Integrated proteomic and metabolomic profiling reveals novel insights on the inflammation and immune response in HFpEF.

BACKGROUND: The precise mechanisms leading to the development of heart failure with preserved ejection fraction (HFpEF) remain incompletely defined. In this study, an integrative approach utilizing untargeted proteomics and metabolomics was employed to delineate the altered proteomic and metabolomic profiles in patients with HFpEF compared to healthy controls. MATERIALS AND METHODS: Data were collected from a prospective cohort consisting of 30 HFpEF participants and 30 healthy controls, matched by gender and age. plasma samples were analyzed by multi-omics platforms. The quantification of plasma proteins and metabolites was performed using data-independent acquisition-based liquid chromatography-tandem mass spectrometry (LC-MS/MS) and ultrahigh-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS), respectively. Additionally, Proteomic and metabolomic results were analyzed separately and integrated using correlation and pathway analysis. This was followed by the execution of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment studies to elucidate the biological relevance of the observed results. RESULTS: A total of 46 significantly differentially expressed proteins (DEPs) and 102 differentially expressed metabolites (DEMs) were identified. Then, GO and KEGG pathway enrichment analyses were performed by DEPs and DEMs. Integrated analysis of proteomics and metabolomics has revealed Tuberculosis and African trypanosomiasis pathways that are significantly enriched and the DEPs and DEMs enriched within them, are associated with inflammation and immune response. CONCLUSIONS: Integrated proteomic and metabolomic analyses revealed distinct inflammatory and immune response pathways in HFpEF, highlighting novel therapeutic avenues.

Genetic Association Between ICAM-1 Gene Variants and Susceptibility to Ischemic Cardiomyopathy.

ABSTRACT: The current work was aimed at exploring the association between single nucleotide polymorphisms (SNPs) in the ICAM-1 gene, along with the identification of additional haplotypes and their potential role in the susceptibility to ischemic cardiomyopathy (ICM). The control group underwent a Hardy-Weinberg equilibrium test. The associations of genotypes and alleles with susceptibility to ICM were then analyzed using logistic regression analysis. Subsequently odds ratios (ORs) along with 95% confidence intervals (95% CI) were calculated. Interaction analysis was conducted between these SNPs. Furthermore, linkage disequilibrium analysis and haplotype analysis were performed on SNPs that showed interactions with each other. The incidence of ICM was significantly higher among individuals carrying the T allele of rs3093032 (OR = 2.032, 95% CI, 1.275-3.241, P = 0.003) relative to those with the C allele. In addition, CT genotype carriers had a higher susceptibility to ICM than CC genotype carriers (OR = 2.490, 95% CI, 1.445-4.29, P = 0.001). Furthermore, 3 SNPs (rs3093032, rs923366, rs3093030) exhibited a strong interaction with each other, whereas rs281437 showed no interaction with the other 3 SNPs. Individuals carrying the C rs3093032 -T rs923366 -C rs3093030 haplotype had an elevated risk of ICM compared with those carrying the C rs3093032 -C rs923366 -C rs3093030 haplotype (OR = 2.280, 95% CI, 1.568-3.315, P < 0.001). Moreover, individuals carrying the T rs3093032 -C rs923366 -C rs3093030 haplotype were more susceptible to ICM than those carrying the C rs3093032 -C rs923366 -C rs3093030 haplotype (OR = 2.388, 95% CI, 1.469-3.880, P < 0.001). Regarding rs3093032, the minor alleles and haplotypes are associated with an increased ICM risk: 3 SNPs (rs3093032, rs923366, rs3093030) in ICAM-1 have strong interaction with each other.

Development and validation of a novel nomogram to predict the impact of the polymorphism of the ICAM-1 gene on the prognosis of ischemic cardiomyopathy.

The current study investigated the association between polymorphisms of the ICAM-1 gene and prognosis of Ischemic cardiomyopathy (ICM), and developed a prognostic nomogram for ICM on the basis of ICAM-1 gene variants. The current study included totally 252 patients with ICM. In addition, PCR-RFLP (polymerase chain reaction-restriction fragment length polymorphism) was used to genotype SNPs in the ICAM-1 gene in the patients. Later, the nomogram model was built by combining clinical data and ICAM-1 gene variants. This study used the least absolute shrinkage and selection operator (LASSO) regression model to optimize feature selection into an ICM prognostic model. Furthermore, multivariate Cox-regression was applied to build the prognostic model, which included clinical and gene features chosen by the LASSO regression model. Following that, the receiver operating characteristic (ROC) curve, C-index, calibration plot analyses and decision curve analysis (DCA) were carried out to evaluate the discrimination ability, consistency, and clinical utility of the prognostic model, and the bootstrap method was adopted for internal validation. predicting factors rs112872667, treating by PCI or CABG, ventricular arrhythmia, left ventricular end-diastolic diameter (LVDD), use of ß-blockers, systolic blood pressure (SBP), heart rate (HR), and serum sodium were incorporated into the prognostic nomogram. The constructed nomogram performed well in discrimination ability, as observed by the time-dependent C-index. Furthermore, as shown by calibration curves, our nomogram's predicted probabilities were highly consistent with measured values. With threshold probabilities, DCA suggested that our nomogram could be useful in the clinic. mutation of rs112872667 have critical predictive value on the prognosis of ICM, ICM patients with the mutant genotype (CT or TT) have higher survival probability than those with the wild genotype (CC). Mutation of rs112872667 in ICAM-1 gene have critical predictive value on the prognosis of ICM, ICM patients with the mutant genotype (CT or TT) have higher survival probability than those with the wild genotype (CC).

Development and Validation of a Novel Nomogram to Predict the Impact of the Polymorphisms of the Variants of ICAM-1 Gene on the Prognosis of Ischemic Cardiomyopathy.

Object: This study investigated the correlation between polymorphisms of the ICAM-1 gene and prognosis of Ischemic cardiomyopathy (ICM), and developed a prognostic model for predicting the prognosis ICM on the basis of ICAM-1 gene variants. Methods: The current study included totally 576 patients with ICM. All patients are randomly divided into training group with 399 patients and validation group with 177 patients. The prognostic model was constructed by using the data of training group. Univariable Cox-regression analysis was performed, including clinical and gene variants, then used the least absolute shrinkage and selection operator (LASSO) regression model to optimize feature selection. Furthermore, multivariate Cox-regression was applied to build the prognostic nomogram model, which included clinical and gene features chosen by the LASSO regression model. Following that, the receiver operating characteristic (ROC) curve, C-index, calibration plot analyses and decision curve analysis (DCA) were carried out to evaluate the discrimination ability, consistency, and clinical utility of the prognostic model. Results: Predicting factors rs281430, ventricular arrhythmia, treating by PCI or CABG, use of ß-blockers, heart rate (HR), serum sodium level, left ventricular end-diastolic diameter (LVDD) were the risk factors of the prognosis of ICM, incorporated these factors into the prognostic nomogram model. The constructed nomogram performed well in discrimination ability, as observed by the ROC and C-index. Furthermore, as shown by calibration curves, our nomogram's predicted probabilities were highly consistent with measured values. With threshold probabilities, DCA suggested that our nomogram could be useful in the clinic. Conclusion: rs281430 mutation (from AA genotype to AG or GG genotype) is a risk factor for ICM patients to have a higher survival probability; the survival probability of ICM patients with the mutant genotype (AG or GG) is lower than those with the wild genotype (AA).

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 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.