https://orcid.org/0000-0001-8749-9972
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ABSTRACT
Introduction
A precise diagnosis is key for the optimal management of allergic diseases and asthma. In vivo or in vitro diagnostic methods that use allergen extracts often fail to identify the molecules eliciting the allergic reactions.
Areas covered
Component-resolved diagnosis (CRD) has solved most of the limitations of extract-based diagnostic procedures and is currently valuable tool for the precision diagnosis in the allergy clinic, for venom and food allergy, asthma, allergic rhinitis, and atopic dermatitis. Its implementation in daily practice facilitates: a) the distinction between genuine multiple sensitizations and cross-reactive sensitization in polysensitized patients; b) the prediction of a severe, systemic reaction in food or insect venom allergy; c) the optimal selection of allergen immunotherapy based on the patient sensitization profile. This paper describes its main advantages and disadvantages, cost-effectiveness and future perspectives.
Expert opinion
The diagnostic strategy based on CRD is part of the new concept of precision immunology, which aims to improve the management of allergic diseases.
Article highlights
More complex sensitization patterns are associated with more severe allergic diseases.
Component-resolved diagnosis (CRD) allows clinicians to distinguish between genuine multiple sensitizations and cross-reactive sensitization in polysensitized patients, to assess the risk of severe, systemic reactions in food and venom allergy, and to select adequate allergen immunotherapy based on the patient sensitization profile.
There are several types of singleplex and multiplex platforms using recombinant or purified natural molecular allergens, each with advantages and disadvantages.
CRD can be used as part of precision diagnosis in the allergy clinic for asthma, allergic rhinitis, atopic dermatitis, venom and food allergy.
The vast information provided by CRD needs an unbiased structured approach to be adequately interpreted facilitated by the machine learning models.
Abbreviations
| ABPA | = | allergic bronchopulmonary aspergillosis |
| AD | = | atopic dermatitis |
| AIT | = | allergen immunotherapy |
| ALEX | = | Allergy Explorer |
| AR | = | allergic rhinitis |
| CCD | = | cross-reactive carbohydrate determinants |
| CRD | = | component resolved diagnosis |
| EAACI | = | European Academy of Allergy and Clinical Immunology |
| FABER | = | patient-Friendly Allergen nano-Bead aRray |
| HDM | = | house dust mite |
| ImmunoCAP ISAC | = | ImmunoCAP Immuno Solid-Phase Allergen Chip |
| LTP | = | lipid transfer protein |
| MADX | = | Macro Array Diagnostics |
| OAS | = | oral allergy syndrome |
| OFC | = | oral food challenge |
| PR-10 | = | pathogenesis-related 10 protein |
| QALY | = | quality-adjusted life year |
| sBT | = | serum baseline tryptase |
| sIgE | = | specific immunoglobulin E |
| SPT | = | skin prick test |
| WDEIA | = | wheat-dependent exercise-induced anaphylaxis |
Conflict of Interest
The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.