Early Detection of Asthma: Exploring Inflammatory Biomarkers in Symptomatic Adults with Normal Spirometry
Notice bibliographique
Résumé
Introduction: We previously showed that individuals without a prior history of asthma, presenting with unexplained respiratory symptoms and normal spirometry, may exhibit airway hyperresponsiveness and underlying eosinophilic (T2) inflammation, features suggestive of undiagnosed early-stage asthma. Improving our understanding of the inflammatory processes that contribute to asthma onset is essential, as it may ultimately lead to earlier detection, timely intervention and improved long-term outcomes. Purpose: This study aimed to evaluate several key inflammatory biomarkers in this well-characterized population and examine their associations with clinical presentation to identify early signs of asthma. Patients and Methods: This retrospective, observational cohort sub-study included Canadian adults with respiratory symptoms and normal pre- and post-bronchodilator spirometry. Demographics and clinical data were extracted from study files. Plasma and serum levels of biomarkers associated with T2 airway inflammation and epithelial shedding, including IL-4, IL-5, IL-13, IL-25, IL-33, eotaxin, eotaxin-3, TARC, periostin and TNF-α, were measured using ELISA and multiplex electrochemiluminescent assays. Airway hyperresponsiveness was defined as a PC 20 < 16 mg/mL, and T2 airway inflammation as sputum eosinophils > 2% and/or FeNO > 25 ppb. Results: Among 128 adults (mean age ±SD: 58.0 ± 13.9 years, 52% women), 45 (35%) had T2 airway inflammation. Most biomarker levels were low or undetectable, with substantial inter-individual variability. No significant differences in biomarker levels were observed between individuals with and without airway hyperresponsiveness or T2 airway inflammation. Eotaxin levels negatively correlated with post-bronchodilator FEV 1 /FVC ratio (r=− 0.18, P=0.0433), and eotaxin-3 positively correlated with FeNO (r=0.18, P=0.0482). Conclusion: This panel of clinically accessible T2 biomarkers may not reliably reflect early pathophysiological signs of asthma in symptomatic adults with normal spirometry. Longitudinal follow-up of this cohort, along with the integration of airway sampling, may provide further insight into the role of these biomarkers in asthma development and progression. Plain Language Summary: Asthma is a common lung disease characterized by inflammation in the airways. Although inflammation is the body’s natural response to harmful triggers, in asthma it becomes exaggerated and persistent. Over time, this can lead to structural and functional changes in the airways, causing symptoms like coughing, wheezing, and shortness of breath. Diagnosing asthma in its early stages can be difficult, as symptoms are often mild and lung function tests may appear normal. As a result, asthma may go undetected, delaying treatment, and increasing the risk of severe respiratory events and hospitalizations. Certain molecules in the blood, called “biomarkers”, can reflect the biological processes involved in asthma, especially in more advanced stages. However, it is unclear whether these same biomarkers can help detect asthma earlier in the process—before it becomes detectable through standard lung function tests. In this study, we measured several inflammatory biomarkers in the blood, including key molecules like IL-4, IL-5, eotaxin, eotaxin-3, and others, and examined their relationship with clinical presentation in adults with unexplained respiratory symptoms and normal lung function tests results. Most biomarkers were either found at low levels or not detected at all. We found no clear differences in biomarker levels between individuals with and without signs of airway inflammation. Only two biomarkers, eotaxin and eotaxin-3, showed weak but statistically significant associations with certain clinical parameters. These findings suggest that blood tests using this panel of biomarkers may not reliably detect early signs of asthma in this at-risk population. Keywords: asthma, diagnosis, airway inflammation, biomarkers, cytokines, chemokines
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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 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».