Peanut sensitization does not necessarily indicate a clinical peanut allergy, and uncertainty about whether or not a true peanut allergy exists can lead to increased anxiety and decreased quality of life for patients and their families. The gold standard for clinical diagnosis of peanut allergy is an oral food challenge, but this method is time-consuming and can cause severe allergic reactions. Therefore, it would be beneficial to develop a tool to predict clinical peanut allergy in peanut-sensitized individuals whose peanut allergy status is unknown to better determine who requires an oral food challenge for diagnosis.
Two separate studies were conducted. In Study 1, we recruited 100 participants from the McMaster University allergy clinic and community allergy outpatient clinics in the greater Hamilton area. We examined 18 different participant variables and used univariate and multivariate logistic regression analysis to determine how well these variables, alone and in combination, were able to predict clinical peanut allergy status.
In Study 2, we performed a retrospective chart review of a second cohort of 194 participants to investigate the reproducibility of our findings. This was a matched case-control study in which 97 peanut-allergic participants were matched for sex and age with 97 non-allergic control participants.
- Skin prick test measurements
The forearm was prepped with alcohol and peanut extract (ALK-Pharmaceuticals, Mississauga, ON, Canada) was applied to the skin of the dorsal forearm. A sterile metal lancet (HollisterStier, Spokane, WA, USA) was used to pierce the skin below the allergen droplet. Wheal size was measured by skin prick test after 15 min. The tape was placed on the dorsal forearm and the outline of the wheel was traced. Two different study nurses measured the widest diameter of the wheel.
- Plasma measurements of total and peanut IgE
Total IgE was measured with Image 800 (Beckman Coulter, Mississauga, ON, Canada) and peanut-specific IgE antibodies were measured with the Phadia 250 (Thermo Scientific, Waltham, MA, USA).
- Cytokine measurements
Mononuclear cells were isolated from 30 to 40 ml of blood by density gradient centrifugation after lysing red blood cells with AKC lysis buffer. Cells were resuspended in RPMI supplemented with 10% FBS, 1% L-glutamine, 1% penicillin/streptomycin, 55 µM 2-mercaptoethanol (Thermo Scientific), 1 mM sodium pyruvate, 10 mM HEPES, and 0 MEM NEAA. 0.1 mM (Thermo Scientific). Scientific).
Viable cells were counted by Trypan Blue exclusion (Thermo Scientific) and resuspended at 8 × 10 6 cells/mL. 125,000 live cells per well were cultured in triplicate in medium alone or with 50 µg/mL/well crude peanut extract in flat-bottom 96-well plates (BD Biosciences, Mississauga, ON, Canada). After 5 days of culture at 37°C and 5% CO2, triplicates were pooled, centrifuged, and cell-free supernatants were collected and stored at -80°C until further analysis. Cytokines in cell-free supernatants were quantified using Luminex (Millipore Canada Ltd, Etobicoke, ON, Canada) following the manufacturer’s instructions.
- Statistic analysis
Each predictor was entered into a univariate logistic regression analysis to determine if it was associated with the primary outcome: peanut allergy clinical status. We then generated cumulative models composed of multiple predictors using multivariable logistic regression. All univariate and multivariate analyzes included all 69 study participants from Groups 1, 2, and 4.
For all models, parameter estimates were obtained for each predictor and expressed as odds ratios with corresponding 95% confidence intervals and associated p-values. p-values are reported to 4 decimal places.
Hierarchical models were compared to determine if the model with the greater number of predictors was statistically significantly better at predicting the primary outcome than the model with fewer predictors. This was done by comparing the −2 Log-Likelihood statistics of the models. For each model, the area under the receiver operating characteristic (ROC) curve was reported as a measure of discriminability.
The best model was used to predict the peanut allergy status of Group 3 participants and to determine the predicted probability (Pr) that each participant had a clinical peanut allergy. Using Pr, we classify each individual as peanut allergic or not, based on a specific cutoff point. We chose this cutoff point to eliminate false negatives and maximize true positives in the data set.
Peanut skin prick test wheal size was the best predictor of clinical peanut allergy in both study cohorts. For every 1-mm increase in wheel size, the odds ratio that an individual had a clinical peanut allergy was 2.36 in our first cohort and 4.85 in our second cohort. No other variable came close to the predictive power of wheal size.
Peanut skin prick test wheel size is a strong predictor of clinical reactivity to peanuts. The findings of this study may be useful in guiding physicians’ decision-making regarding the diagnosis of peanut allergy.