AUTHOR=Naman Tuersunjiang , Abuduhalike Refukaiti , Yakufu Mubalake , Bawudun Ayixigu , Sun Juan , Mahemuti Ailiman 

TITLE=Development and validation of a predictive model of the impact of single nucleotide polymorphisms in the ICAM-1 gene on the risk of ischemic cardiomyopathy

JOURNAL=Frontiers in Cardiovascular Medicine

VOLUME=9

YEAR=2022

URL=https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2022.977340

DOI=10.3389/fcvm.2022.977340

ISSN=2297-055X

ABSTRACT=<sec><title>Objective</title><p>Previous research has linked single nucleotide polymorphisms (SNPs) in the <italic>ICAM-1</italic> gene to an increased risk of developing ischemic cardiomyopathy (ICM); however, a diagnostic model of ICM according to the <italic>ICAM-1</italic> variant has not yet been developed. Therefore, this study aimed to explore the correlation between SNPs in <italic>ICAM-1</italic> and the presence of ICM, along with developing a diagnostic model for ICM based on the variants of the <italic>ICAM-1</italic> gene.</p></sec><sec><title>Method</title><p>This study recruited a total of 252 patients with ICM and 280 healthy controls. In addition, all the participants were genotyped for SNPs in the <italic>ICAM-1</italic> gene by polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP). Using the training dataset of 371 people, we constructed a nomogram model based on <italic>ICAM-1</italic> gene variants and clinical variables. To optimize the feature choice for the ICM risk model, a least absolute shrinkage and selection operator (LASSO) regression model was adopted. We also employed multivariable logistic regression analysis to build a prediction model by integrating the clinical characteristics chosen in the LASSO regression model. Following the receiver operating characteristic (ROC), a calibration plot and decision curve analysis (DCA) were used to evaluate the discrimination, calibration, and clinical usefulness of the predictive model.</p></sec><sec><title>Result</title><p>The predictors involved in the prediction nomogram included age, smoking, diabetes, low-density lipoprotein-cholesterol, hemoglobin, N-terminal pro-B-type natriuretic peptide, ejection fraction, and the rs5491 SNP. The nomogram model exhibited good discrimination ability, with the AUC value of ROC of 0.978 (95%CI: 0.967–0.989, <italic>P</italic> &lt; 0.001) in the training group and 0.983 (95% CI: 0.969–0.998, <italic>P</italic> &lt; 0.001) in the validation group. The Hosmer–Lemeshow test demonstrated good model calibration with consistency (<italic>P</italic><sub>training group =</sub> 0.937; <italic>P</italic><sub>validation group =</sub> 0.910). The DCA showed that the ICM nomogram was clinically beneficial, with the threshold probabilities ranging from 0.0 to 1.0.</p></sec><sec><title>Conclusion</title><p>The AT genotype in rs5491 of the <italic>ICAM-1</italic> gene was associated with having a higher frequency of ICM. Individuals carrying the mutant AT genotype showed a 5.816-fold higher frequency of ICM compared with those with the AA genotype. ICM patients with the AT genotype also had a higher rate of cardiogenic death. We, therefore, developed a nomogram model that could offer an individualized prediction of ICM risk factors.</p></sec>