to clinical practice. Here, we review four aspects of AI and ML implementation in asthma and COPD to summarize existing knowledge and indicate future steps required for the safe and effective application of AI and ML tools by clinicians.

Systems biology and big data in asthma and allergy: recent discoveries and emerging challenges

Asthma is a common condition caused by immune and respiratory dysfunction, and it is often linked to allergy. A systems perspective may prove helpful in unravelling the complexity of asthma and allergy. Our aim is to give an overview of systems biology approaches used in allergy and asthma research. Specifically, we describe recent "omic"-level findings, and examine how these findings have been systematically integrated to generate further insight.Current research suggests that allergy is driven by genetic and epigenetic factors, in concert with environmental factors such as microbiome and diet, leading to early-life disturbance in immunological development and disruption of balance within key immuno-inflammatory pathways. Variation in inherited susceptibility and exposures causes heterogeneity in manifestations of asthma and other allergic diseases. Machine learning approaches are being used to explore this heterogeneity, and to probe the pathophysiological patterns or "endotypes" that correlate with subphenotypes of asthma and allergy. Mathematical models are being built based on genomic, transcriptomic and proteomic data to predict or discriminate disease phenotypes, and to describe the biomolecular networks behind asthma.The use of systems biology in allergy and asthma research is rapidly growing, and has so far yielded fruitful results. However, the scale and multidisciplinary nature of this research means that it is accompanied by new challenges. Ultimately, it is hoped that systems medicine, with its integration of omics data into clinical practice, can pave the way to more precise, personalised and effective management of asthma.

Small, Prospective, Observational, Pilot Study in Patients with Severe Asthma after Discontinuation of Omalizumab Treatment

PURPOSE

Omalizumab has demonstrated clinical efficacy in severe allergic asthma by reducing exacerbation rates and increasing quality of life. However, data concerning its sustained effect after treatment discontinuation are still needed.

METHODS

This analysis was an observational pilot study (simple within-subjects design) of 12 patients experiencing severe asthma, treated with omalizumab, for 1 year after treatment discontinuation. We prospectively analyzed clinical measurements (pulmonary functions, inhaled corticosteroid [ICS] doses, Asthma Control Test [ACT] scores, skin prick test [SPT] positivity, fraction of exhaled nitric oxide, and exacerbation rates) and laboratory test results (eosinophils and total immunoglobulin E levels) at the time of discontinuation and 6 and 12 months thereafter. Baseline data (before the treatment period; range, 11-61 months) were collected retrospectively. The treatment effect until discontinuation was calculated. To determine its persistence, repeated measures were compared with baseline levels and analyzed by using a general linear model for repeated measures or the Friedman ANOVA, and χ² tests in case of normality assumption violation or frequencies. Post hoc analysis was applied by using a simple or repeated contrasts analysis or Wilcoxon signed rank test with Bonferroni correction.

FINDINGS

We proved a significant reduction in ICS doses and SPT reactivity and an increase in ACT score during the retrospective treatment phase. Moreover, persistence of these statistically significant effects was recorded 6 months after treatment discontinuation. ACT score and ICS doses (but not SPT reactivity) remained improved for 12 months after discontinuation of omalizumab treatment.

IMPLICATIONS

Omalizumab treatment exhibited sustained treatment benefit after its discontinuation for patients experiencing severe allergic asthma.

Models and Data Sources Used in Systems Medicine

Background: Systems medicine is a new approach for the development and selection of treatment strategies for patients with complex diseases. It is often referred to as the application of systems biology methods for decision making in patient care. For systems medicine computer applications, many different data sources have to be integrated and included into models. This is a challenging task for Medical Informatics since the approach exceeds traditional systems like Electronic Health Records. To prioritize research activities for systems medicine applications, it is necessary to get an overview over modelling methods and data sources already used in this field.

Objectives: We performed a systematic literature review with the objective to capture current use of 1) modelling methods and 2) data sources in systems medicine related research projects.

Methods: We queried the MEDLINE and ScienceDirect databases for papers associated with the search term systems medicine and related terms. Papers were screened and assessed in full text in a two-step process according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement guidelines.

Results: The queries returned 698 articles of which 34 papers were finally included into the study. A multitude of modelling approaches such as machine learning and network analysis was identified and classified. Since these approaches are also used in other domains, no methods specific for systems medicine could be identified. Omics data are the most widely used data types followed by clinical data. Most studies only include a rather limited number of data sources.

