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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

OBJECTIVE: Previous research has linked single nucleotide polymorphisms (SNPs) in the ICAM-1 gene to an increased risk of developing ischemic cardiomyopathy (ICM); however, a diagnostic model of ICM according to the ICAM-1 variant has not yet been developed. Therefore, this study aimed to explore th...

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Detalles Bibliográficos
Autores principales: Naman, Tuersunjiang, Abuduhalike, Refukaiti, Yakufu, Mubalake, Bawudun, Ayixigu, Sun, Juan, Mahemuti, Ailiman
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9684327/
https://www.ncbi.nlm.nih.gov/pubmed/36440000
http://dx.doi.org/10.3389/fcvm.2022.977340
Descripción
Sumario:OBJECTIVE: Previous research has linked single nucleotide polymorphisms (SNPs) in the ICAM-1 gene to an increased risk of developing ischemic cardiomyopathy (ICM); however, a diagnostic model of ICM according to the ICAM-1 variant has not yet been developed. Therefore, this study aimed to explore the correlation between SNPs in ICAM-1 and the presence of ICM, along with developing a diagnostic model for ICM based on the variants of the ICAM-1 gene. METHOD: 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 ICAM-1 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 ICAM-1 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. RESULT: 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, P < 0.001) in the training group and 0.983 (95% CI: 0.969–0.998, P < 0.001) in the validation group. The Hosmer–Lemeshow test demonstrated good model calibration with consistency (P(training group =) 0.937; P(validation group =) 0.910). The DCA showed that the ICM nomogram was clinically beneficial, with the threshold probabilities ranging from 0.0 to 1.0. CONCLUSION: The AT genotype in rs5491 of the ICAM-1 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.