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Combining immunoglobulins and cytokine levels could predict allergen-specific immunotherapy effectivenessBiological markers may help predict allergen immunotherapy success

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Key Takeaway
Consider combining immunoglobulins and cytokine levels to predict allergen-specific immunotherapy effectiveness.

This narrative review addresses the challenge of predicting the effectiveness of allergen-specific immunotherapy (AIT) for allergic diseases. The authors explore various biological markers that might serve as predictors for clinical outcomes.

The review proposes that combining immunoglobulins, such as sIgE and tIgE, along with their ratios and cytokine levels like IL-10 and IL-35, could be used to predict clinical efficacy. This approach aims to construct a composite prediction scoring system for improved accuracy.

Furthermore, the authors recommend monitoring changes in sIgE, sIgG4, sIgG2, cellular markers, and cellular functions such as BAT and ECP. These measurements should be implemented to assess patient adherence and guide therapy, as they are closely associated with clinical outcomes.

A key limitation identified is that a standardized, unified panel of biological parameters has not yet been established. The review does not report specific adverse events or discontinuation rates. The findings offer a framework for future research rather than immediate clinical guidelines.

This narrative review examines how biological markers might predict the effectiveness of allergen-specific immunotherapy for allergic diseases. The authors look at combining immunoglobulins, such as sIgE and tIgE, along with their ratios and cytokine levels like IL-10 and IL-35. They suggest these markers could help build a composite prediction scoring system to improve accuracy in forecasting clinical outcomes.

The review also highlights the importance of monitoring changes in specific markers over time. Tracking sIgE, sIgG4, sIgG2, cellular markers, and cellular functions can help assess patient adherence and guide therapy decisions. These biological measures are closely associated with clinical results during long-term treatment.

The study notes a key limitation: a standardized, unified panel of biological parameters has not yet been established. Because this review is narrative and does not report specific adverse events or discontinuations, readers should view these findings as a framework for future research rather than immediate clinical proof. Combining these markers may eventually improve how doctors tailor treatment for allergic conditions.

What this means for you:
Biological markers may help predict allergen immunotherapy success and guide therapy for allergic diseases.

Study Details

Study typeSystematic review
EvidenceLevel 1
PublishedJun 2026
View Original Abstract ↓
Allergen-specific immunotherapy (AIT) is a disease-modifying therapeutic approach that addresses the fundamental etiology of allergic diseases by inducing immune tolerance through modulation of the immune system. Nevertheless, assessing AIT efficacy necessitates long-term treatment, underscoring the need for reliable predictive biomarkers. This review summarizes the recent advances in biomarker research for predicting the effectiveness of AIT, including immunoglobulins, cellular parameters, functional cytokine assays, and provocation tests. This study aims to predict the efficacy of AIT treatment early by classifying and evaluating biomarkers. Combining immunoglobulins, such as sIgE and tIgE, and their ratios, along with cytokine levels (e.g., IL-10, IL-35), could be used to predict clinical efficacy and to construct a composite prediction scoring system for improved accuracy. Monitoring of changes in sIgE, sIgG4, sIgG2, cellular markers, and cellular functions (e.g., BAT, ECP, cytokines) should be implemented to assess patient adherence and guide therapy, as they are closely associated with clinical outcomes. Although multiple biomarkers show promising potential, a standardized, unified panel of biological parameters has not yet been established. Future research should integrate multi-omics technologies, combine provocation tests with clinical evaluations, and develop accurate predictive models and more reliable, stable biomarkers for the evaluation of AIT efficacy.
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