Value of S100A12 in predicting in-stent restenosis in patients with coronary drug-eluting stent implantation

A systematic review and bioinformatic study on clinical, paraclinical, and genetic factors predisposing to stent restenosis following percutaneous coronary intervention

BACKGROUND

Stent restenosis is a relatively common phenomenon among patients with coronary heart disease undergoing percutaneous coronary intervention (PCI). It seems that a set of clinical, laboratory, and even genetic factors make people susceptible to such a phenomenon and in fact, this is multi-factorial. We aimed to first determine the underlying clinical and laboratory risk factors for the occurrence of stent re-stenosis after PCI based on a systematic review study, and after that, through a bioinformatics study, to evaluate the related genes and microRNAs with the occurrence of stent re-stenosis.

MAIN TEXT

In the first step, the manuscript databases including Medline, Web of Knowledge, Google Scholar, Scopus, and Cochrane were deeply searched by the two blinded investigators for all eligible studies based on the considered keywords to introduce clinical and laboratory determinants of stent re-stenosis. In the bioinformatic phase, and following a review of the literature to identify genes and microRNAs involved in restenosis, the interaction of each gene with other genes associated with stent re-stenosis was determined by GeneMANIA network analysis and Cytoscape software. Overall, 67 articles (including 40,789 patients) on clinical and biochemical predictors for stent restenosis and 25 articles on genetic determinants of this event were eligible for the final analysis. The predictors for this event were categorized into four subgroups patient-based parameters including traditional cardiovascular risk profiles, stent-based parameters including type and diametric characteristics of the stents used, coronary lesion-based parameters including several two target lesions and coronary involvement severity and laboratory-based parameters particularly related to activation of inflammatory processes. In the bioinformatic phase, we uncovered 42 genes that have been described to be involved in such a phenomenon considering a special position for genes encoding inflammatory cytokines. Also, 12 microRNAs have been pointed to be involved in targeting genes involved in stent re-stenosis.

CONCLUSIONS

The incidence of stent re-stenosis will be the result of a complex interaction of clinical risk factors, laboratory factors mostly related to the activation of inflammatory processes, and a complex network of gene-to-gene interactions.

Biomarker Prediction of Complex Coronary Revascularization Procedures in the FOURIER Trial

BACKGROUND

Biomarkers are known to predict major adverse cardiovascular events. However, the association of biomarkers with complex coronary revascularization procedures or high-risk coronary anatomy at the time of revascularization is not understood.

OBJECTIVES

We examined the associations between baseline biomarkers and major coronary events (MCE) and complex revascularization procedures.

METHODS

FOURIER was a randomized trial of the proprotein convertase subtilisin-kexin type 9 inhibitor evolocumab vs placebo in 27,564 patients with stable atherosclerosis. We analyzed adjusted associations among the biomarkers, MCE (coronary death, myocardial infarction, or revascularization), and complex revascularization (coronary artery bypass graft or complex percutaneous coronary intervention) using a multimarker score with 1 point assigned for each elevated biomarker (high-sensitivity C-reactive protein ≥2 mg/L; N-terminal pro-B-type natriuretic peptide ≥450 pg/mL; high-sensitivity troponin I ≥6 ng/L; growth-differentiation factor-15 ≥1,800 pg/mL).

RESULTS

When patients were grouped by the number of elevated biomarkers (0 biomarkers, n = 6,444; 1-2 biomarkers, n = 12,439; ≥3 biomarkers, n = 2,761), there was a significant graded association between biomarker score and the risk of MCE (intermediate score: HR_adj: 1.57 [95% CI: 1.38-1.78]; high score: HR_adj: 2.90 [95% CI: 2.47-3.40]), and for complex revascularization (intermediate: HR_adj: 1.33 [95% CI: 1.06-1.67]; high score: HR_adj: 2.07 [95% CI: 1.52-2.83]) and its components (P_trend <0.05 for each). The number of elevated biomarkers also correlated with the presence of left main disease, multivessel disease, or chronic total occlusion at the time of revascularization (P < 0.05 for each).

CONCLUSIONS

A biomarker-based strategy identifies stable patients at risk for coronary events, including coronary artery bypass graft surgery and complex percutaneous coronary intervention, and predicts high-risk coronary anatomy at the time of revascularization. These findings provide insight into the relationships between cardiovascular biomarkers, coronary anatomical complexity, and incident clinical events. (Further Cardiovascular Outcomes Research With PCSK9 Inhibition in Subjects With Elevated Risk [FOURIER]; NCT01764633).

A prediction model based on platelet parameters, lipid levels, and angiographic characteristics to predict in-stent restenosis in coronary artery disease patients implanted with drug-eluting stents

BACKGROUND

The present study was aimed to establish a prediction model for in-stent restenosis (ISR) in subjects who had undergone percutaneous coronary intervention (PCI) with drug-eluting stents (DESs).

MATERIALS AND METHODS

A retrospective cohort study was conducted. From September 2010 to September 2013, we included 968 subjects who had received coronary follow-up angiography after primary PCI. The logistic regression analysis, receiver operator characteristic (ROC) analysis, nomogram analysis, Hosmer-Lemeshow χ2 statistic, and calibration curve were applied to build and evaluate the prediction model.

RESULTS

Fifty-six patients (5.79%) occurred ISR. The platelet distribution width (PDW), total cholesterol (TC), systolic blood pressure (SBP), low-density lipoprotein cholesterol (LDL-C), and lesion vessels had significant differences between ISR and non-ISR groups (all P < 0.05). And these variables were independently associated with ISR (all P < 0.05). Furthermore, they were identified as predictors (all AUC > 0.5 and P < 0.05) to establish a prediction model. The prediction model showed a good value of area under curve (AUC) (95%CI): 0.72 (0.64-0.80), and its optimized cut-off was 6.39 with 71% sensitivity and 65% specificity to predict ISR.

CONCLUSION

The incidence of ISR is 5.79% in CAD patients with DES implantation in the Xinjiang population, China. The prediction model based on PDW, SBP, TC, LDL-C, and lesion vessels was an effective model to predict ISR in CAD patients with DESs implantation.

Clinical severity of serum S100A12 levels in patients with osteoarthritis and S100A12 open inflammation of osteoarthritis model by NLRP3

Osteoarthritis is a common chronic bone and joint disease, which is characterized by degenerative changes and destruction of articular cartilage, secondary hyperostosis. This study aimed to investigate the clinical severity and mechanism of S100A12 in patients with osteoarthritis. Serum samples were obtained from patients with osteoarthritis or normal volunteer in Minhang Branch of Yueyang Hospital of Integrated Traditional Chinese and Western Medicine affiliated to Shanghai University of Traditional Chinese Medicine (Shanghai, China). C57BL/6J mice performed Resection of the medial collateral ligament and medial meniscus as mice model. MC3T3-E1 cells were induced with 100 ng of LPS as vitro model. The serum level of S100A12 was increased in patients with osteoarthritis. Similarly, S100A12 levels of serum and bone tissue from mice model of osteoarthritis were also higher than those of sham group. Over-expression of S100A12 promoted inflammation levels while down-regulation of S100A12 decreased inflammation levels in in vitro model of osteoarthritis. NLRP3 is an important target of S100A12 in pro-inflammation effects of osteoarthritis. NLRP3 was involved in the effects of S100A12 on inflammation in in vitro model of osteoarthritis. S100A12 also accelerated inflammation by NLRP3 in mice model of osteoarthritis. We conclude that serum S100A12 levels was a possible clinical severity, open inflammation of osteoarthritis model by NLRP3 and its receptors may be effective in preventing the development of osteoarthritis.