Predictive value of circulating cystatin C level in patients with acute coronary syndrome: a meta-analysis

Abstract Background Acute coronary syndrome (ACS) is a cardiac emergency with high mortality. Exposure to high copper (Cu) concentration has been linked to ACS. However, whether DNA methylation contributes to the association between Cu and ACS is unclear. Methods We measured methylation level at > 485,000 cytosine-phosphoguanine sites (CpGs) of blood leukocytes using Human Methylation 450 Bead Chip and conducted a genome-wide meta-analysis of plasma Cu in a total of 1243 Chinese individuals. For plasma Cu-related CpGs, we evaluated their associations with the expression of nearby genes as well as major cardiovascular risk factors. Furthermore, we examined their longitudinal associations with incident ACS in the nested case-control study. Results We identified four novel Cu-associated CpGs (cg20995564, cg18608055, cg26470501 and cg05825244) within a 5% false discovery rate (FDR). DNA methylation level of cg18608055, cg26470501, and cg05825244 also showed significant correlations with expressions of SBNO2, BCL3, and EBF4 gene, respectively. Higher DNA methylation level at cg05825244 locus was associated with lower high-density lipoprotein cholesterol level and higher C-reactive protein level. Furthermore, we demonstrated that higher cg05825244 methylation level was associated with increased risk of ACS (odds ratio [OR], 1.23; 95% CI 1.02–1.48; P = 0.03). Conclusions We identified novel DNA methylation alterations associated with plasma Cu in Chinese populations and linked these loci to risk of ACS, providing new insights into the regulation of gene expression by Cu-related DNA methylation and suggesting a role for DNA methylation in the association between copper and ACS.

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Validation of a machine learned model to predict the diagnosis of myocardial infarction

European Heart Journal ◽

10.1093/ehjci/ehaa946.1669 ◽

2020 ◽

Vol 41 (Supplement_2) ◽

Author(s):

D Doudesis ◽

J Yang ◽

A Tsanas ◽

C Stables ◽

A Shah ◽

...

Keyword(s):

Myocardial Infarction ◽

Acute Coronary Syndrome ◽

Predictive Value ◽

High Probability ◽

Cardiovascular Death ◽

Gradient Boosting ◽

Funding Source ◽

Coronary Syndrome ◽

Low Probability ◽

One Year

Abstract Introduction The myocardial-ischemic-injury-index (MI3) is a promising machine learned algorithm that predicts the likelihood of myocardial infarction in patients with suspected acute coronary syndrome. Whether this algorithm performs well in unselected patients or predicts recurrent events is unknown. Methods In an observational analysis from a multi-centre randomised trial, we included all patients with suspected acute coronary syndrome and serial high-sensitivity cardiac troponin I measurements without ST-segment elevation myocardial infarction. Using gradient boosting, MI3 incorporates age, sex, and two troponin measurements to compute a value (0–100) reflecting an individual's likelihood of myocardial infarction, and estimates the negative predictive value (NPV) and positive predictive value (PPV). Model performance for an index diagnosis of myocardial infarction, and for subsequent myocardial infarction or cardiovascular death at one year was determined using previously defined low- and high-probability thresholds (1.6 and 49.7, respectively). Results In total 20,761 of 48,282 (43%) patients (64±16 years, 46% women) were eligible of whom 3,278 (15.8%) had myocardial infarction. MI3 was well discriminated with an area under the receiver-operating-characteristic curve of 0.949 (95% confidence interval 0.946–0.952) identifying 12,983 (62.5%) patients as low-probability (sensitivity 99.3% [99.0–99.6%], NPV 99.8% [99.8–99.9%]), and 2,961 (14.3%) as high-probability (specificity 95.0% [94.7–95.3%], PPV 70.4% [69–71.9%]). At one year, subsequent myocardial infarction or cardiovascular death occurred more often in high-probability compared to low-probability patients (17.6% [520/2,961] versus 1.5% [197/12,983], P<0.001). Conclusions In unselected consecutive patients with suspected acute coronary syndrome, the MI3 algorithm accurately estimates the likelihood of myocardial infarction and predicts probability of subsequent adverse cardiovascular events. Performance of MI3 at example thresholds Funding Acknowledgement Type of funding source: Foundation. Main funding source(s): Medical Research Council

