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

ObjectivePrevious 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...

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Main Authors: Tuersunjiang Naman, Refukaiti Abuduhalike, Mubalake Yakufu, Ayixigu Bawudun, Juan Sun, Ailiman Mahemuti
Format: Article
Language:English
Published: Frontiers Media S.A. 2022-11-01
Series:Frontiers in Cardiovascular Medicine
Subjects:
Online Access:https://www.frontiersin.org/articles/10.3389/fcvm.2022.977340/full
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author Tuersunjiang Naman
Refukaiti Abuduhalike
Mubalake Yakufu
Ayixigu Bawudun
Juan Sun
Ailiman Mahemuti
author_facet Tuersunjiang Naman
Refukaiti Abuduhalike
Mubalake Yakufu
Ayixigu Bawudun
Juan Sun
Ailiman Mahemuti
author_sort Tuersunjiang Naman
collection DOAJ
description ObjectivePrevious 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.MethodThis 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.ResultThe 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 (Ptraining group = 0.937; Pvalidation group = 0.910). The DCA showed that the ICM nomogram was clinically beneficial, with the threshold probabilities ranging from 0.0 to 1.0.ConclusionThe 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.
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spelling doaj.art-a8a15549ed124874b73059e271d79daf2022-12-22T03:35:39ZengFrontiers Media S.A.Frontiers in Cardiovascular Medicine2297-055X2022-11-01910.3389/fcvm.2022.977340977340Development and validation of a predictive model of the impact of single nucleotide polymorphisms in the ICAM-1 gene on the risk of ischemic cardiomyopathyTuersunjiang NamanRefukaiti AbuduhalikeMubalake YakufuAyixigu BawudunJuan SunAiliman MahemutiObjectivePrevious 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.MethodThis 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.ResultThe 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 (Ptraining group = 0.937; Pvalidation group = 0.910). The DCA showed that the ICM nomogram was clinically beneficial, with the threshold probabilities ranging from 0.0 to 1.0.ConclusionThe 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.https://www.frontiersin.org/articles/10.3389/fcvm.2022.977340/fullgene polymorphismvariants of ICAM-1 geneischemic cardiomyopathy (ICM)predictive modelrisk factors of ICM
spellingShingle Tuersunjiang Naman
Refukaiti Abuduhalike
Mubalake Yakufu
Ayixigu Bawudun
Juan Sun
Ailiman Mahemuti
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
Frontiers in Cardiovascular Medicine
gene polymorphism
variants of ICAM-1 gene
ischemic cardiomyopathy (ICM)
predictive model
risk factors of ICM
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
title_full 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
title_fullStr 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
title_full_unstemmed 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
title_short 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
title_sort 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
topic gene polymorphism
variants of ICAM-1 gene
ischemic cardiomyopathy (ICM)
predictive model
risk factors of ICM
url https://www.frontiersin.org/articles/10.3389/fcvm.2022.977340/full
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