Conclusions: Currently, many different modelling approaches are used in systems medicine. Thus, highly flexible modular solutions are necessary for systems medicine clinical applications. However, the number of data sources included into the models is limited and most projects currently focus on prognosis. To leverage the potential of systems medicine further, it will be necessary to focus on treatment strategies for patients and consider a broader range of data.

The Potential for Emerging Microbiome-Mediated Therapeutics in Asthma

PURPOSE OF REVIEW

In terms of immune regulating functions, analysis of the microbiome has led the development of therapeutic strategies that may be applicable to asthma management. This review summarizes the current literature on the gut and lung microbiota in asthma pathogenesis with a focus on the roles of innate molecules and new microbiome-mediated therapeutics.

RECENT FINDINGS

Recent clinical and basic studies to date have identified several possible therapeutics that can target innate immunity and the microbiota in asthma. Some of these drugs have shown beneficial effects in the treatment of certain asthma phenotypes and for protection against asthma during early life. Current clinical evidence does not support the use of these therapies for effective treatment of asthma. The integration of the data regarding microbiota with technologic advances, such as next generation sequencing and omics offers promise. Combining comprehensive bioinformatics, new molecules and approaches may shape future asthma treatment.

Resolving the etiology of atopic disorders by using genetic analysis of racial ancestry

Atopic dermatitis (AD), food allergy, allergic rhinitis, and asthma are common atopic disorders of complex etiology. The frequently observed atopic march from early AD to asthma, allergic rhinitis, or both later in life and the extensive comorbidity of atopic disorders suggest common causal mechanisms in addition to distinct ones. Indeed, both disease-specific and shared genomic regions exist for atopic disorders. Their prevalence also varies among races; for example, AD and asthma have a higher prevalence in African Americans when compared with European Americans. Whether this disparity stems from true genetic or race-specific environmental risk factors or both is unknown. Thus far, the majority of the genetic studies on atopic diseases have used populations of European ancestry, limiting their generalizability. Large-cohort initiatives and new analytic methods, such as admixture mapping, are currently being used to address this knowledge gap. Here we discuss the unique and shared genetic risk factors for atopic disorders in the context of ancestry variations and the promise of high-throughput "-omics"-based systems biology approach in providing greater insight to deconstruct their genetic and nongenetic etiologies. Future research will also focus on deep phenotyping and genotyping of diverse racial ancestry, gene-environment, and gene-gene interactions.

Asthma Pharmacogenomics: 2015 Update

There is evidence that genetic factors are implicated in the observed differences in therapeutic responses to the common classes of asthma therapy such as β2-agonists, corticosteroids, and leukotriene modifiers. Pharmacogenomics explores the roles of genetic variation in drug response and continues to be a field of great interest in asthma therapy. Prior studies have focused on candidate genes and recently emphasized genome-wide association analyses. Newer integrative omics and system-level approaches have recently revealed novel understanding of drug response pathways. However, the current known genetic loci only account for a fraction of variability in drug response and ongoing research is needed. While the field of asthma pharmacogenomics is not yet fully translatable to clinical practice, ongoing research should hopefully achieve this goal in the near future buttressed by the recent precision medicine efforts in the USA and worldwide.

Association between psoriasis and asthma: a population-based retrospective cohort analysis

BACKGROUND

Both psoriasis and asthma are chronic immune-mediated inflammatory diseases.

OBJECTIVES

To evaluate the risk of developing asthma in patients with psoriasis compared with controls.

METHODS

This cohort study was conducted using data from the Taiwan National Health Insurance Research Database. Patients with psoriasis (n = 10,288) and matched comparison patients without psoriasis (n = 41,152) were evaluated. A Cox proportional hazard regression analysis was used to determine the risk of asthma in patients with and without psoriasis.

RESULTS

The risk of asthma was 1·38-fold higher [95% confidence interval (CI) 1·23-1·54] in the cohort with psoriasis than in the reference cohort, after adjusting for age, sex and comorbidities. The incidence of asthma in men and women with psoriasis exhibited nonsignificant differences. Among all patients aged > 50 years, psoriasis was associated with a higher risk of asthma compared with not having psoriasis [adjusted hazard ratio (HR) 1·49; 95% CI 1·18-1·88 (in patients aged 50-64 years); adjusted HR 1·63; 95% CI 1·34-1·99 (in patients aged > 65 years)].

