Asthma is a complex inflammatory disease of the airways that involves the interaction among multiple genetic, environmental, and lifestyle factors. Nitric oxide (NO) and Immunoglobulin E (IgE) have a relevant role in asthma pathology since NO is both an endogenous modulator of airway function and a pro-inflammatory mediator, while IgE is the main mediator of atopic asthma, the most common form of the disease, which is characterized by type 1 hypersensitivity reactions. Fractional exhaled nitric oxide (FeNO) and total IgE are biomarkers for assessing airways inflammation in asthma, in particular type 2 inflammation(TH2)-associated asthma. Previous genetic association studies have shown that different genes and single nucleotide polymorphisms (SNPs) are linked to FeNO and total IgE. The aim of this thesis is to evaluate the association of SNPs in candidate genes with the levels of FeNO and total IgE in adult subjects with asthma, who were identified in random samples from the general Italian population within the Gene Environment Interactions in Respiratory Diseases (GEIRD) study. Two statistical approaches were used: a classical method for the analysis of total IgE (correction of the levels of statistical significance for the multiplicity of the tests, which allows to evaluate a single variant at a time) and a modern method for the analysis of FeNO [Gradient Boosting Machine (GBM), a machine learning techniques that allows to simultaneously evaluate many variants]. The observed results were replicated within an independent sample of patients from other European countries within the European Community Respiratory Health Survey (ECRHS). SNPs rs987314 in FAM13A and rs3218258 in IL2RB genes were associated with FeNO, while SNP rs549908 in IL18 gene was associated with total IgE. These genes are involved in different biological pathways responsible of asthma pathogenesis and symptomatology.

Association analyses of candidate gene polymorphisms with biomarkers of inflammation in adult subjects with asthma

LANDO, VALENTINA
2023

Abstract

Asthma is a complex inflammatory disease of the airways that involves the interaction among multiple genetic, environmental, and lifestyle factors. Nitric oxide (NO) and Immunoglobulin E (IgE) have a relevant role in asthma pathology since NO is both an endogenous modulator of airway function and a pro-inflammatory mediator, while IgE is the main mediator of atopic asthma, the most common form of the disease, which is characterized by type 1 hypersensitivity reactions. Fractional exhaled nitric oxide (FeNO) and total IgE are biomarkers for assessing airways inflammation in asthma, in particular type 2 inflammation(TH2)-associated asthma. Previous genetic association studies have shown that different genes and single nucleotide polymorphisms (SNPs) are linked to FeNO and total IgE. The aim of this thesis is to evaluate the association of SNPs in candidate genes with the levels of FeNO and total IgE in adult subjects with asthma, who were identified in random samples from the general Italian population within the Gene Environment Interactions in Respiratory Diseases (GEIRD) study. Two statistical approaches were used: a classical method for the analysis of total IgE (correction of the levels of statistical significance for the multiplicity of the tests, which allows to evaluate a single variant at a time) and a modern method for the analysis of FeNO [Gradient Boosting Machine (GBM), a machine learning techniques that allows to simultaneously evaluate many variants]. The observed results were replicated within an independent sample of patients from other European countries within the European Community Respiratory Health Survey (ECRHS). SNPs rs987314 in FAM13A and rs3218258 in IL2RB genes were associated with FeNO, while SNP rs549908 in IL18 gene was associated with total IgE. These genes are involved in different biological pathways responsible of asthma pathogenesis and symptomatology.
2023
Inglese
118
File in questo prodotto:
File Dimensione Formato  
Lando_Tesi di Dottorato.pdf

embargo fino al 28/02/2025

Dimensione 2.13 MB
Formato Adobe PDF
2.13 MB Adobe PDF

I documenti in UNITESI sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14242/115447
Il codice NBN di questa tesi è URN:NBN:IT:UNIVR-115447