Notice bibliographique
Résumé
Asthma remains a treatable yet incurable disease of the airways, or more appropriately recognized, it is a syndrome that encompasses different disease entities2. An asthmatic individual may differ from another asthmatic individual in terms of symptoms, severity, clinical characteristics, histological features, genetic susceptibility and treatment responses. The term heterogeneity refers to the non-uniformity and variability in the disease. Variability in treatment responses has great clinical and economical implications. Inhaled corticosteroid is the most commonly prescribed medication to treat asthma symptoms, however, it is ineffective in the more severe forms of the disease. It has been estimated that about 60% of forced expiratory volume in 1 second response variability to β2-agonists and leukotriene modifiers are attributable to inter-individual variability, and that medications account for 38–89% of the total cost for asthma 3, 4. Traditionally asthma has been studied and considered as an eosinophilic inflammatory disease with an underlying T-helper cell type 2 (Th2) immune response. However, the realization that it is a heterogeneous disease has prompted the search for the ‘hidden’ subgroups of asthma. Initially, attempts were made to categorize asthma as atopic vs. non-atopic, intrinsic vs. extrinsic, eosinophilic vs. neutrophilic asthma or childhood onset vs. adult onset based on observable characteristics. Individuals with similar observable disease characteristics are considered to have the same phenotype and are conventionally treated similarly. More recently, efforts have been made to stratify asthmatic individuals of similar severity, as defined by various international guidelines, into different subgroups based on lung function measurements and disease pathophysiology 5-8. For example, Wenzel et al. were able to further divide 34 refractory asthmatic individuals into two inflammatory subtypes of eosinophilic and non-eosinophilic, with each group exhibited distinct physiological and clinical characteristics 8. In another study consisted of 80 severe asthmatic individuals, Miranda et al. further classified the subjects based on age-of-onset and the presence of eosinophilic inflammation 6. In a study of 84 asthmatic individuals with a range of severity, Brasier et al. carried out an unsupervised clustering analysis based on cytokine profile in bronchoalveolar lavage (BAL), and identified 4 groups which differed in BAL cellularity, baseline and post-treatment lung function 9. Lastly, Moore et al. conducted a large-scale unsupervised cluster analysis with 726 asthmatic subjects and 34 parameters 7. Five groups were identified which differed in atopy status, age-of-onset, medication usage, airflow obstruction and bronchodilator responsiveness. All these stratification efforts clearly demonstrated that (1) asthma is not a single disease, and (2) different distinct subclasses of asthma have different disease mechanisms underlying the pathogenesis. If different subclasses of asthma are manifestation of different disease mechanisms, then the most accurate way to distinguish them is based on disease mechanism. This concept of classifying asthma based on disease mechanism was first proposed by Anderson GP in his asthma endotype model 10. The term endotype or endophenotype refers to a subtype of a condition defined by distinct biopathological mechanism. Lotvall et al. further described 7 plausible asthma endotypes based on disease characteristics (clinical characteristics, biomarkers, lung physiology, genetics, histopathology, epidemiology and treatment response), and proposed a dominant disease mechanism for each endotype 11. The identification of asthma endotypes has tremendous clinical implication given the wide variability in treatment response. At present the majority of the stratification efforts were aimed at adult asthmatic individuals, and few have performed for children 12-14. In a study of 161 asthmatic children between ages 6–17 years and of difficult-to-treat asthma, Fitzpatrick et al. conducted an unsupervised clustering analysis with 12 variables and identified 4 groups which differed in age-of-onset, asthma duration, comorbidities, atopic status, allergen sensitization, medication use, health care use, lung function and exhaled nitric oxide concentration. In a study of 307 children of asthmatic mother, Bisgaard et al. classified children based on symptom frequency and age-of-onset in the first 6 years of life and identified 4 groups (asymptomatic, transient early troublesome lung symptoms, persistent troublesome lung symptoms and late-onset troublesome lung symptoms) 12. In this issue of the journal, Meinmayr et al. reported their efforts in stratifying school-aged children who participated in the Spanish sites of the International Study of Asthma and Allergic Childhood (ISAAC)-Phase Two1. By using a statistical approach of latent class analysis, which groups data points described by multiple categorical variables into discrete classes, Meinmayr et al. clustered 3890, both asthmatic and non-asthmatic, children between the ages of 8–12 years into 7 classes based on 11 respiratory symptoms. They labelled the classes: no respiratory symptoms (class 1), cough during colds (class 2), chronic cough and phlegm (class 3), nocturnal breathiness (class 4), wheeze only with colds (class 5), wheeze without colds, with cough (class 6) and wheeze without colds, without cough (class 7). Meinmayr et al. further associated the 7 identified classes with objective markers of allergic disease (allergic sensitization, lung function and bronchial hyperresponsiveness), demographic characteristics, clinical symptoms of rhinitis, examined eczema, parental history of asthma and asthma severity. The overall finding from the association is that children who wheezed (classes 5, 6, and 7) tended to have a higher risk of asthma, rhinitis symptoms, atopic, use of inhaled corticosteroid and bronchodilator response than children without wheeze. These data are demonstrating that in children, as in adults, there are different asthma entities, likely with different underlying disease mechanisms. For example, elevated levels of total and specific IgE and an increase risk for asthma in the wheezing groups (classes 5, 6, and 7) imply that these children are likely to have a Th2 immune response mediated by interleukin (IL)-4 and IL-13, with a lesser degree in class 5 children 15. Children in class 4 have a comparable asthma risk to that of class 5, yet tended to be atopic, hence these children may have an IL-5-mediated Th2 immune response 15. The stratification performed by Meinmayr et al. have great clinical implications. The clustering was performed exclusively on respiratory symptoms documented in a widely used international questionnaire in a cross-sectional setting, hence highlighting the practicality of the method. With better understandings of the underlying disease mechanism for each class, more effective treatment schemes can be designed based on respiratory symptoms alone, thus avoiding invasive testing, ineffective treatments and unnecessary side-effects in children who suffer from asthma. Conflict of interests: The author has declared no conflict of interests.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,016 | 0,016 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».