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MDPI, International Journal of Molecular Sciences, 9(23), p. 4979, 2022

DOI: 10.3390/ijms23094979

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Basophil Activation Test Utility as a Diagnostic Tool in LTP Allergy

This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

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Abstract

Plant-food allergy is an increasing problem, with nonspecific lipid transfer proteins (nsLTPs) triggering mild/severe reactions. Pru p 3 is the major sensitizer in LTP food allergy (FA). However, in vivo and in vitro diagnosis is hampered by the need for differentiating between asymptomatic sensitization and allergy with clinical relevance. The basophil activation test (BAT) is an ex vivo method able to identify specific IgE related to the allergic response. Thus, we aimed to establish the value of BAT in a precise diagnosis of LTP-allergic patients. Ninety-two individuals with peach allergy sensitized to LTP, Pru p 3, were finally included, and 40.2% of them had symptoms to peanut (n = 37). In addition, 16 healthy subjects were recruited. BAT was performed with Pru p 3 and Ara h 9 (peanut LTP) at seven ten-fold concentrations, and was evaluated by flow cytometry, measuring the percentage of CD63 (%CD63+) and CD203c (%CD203chigh) cells, basophil allergen threshold sensitivity (CD-Sens), and area under the dose–response curve (AUC). Significant changes in BAT parameters (%CD63+ and %CD203chigh) were found between the controls and patients. However, comparisons for %CD63+, %CD203chigh, AUC, and CD-Sens showed similar levels among patients with different symptoms. An optimal cut-off was established from ROC curves, showing a significant positive percentage of BAT in patients compared to controls and great values of sensitivity (>87.5%) and specificity (>85%). In addition, BAT showed differences in LTP-allergic patients tolerant to peanut using its corresponding LTP, Ara h 9. BAT can be used as a potential diagnostic tool for identifying LTP allergy and for differentiating peanut tolerance, although neither reactivity nor sensitivity can distinguish the severity of the clinical symptoms.