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Introduction: Asthma is one of the common chronic polygenic inflammatory diseases. Genome wide association studies have identified ADAM33 as an asthma candidate gene. The present study investigated possible association of rs2280090 (T1), rs2280091 (T2) and rs3918396 (S1) single nucleotide polymorphisms (SNPs) of ADAM33 with aeroallergen induced asthma in West Bengal population, India. In addition, in-silico analysis was performed to find out changes in protein function. Methods: Forced expiratory volume in 1 second (FEV1)/Forced vital capacity (FVC), peak expiratory flow rate (PEFR) were assessed using spirometry in 1039 participants. Allergic sensitivity of 619 spirometry positive asthma patients was assessed by skin prick test (SPT) against 22 aeroallergens. For genotyping of T1, T2, and S1 SNPs in 540 allergic asthma patient and 420 control subjects, polymerase chain reaction-based restriction fragment length polymorphism was performed. Total Immunoglobulin-E (IgE) level was measured in both patients and controls. ADAM333 haplotype blocks were constructed using Haploview software v.4.2. Structural model of transmembrane and cytoplasmic domains of ADAM33 was generated using RaptorX. Protein-protein interaction was analysed using the STRING server. Results: Highest number of patient sensitivity was observed towards Cocos nusifera (n = 215) and Dermatophagoides farinae (n = 229). Significant difference in sensitivity was observed between child and late adult (P = 0.03), child and early adult (P = 0.02), adolescent and late adult (P = 0.02) and adolescent and early adult (P = 0.01). Genotypic frequencies differed significantly between patients and controls (P

Background: The recent upsurge in food allergy indicates the need for accurate medical diagnostics. The application of predictive diagnostic models can envisage the outcome of oral food challenge (OFC), reducing cost and time. A logistic regression model was developed by DunnGalvin for children predicting OFC outcome using six predictors viz: sex, age, history, specific IgE, total IgE minus specific IgE, and skin prick test. This model was later updated by Klemans, reducing the number of predictors enhancing the calibration and discrimination of outcome. Objective: Our aim was to revalidate both the models for assessment of egg and milk allergies among Indians in the age group 0–19 years and to determine regression coefficients for our study population. Methods: Revalidation was done at the allergy clinic using OFC outcomes of egg and milk allergic patients. Precise values of the predictors were set up for which calibration (predicted against observed outcome) and discrimination (area under curve [AUC] of receiver operator characteristic curve [ROC]) would be better. Results: The Klemans model with reduced number of predictors showed better accuracy, calibration and discrimination than the DunnGalvin. Best calibration for egg allergy was achieved in the Klemans model with correlation coefficient (r2) of 0.90 and accuracy of 97%. The AUC of ROC was 0.90. For milk allergy, the coefficient was 0.94 with accuracy of 98%. The AUC was 0.91. Conclusion: The present study showed that mathematical models are non-invasive and can be successfully used as appropriate alternative to OFC in Indian population after proper validation.