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Comparison of prasugrel and ticagrelor for patient with acute coronary syndrome: a systematic review and meta-analysis

European Heart Journal ◽

10.1093/ehjci/ehaa946.1434 ◽

2020 ◽

Vol 41 (Supplement_2) ◽

Author(s):

L.C.W Fong ◽

N Lee ◽

A.T Yan ◽

M.Y Ng

Keyword(s):

Acute Coronary Syndrome ◽

Stent Thrombosis ◽

Meta Analysis ◽

Composite Endpoint ◽

Cardiovascular Death ◽

Coronary Syndrome ◽

Primary Composite Endpoint ◽

Significant Difference ◽

St Elevation

Abstract Background Prasugrel and ticagrelor are both effective anti-platelet drugs for patients with acute coronary syndrome. However, there has been limited data on the direct comparison of prasugrel and ticagrelor until the recent ISAR-REACT 5 trial. Purpose To compare the efficacy of prasugrel and ticagrelor in patients with acute coronary syndrome with respect to the primary composite endpoint of myocardial infarction (MI), stroke or cardiac cardiovascular death, and secondary endpoints including MI, stroke, cardiovascular death, major bleeding (Bleeding Academic Research Consortium (BARC) type 2 or above), and stent thrombosis within 1 year. Methods Meta-analysis was performed on randomised controlled trials (RCT) up to December 2019 that randomised patients with acute coronary syndrome to either prasugrel or ticagrelor. RCTs were identified from Medline, Embase and ClinicalTrials.gov using Cochrane library CENTRAL by 2 independent reviewers with “prasugrel” and “ticagrelor” as search terms. Effect estimates with confidence intervals were generated using the random effects model by extracting outcome data from the RCTs to compare the primary and secondary clinical outcomes. Cochrane risk-of-bias tool for randomised trials (Ver 2.0) was used for assessment of all eligible RCTs. Results 411 reports were screened, and we identified 11 eligible RCTs with 6098 patients randomised to prasugrel (n=3050) or ticagrelor (n=3048). The included trials had a follow up period ranging from 1 day to 1 year. 330 events on the prasugrel arm and 408 events on the ticagrelor arm were recorded. There were some concerns over the integrity of allocation concealment over 7 trials otherwise risk of other bias was minimal. Patients had a mean age of 61±4 (76% male; 50% with ST elevation MI; 35% with non-ST elevation MI; 15% with unstable angina; 25% with diabetes mellitus; 64% with hypertension; 51% with hyperlipidaemia; 42% smokers). There was no significant difference in risk between the prasugrel group and the ticagrelor group on the primary composite endpoint (Figure 1) (Risk Ratio (RR)=1.17; 95% CI=0.97–1.41; p=0.10, I2=0%). There was no significant difference between the use of prasugrel and ticagrelor with respect to MI (RR=1.24; 95% CI=0.81–1.90; p=0.31); stroke (RR=1.05; 95% CI=0.66–1.67; p=0.84); cardiovascular death (RR=1.01; 95% CI=0.75–1.36; p=0.95); BARC type 2 or above bleeding (RR=1.17; 95% CI =0.90–1.54; p=0.24); stent thrombosis (RR=1.58; 95% CI =0.90–2.76; p=0.11). Conclusion Compared with ticagrelor, prasugrel did not reduce the primary composite endpoint of MI, stroke and cardiovascular death within 1 year. There was also no significant difference in the risk of MI, stroke, cardiovascular death, major bleeding and stent thrombosis respectively. Figure 1. Primary Objective Funding Acknowledgement Type of funding source: None

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