CONCLUSIONS

Our results indicate that patients with psoriasis are associated with a increased risk of developing asthma.

Characterizing wheeze phenotypes to identify endotypes of childhood asthma, and the implications for future management

It is now a commonly held view that asthma is not a single disease, but rather a set of heterogeneous diseases sharing common symptoms. One of the major challenges in treating asthma is understanding these different asthma phenotypes and their underlying biological mechanisms. This review gives an epidemiological perspective of our current understanding of the different phenotypes that develop from birth to childhood that come under the umbrella term 'asthma'. The review focuses mainly on publications from longitudinal birth cohort studies where the natural history of asthma symptoms is observed over time in the whole population. Identifying distinct pathophysiological mechanisms for these different phenotypes will potentially elucidate different asthma endotypes, ultimately leading to more effective treatment and management strategies.

Predicting phenotypes of asthma and eczema with machine learning

Background

There is increasing recognition that asthma and eczema are heterogeneous diseases. We investigated the predictive ability of a spectrum of machine learning methods to disambiguate clinical sub-groups of asthma, wheeze and eczema, using a large heterogeneous set of attributes in an unselected population. The aim was to identify to what extent such heterogeneous information can be combined to reveal specific clinical manifestations.

Methods

The study population comprised a cross-sectional sample of adults, and included representatives of the general population enriched by subjects with asthma. Linear and non-linear machine learning methods, from logistic regression to random forests, were fit on a large attribute set including demographic, clinical and laboratory features, genetic profiles and environmental exposures. Outcome of interest were asthma, wheeze and eczema encoded by different operational definitions. Model validation was performed via bootstrapping.

Results

The study population included 554 adults, 42% male, 38% previous or current smokers. Proportion of asthma, wheeze, and eczema diagnoses was 16.7%, 12.3%, and 21.7%, respectively. Models were fit on 223 non-genetic variables plus 215 single nucleotide polymorphisms. In general, non-linear models achieved higher sensitivity and specificity than other methods, especially for asthma and wheeze, less for eczema, with areas under receiver operating characteristic curve of 84%, 76% and 64%, respectively. Our findings confirm that allergen sensitisation and lung function characterise asthma better in combination than separately. The predictive ability of genetic markers alone is limited. For eczema, new predictors such as bio-impedance were discovered.

Conclusions

More usefully-complex modelling is the key to a better understanding of disease mechanisms and personalised healthcare: further advances are likely with the incorporation of more factors/attributes and longitudinal measures.

Challenges in identifying asthma subgroups using unsupervised statistical learning techniques

RATIONALE

Unsupervised statistical learning techniques, such as exploratory factor analysis (EFA) and hierarchical clustering (HC), have been used to identify asthma phenotypes, with partly consistent results. Some of the inconsistency is caused by the variable selection and demographic and clinical differences among study populations.

OBJECTIVES

To investigate the effects of the choice of statistical method and different preparations of data on the clustering results; and to relate these to disease severity.

METHODS

Several variants of EFA and HC were applied and compared using various sets of variables and different encodings and transformations within a dataset of 383 children with asthma. Variables included lung function, inflammatory and allergy markers, family history, environmental exposures, and medications. Clusters and original variables were related to asthma severity (logistic regression and Bayesian network analysis).

MEASUREMENTS AND MAIN RESULTS

EFA identified five components (eigenvalues ≥ 1) explaining 35% of the overall variance. Variations of the HC (as linkage-distance functions) did not affect the cluster inference; however, using different variable encodings and transformations did. The derived clusters predicted asthma severity less than the original variables. Prognostic factors of severity were medication usage, current symptoms, lung function, paternal asthma, body mass index, and age of asthma onset. Bayesian networks indicated conditional dependence among variables.

CONCLUSIONS

The use of different unsupervised statistical learning methods and different variable sets and encodings can lead to multiple and inconsistent subgroupings of asthma, not necessarily correlated with severity. The search for asthma phenotypes needs more careful selection of markers, consistent across different study populations, and more cautious interpretation of results from unsupervised learning.