Login / Signup

Allergen immunotherapy for IgE-mediated food allergy: There is a measure in everything to a proper proportion of therapy.

Giovanni Battista PajnoRiccardo CastagnoliAntonella MuraroMontserrat Alvaro-LozanoCezmi A AkdisMűbeccel AkdisStefania Arasi
Published in: Pediatric allergy and immunology : official publication of the European Society of Pediatric Allergy and Immunology (2019)
IgE-mediated food allergy (FA) is a potentially life-threatening condition with a negative impact on quality of life and an increasing prevalence in westernized countries in the recent two decades. A strict avoidance of the triggering food(s) represents the current standard approach. However, an elimination diet may be difficult and frustrating, in particular for common foods, (eg, milk, egg, and peanut). Food allergy immunotherapy (FA-AIT) may provide an active treatment that enables to increase the amount of food that the patient can intake without reaction during treatment (ie, desensitization), and reduces the risk of potential life-threatening allergic reaction in the event of accidental ingestion. However, several gaps need still to be filled. A memorable Latin orator stated: "Est modus in rebus" (Horace, Sermones I, 1, 106-07). This sentence remembers that there is a measure in everything to a proper proportion of therapy. The common sense of measure should find application in each stage of treatment. A personalized approaching should consider the specific willing and features of each patient. Efforts are devoted to improve the efficacy, the safety but also the quality of life of patients suffering from FA. In the near future, it will be important to clarify immunologic pathways of FA-AIT, and to identify reliable biomarkers in order to recognize the most suitable candidates to FA-AIT and algorithms for treatments tailored on well-characterized subpopulations of patients.
Keyphrases
  • end stage renal disease
  • ejection fraction
  • newly diagnosed
  • chronic kidney disease
  • peritoneal dialysis
  • machine learning
  • risk assessment
  • deep learning
  • human health
  • weight loss
  • current status
  • weight gain