Obesity and Asthma: Implementing a Treatable Trait Care Model.

Recognition of obesity as a treatable trait of asthma, impacting its development, clinical presentation and management, is gaining widespread acceptance. Obesity is a significant risk factor and disease modifier for asthma, complicating treatment. Epidemiological evidence highlights that obese asthma correlates with poorer disease control, increased severity and persistence, compromised lung function and reduced quality of life. Various mechanisms contribute to the physiological and clinical complexities observed in individuals with obesity and asthma. These encompass different immune responses, including Type IVb, where T helper 2 cells are pivotal and driven by cytokines like interleukins 4, 5, 9 and 13, and Type IVc, characterised by T helper 17 cells and Type 3 innate lymphoid cells producing interleukin 17, which recruits neutrophils. Additionally, Type V involves immune response dysregulation with significant activation of T helper 1, 2 and 17 responses. Finally, Type VI is recognised as metabolic-induced immune dysregulation associated with obesity. Body mass index (BMI) stands out as a biomarker of a treatable trait in asthma, readily identifiable and targetable, with significant implications for disease management. There exists a notable gap in treatment options for individuals with obese asthma, where asthma management guidelines lack specificity. For example, there is currently no evidence supporting the use of incretin mimetics to improve asthma outcomes in asthmatic individuals without Type 2 diabetes mellitus (T2DM). In this review, we advocate for integrating BMI into asthma care models by establishing clear target BMI goals, promoting sustainable weight loss via healthy dietary choices and physical activity and implementing regular reassessment and referral as necessary.

Component-resolved diagnosis in childhood and prediction of asthma in early adolescence: A birth cohort study.

INTRODUCTION: Component-resolved diagnosis (CRD) has been decisive in exploring the mechanisms of IgE sensitization, but the predictive ability to detect asthma has not been addressed. We aim to develop and evaluate the performance of a personalized predictive algorithm for asthma that integrates information on allergic sensitization using CRD. METHODS: One thousand one hundred one twenty-five children from the Generation XXI birth cohort were randomly selected to perform a screening test for allergic sensitization and a subsample was characterized using CRD against 112 allergen components. Allergen components were analyzed using volcano plots and partial least squares (PLS) analysis. Logistic regression was performed to assess the associations between the obtained latent components (LC) and allergic outcomes (asthma, rhinitis, eczema) including other potential predictors used in previous asthma risk scores. The accuracy of the model in predicting asthma was assessed using Receiver Operating Characteristic (ROC) curve statistics. RESULTS: In the PLS, the first LC was positively associated with asthma, rhinitis, and eczema. This LC was mainly driven by positive weights for Der p 1/2/23, Der f 1/2, and Fel d 1. The main components in the second LC were pollen and food allergens. History of early wheezing and parental allergy were included in the predictive model and the area under the curve improved to 0.82. CONCLUSIONS: This is the first approach to improve the clinical applicability of CRD by combining CRD and clinical data to predict asthma at 13 years. Sensitization to distinct allergen molecules seems relevant to improve the accuracy of asthma prediction models.