Genetic risk factors for restenosis after percutaneous coronary intervention in Kazakh population

Association of Endothelial Nitric Oxide Synthase and Angiotensin-Converting Enzyme Genes Polymorphism With In-Sent Restenosis of Bare Metal Stents vs Drug-Eluting Stents in Egyptians

Despite its unequivocal superiority compared with balloon angioplasty, coronary stenting did not abolish restenosis. We aimed to evaluate the associations between a common single nucleotide polymorphism occurring in endothelial nitric oxide synthase (eNOS) and angiotensin-converting enzyme (ACE) genes and the risk of in-stent restenosis (ISR) of bare metal stents vs drug-eluting stents (BMS vs DES) implanted in Egyptian patients. Two hundred patients who had coronary stenting were divided into group I (n = 98) who received a BMS and group II (n = 102) who received a DES. eNOS and ACE genes polymorphism were analyzed by polymerase chain reaction (PCR). We found that the GA and AA genotypes of the eNOS gene were associated with the ISR with both BMS and DES. However, the ACE gene was not associated with ISR. We concluded that eNOS gene polymorphism is associated with ISR. Hypertension, stent length, and AA genotype of the eNOS gene were found to be independent predictors of the occurrence of ISR after both BMS and DES use.

In-stent restenosis in acute coronary syndrome-a classic and a machine learning approach

Background

In acute coronary syndrome (ACS), a number of previous studies tried to identify the risk factors that are most likely to influence the rate of in-stent restenosis (ISR), but the contribution of these factors to ISR is not clearly defined. Thus, the need for a better way of identifying the independent predictors of ISR, which comes in the form of Machine Learning (ML).

Objectives

The aim of this study is to evaluate the relationship between ISR and risk factors associated with ACS and to develop and validate a nomogram to predict the probability of ISR through the use of ML in patients undergoing percutaneous coronary intervention (PCI).

Methods

Consecutive patients presenting with ACS who were successfully treated with PCI and who had an angiographic follow-up after at least 3 months were included in the study. ISR risk factors considered into the study were demographic, clinical and peri-procedural angiographic lesion risk factors. We explored four ML techniques (Random Forest (RF), support vector machines (SVM), simple linear logistic regression (LLR) and deep neural network (DNN)) to predict the risk of ISR. Overall, 21 features were selected as input variables for the ML algorithms, including continuous, categorical and binary variables.

Results

The total cohort of subjects included 340 subjects, in which the incidence of ISR observed was 17.68% (n = 87). The most performant model in terms of ISR prediction out of the four explored was RF, with an area under the receiver operating characteristic (ROC) curve of 0.726. Across the predictors herein considered, only three predictors were statistically significant, precisely, the number of affected arteries (≥2), stent generation and diameter.

Conclusion

ML models applied in patients after PCI can contribute to a better differentiation of the future risk of ISR.

Genetic Variants in PHACTR1 & LPL Mediate Restenosis Risk in Coronary Artery Patients

Background and Objective

Coronary artery disease (CAD) is a major cause of death worldwide. Revascularization via stent placement or coronary artery bypass grafting (CABG) are standard treatments for CAD. Despite a high success rate, these approaches are associated with long-term failure due to restenosis. Risk factors associated with restenosis were investigated using a case-control association study design.

Methods

Five thousand two hundred and forty-two patients were enrolled in this study and were assigned as follows: Stenosis Group: 3570 patients with CAD >50% without a prior stent or CABG (1394 genotyped), and Restenosis Group: 1672 patients with CAD >50% and prior stent deployment or CABG (705 genotyped). Binomial regression models were applied to investigate the association of restenosis with diabetes, hypertension, and dyslipidemia. The genetic association with restenosis was conducted using PLINK 1.9.

Results

Dyslipidemia is a major risk factor (Odds Ratio (OR) = 2.14, P-value <0.0001) for restenosis particularly among men (OR = 2.32, P < 0.0001), while type 2 diabetes (T2D) was associated with an increased risk of restenosis in women (OR = 1.36, P = 0.01). The rs9349379 (PHACTR1) and rs264 (LPL) were associated with an increased risk of restenosis in our patients. PHACTR1 variant was associated with increased risk of restenosis mainly in women and in diabetic patients, while the LPL variant was associated with increased risk of restenosis in men.

Conclusion

The rs9349379 in PHACTR1 gene is significantly associated with restenosis, this association is more pronounced in women and in diabetic patients. The rs264 in LPL gene was associated with increased risk of restenosis in male patients.

GBAP1 polymorphisms (rs140081212, rs1057941 and rs2990220) contribute to reduced risk of gastric cancer

Aim: This study was designed to evaluate the contribution of GBAP1 variants to gastric cancer (GC) risk in a Chinese Han population. Methods: The genotypes of GBAP1 polymorphisms were detected using the Agena MassARRAY platform. Logistic regression analysis was used to calculate odds ratios (ORs) and 95% CIs. Results: GBAP1 rs140081212 (OR = 0.51, p = 4.50 × 10^-07), rs1057941 (OR = 0.48, p = 1.19 × 10^-08) and rs2990220 (OR = 0.46, p = 7.34 × 10^-09) contribute to reduced GC risk, especially gastric adenocarcinoma. Interestingly, the contribution of GBAP1 variants to GC susceptibility was associated with age, sex, BMI, smoking and drinking. Conclusion: This research suggested that GBAP1 polymorphisms might provide a protective effect against GC occurrence in a Chinese Han population.

Association of polymorphisms in TP53 and the promoter region of IL10 with gastric cancer in a Kazakh population

The emerging evidence indicates that single nucleotide polymorphisms (SNPs) of the tumor necrosis factor (TNF), interleukin 10 (IL10), tumor protein p53 (TP53), and cluster of differentiation 14 (CD14) genes may determine individual susceptibility to gastric cancer (GC). We aimed to investigate the associations for polymorphisms of the TNF, IL10, TP53, and CD14 genes in a population of Kazakhs, to identify potential risk or protective associations of the SNPs with GC. A case group of 143 patients hospitalized for GC was enrolled. Controls were 355 volunteers with no history of any cancer and frequency matched with cases by age. Differences in proportions for categorical variables and the assessment of genotypic frequencies conforming to the Hardy-Weinberg equilibrium law were evaluated by the Chi-square test. Associations between genetic polymorphisms and the risk of GC were estimated by regression analysis. For genetic analysis, three genetic models (additive, dominant, and recessive) were used. Four significant associations were found. The SNPs rs1042522 of TP53 and rs1800896 of IL10 were risk factors for GC by the additive model. Two polymorphisms of IL10 were protective of GC, namely, rs1800872 by additive model and rs1800871 by recessive model. No significant associations were observed between the TNF and CD14 polymorphisms and GC. The polymorphisms TP53 rs1042522 and IL10 rs1800896 are associated with GC risk, while the polymorphisms IL10 rs1800872 and rs1800871 are protective of GC in the population of Kazakhs.