Asthma endotypes: A new approach to classification of disease entities within the asthma syndrome

Lötvall, Jan; Akdiş, Cezmi A.; Bacharier, Leonard B.; Bjermer, Leif; Casale, Thomas B.; Čustović, Adnan; Lemanske, Robert F.; Wardlaw, Andrew J.; Wenzel, Sally E.; Greenberger, Paul A.

doi:10.1016/j.jaci.2010.11.037

Cited by 991 publications

(808 citation statements)

References 24 publications

(19 reference statements)

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“…Asthma endotypes, however, describe disease subtypes based on cellular and molecular mechanisms, including the reactivity of structural cells. Understanding these events would allow us to understand and classify asthma endotypes by the use of biomarkers from body fluids and/or affected tissues (1). Endotyping asthma based on disease mechanisms could eventually lead to an individualized management (2,3).…”

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confidence: 99%

Untangling asthma phenotypes and endotypes

Agache

Akdiş

Jutel

et al. 2012

Allergy

283

235

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Asthma phenotypes have been developed to address the complexities of the disease. However, owing to a lack of longitudinal studies, little is known about the onset as well as the stability of phenotypes. Distinguishing phenotypes with regard to the severity or duration of the disease is essential. A phenotype covers the clinically relevant properties of the disease, but does not show the direct relationship to disease etiology and pathophysiology. Different pathogenetic mechanisms might cause similar asthma symptoms and might be operant in a certain phenotype. These putative mechanisms are addressed by the term 'endotype'. Classification of asthma based on endotypes provides advantages for epidemiological, genetic, and drug-related studies. A successful definition of endotypes should link key pathogenic mechanisms with the asthma phenotype. Thus, the identification of corresponding molecular biomarkers for individual pathogenic mechanism underlying phenotypes or subgroups within a phenotype is important. Whether newly defined asthma endotypes predict the individual course of asthma has to be validated in longitudinal studies. The accurate endotyping reflects natural history of asthma and should help to predict treatment response. Thus, understanding asthma endotypes might be useful in clinical practice.

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confidence: 99%

Untangling asthma phenotypes and endotypes

Agache

Akdiş

Jutel

et al. 2012

Allergy

283

235

View full text Add to dashboard Cite

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“…There is increasing recognition that asthma is a heterogeneous disease with multiple disease variants, which may have a similar clinical presentation, but differ in their etiology and pathogenesis (2,3). It is likely that these different asthma subgroups (sometimes referred to as asthma endotypes [3]) have different causative mechanisms, and may require different treatments.…”

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confidence: 99%

Challenges in Identifying Asthma Subgroups Using Unsupervised Statistical Learning Techniques

Prosperi

Şahiner

Belgrave

et al. 2013

Am J Respir Crit Care Med

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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.

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“…2 This new concept might accelerate the proper treatment of asthma. However, the precise clinical significance of asthma endotypes in the management of asthmatic patients is unknown.…”

Section: Letter To the Editormentioning

confidence: 99%

“…Phenotype describes "observable characteristics" with no direct relationship to a disease process including physiology, triggers, and inflammatory parameters. 2 A review article with special reference to asthma phenotypes by Horiguchi 3 summarized two previous articles 4,5 referring to asthma phenotypes. For example, one report by Haldar et al 4 analyzed 184 asthma patients and identified five phenotypes using cluster analysis.…”

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confidence: 99%

Asthma phenotypes: An important step for tailor‐made therapy

Nakamoto

Saraya

Takizawa

2017

J of Gen and Family Med

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Recently, many bronchial asthma patients have achieved good control with inhaled corticosteroid and long-acting beta2-agonists. However, some patients suffer from persistent asthmatic symptoms and have asthma attacks despite treatment with high doses of inhaled corticosteroid. This condition is called severe asthma, refractory asthma, or difficult-to-treat asthma. Severe asthma is a serious socio-medical problem that affects 5%-10% of patients, and the associated medical costs are high. 1

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Asthma endotypes: A new approach to classification of disease entities within the asthma syndrome

Cited by 991 publications

References 24 publications

Untangling asthma phenotypes and endotypes

Untangling asthma phenotypes and endotypes

Challenges in Identifying Asthma Subgroups Using Unsupervised Statistical Learning Techniques

Asthma phenotypes: An important step for tailor‐made therapy

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