Genetic polymorphism in Angiotensin-Converting Enzyme (ACE) and Asthma risk: A comparative meta-analysis based on Four gene model strategy

Abstract

Recent research indicates that angiotensin-converting enzyme (ACE) gene activity contributes to the etiology of asthma disease.  Over the years, various studies on the ACE gene polymorphism have been conducted, but the results have been inconsistent. We conducted a meta-analysis to assess the relationship between the ACE gene polymorphism (I/D) and asthma risk using the conventional meta-analysis method and four-gene model strategy proposed by Horita and Kaneko. A systematic search of PubMed, Medline, Embase, etc. was conducted to retrieve all studies pertaining to ACE gene polymorphism. Data were extracted from all the eligible studies, and pooled Odds ratio (OR) and 95% CI were calculated to determine the strength of an association between ACE gene polymorphism and asthma risk. Using software such as SPSS, Review Manager 5.4, and JASP (Jeffrey's Amazing Statistics Program), the data were statistically analyzed. In our analysis, we contrasted a number of gene polymorphism models. In accordance with standard meta-analysis procedures, we selected the dominant genetic model to be the random effect model in our meta-analyses. Using this model, we found that DD homozygotes have an increased risk of asthma disease compared to DI heterozygotes and II homozygotes (OR=1.61, CI=1.28-2.03, I2=68%, p<0.00001). According to ethnicity-based stratified analyses, Asians are more susceptible to asthma than Europeans. Moreover, age-based analysis revealed that minors are more susceptible to asthma than adults. Our research aimed to compare conventional meta-analysis practices to the four-gene model strategy as it reduces type 1 error. We have chosen the dominant genetic model as the random effect (RE) model under the conventional meta-analysis technique and observed the significant association between ACE gene polymorphism and asthma disease. Although, while performing the four-gene model strategy, the over-dominant model was selected as the best possible model for the meta-analysis under which no significant association was observed.