Dataset Information

Identification and validation of six acute myocardial infarction-associated variants, including a novel prognostic marker for cardiac mortality.

ABSTRACT:

Background

Acute myocardial infarction (AMI) is one of the leading causes of death worldwide, and approximately half of AMI-related deaths occur before the affected individual reaches the hospital. The present study aimed to identify and validate genetic variants associated with AMI and their role as prognostic markers.

Materials and methods

We conducted a replication study of 29 previously identified novel loci containing 85 genetic variants associated with early-onset AMI using a new independent set of 2,920 Koreans [88 patients with early- and 1,085 patients with late-onset AMI, who underwent percutaneous coronary intervention (PCI), and 1,747 healthy controls].

Results

Of the 85 previously reported early-onset variants, six were confirmed in our genome-wide association study with a false discovery rate of less than 0.05. Notably, rs12639023, a cis-eQTL located in the intergenic region between LINC02005 and CNTN3, significantly increased longitudinal cardiac mortality and recurrent AMI. CNTN3 is known to play a role in altering vascular permeability. Another variant, rs78631167, located upstream of PLAUR and known to function in fibrinolysis, was moderately replicated in this study. By surveying the nearby genomic region around rs78631167, we identified a significant novel locus (rs8109584) located 13 bp downstream of rs78631167. The present study showed that six of the early-onset variants of AMI are applicable to both early- and late-onset cases.

Conclusion

Our results confirm markers that can potentially be utilized to predict, screen, prevent, and treat candidate patients with AMI and highlight the potential of rs12639023 as a prognostic marker for cardiac mortality in AMI.

SUBMITTER: Jeon Y

PROVIDER: S-EPMC10350496 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Identification and validation of six acute myocardial infarction-associated variants, including a novel prognostic marker for cardiac mortality.

Jeon Yeonsu Y Jeon Sungwon S An Kyungwhan K Kim Yeo Jin YJ Kim Byoung-Chul BC Ryu Hyojung H Choi Whan-Hyuk WH Choi HyunJoo H Kim Weon W Lee Sang Yeub SY Lee Sang Yeub SY Bae Jang-Whan JW Hwang Jin-Yong JY Kang Min Gyu MG An Seolbin S Kim Yeonkyung Y Kang Younghui Y Kim Byung Chul BC Bhak Jong J Shin Eun-Seok ES

Frontiers in cardiovascular medicine 20230703

<h4>Background</h4>Acute myocardial infarction (AMI) is one of the leading causes of death worldwide, and approximately half of AMI-related deaths occur before the affected individual reaches the hospital. The present study aimed to identify and validate genetic variants associated with AMI and their role as prognostic markers.<h4>Materials and methods</h4>We conducted a replication study of 29 previously identified novel loci containing 85 genetic variants associated with early-onset AMI using ...[more]

PMID: 37465449

Similar Datasets

Project description:Background Cardiovascular magnetic resonance imaging is considered the reference methodology for cardiac morphology and function but requires manual postprocessing. Whether novel artificial intelligence-based automated analyses deliver similar information for risk stratification is unknown. Therefore, this study aimed to investigate feasibility and prognostic implications of artificial intelligence-based, commercially available software analyses. Methods and Results Cardiovascular magnetic resonance data (n=1017 patients) from 2 myocardial infarction multicenter trials were included. Analyses of biventricular parameters including ejection fraction (EF) were manually and automatically assessed using conventional and artificial intelligence-based software. Obtained parameters entered regression analyses for prediction of major adverse cardiac events, defined as death, reinfarction, or congestive heart failure, within 1 year after the acute event. Both manual and uncorrected automated volumetric assessments showed similar impact on outcome in univariate analyses (left ventricular EF, manual: hazard ratio [HR], 0.93 [95% CI 0.91-0.95]; P<0.001; automated: HR, 0.94 [95% CI, 0.92-0.96]; P<0.001) and multivariable analyses (left ventricular EF, manual: HR, 0.95 [95% CI, 0.92-0.98]; P=0.001; automated: HR, 0.95 [95% CI, 0.92-0.98]; P=0.001). Manual correction of the automated contours did not lead to improved risk prediction (left ventricular EF, area under the curve: 0.67 automated versus 0.68 automated corrected; P=0.49). There was acceptable agreement (left ventricular EF: bias, 2.6%; 95% limits of agreement, -9.1% to 14.2%; intraclass correlation coefficient, 0.88 [95% CI, 0.77-0.93]) of manual and automated volumetric assessments. Conclusions User-independent volumetric analyses performed by fully automated software are feasible, and results are equally predictive of major adverse cardiac events compared with conventional analyses in patients following myocardial infarction. Registration URL: https://www.clinicaltrials.gov; Unique identifiers: NCT00712101 and NCT01612312.

Project description:ObjectiveThe purpose of this study was to examine the level of serum FGF21 in acute myocardial infarction (AMI) and to explore the association between serum FGF21 and major adverse cardiovascular events in AMI patients.MethodsA total of 238 Chinese patients were recruited for this study between January 2016 and June 2016, including 183 AMI patients and 55 patients without AMI were enrolled in this study. The level of serum FGF21 was measured by ELISA. Patients were followed-up after admission using a standardized protocol that included Outpatient follow-up and telephone contacts to record the Major Adverse Cardiovascular Events (MACEs). Finally, 165 patients were enrolled and followed up during the period of 24 months.ResultsCirculating FGF21 level was elevated in the AMI patients compared to control (P < 0.05). Multivariate linear regression analysis showed that cTnI (β = -0.001, 95% CI = 0.021-0.001, P = 0.002), fibrinogen (β = -0.055, 95% CI = 0.098-0.013, P = 0.011) and hyperlipidemia (β = 0.129, 95% CI = 0.014-0.243, P = 0.011) were the independent risk factors for the serum of FGF21 in AMI patients. After follow-up, A Kaplan-Meier analysis showed that the all-cause mortality rate was not significantly different between the two groups (P = 0.4146). Meanwhile the rate of cardiovascular events was significantly higher in the high-FGF21 group than in the low-FGF21 group (P = 0.0399). In the multivariate Cox proportional hazards regression model suggested that the predictive independent risk factors for the occurrence of MACEs were FGF21 ≥ cut-off (HR: 1.637; 95% CI: 1.357-3.647, P = 0.029) and D-Dimer (HR: 1.420; 95% CI: 1.069-3.014, P = 0.046).ConclusionCirculating FGF21 level is elevated in the AMI patients. In AMI patients, cTnI, fibrinogen and hyperlipidemia are the inde pendent risk factors for the serum of FGF21. Higher circulating FGF21 level is associated with increased MACEs rate. This study suggests that circulating FGF21 level may be a predictive marker of the clinical outcomes in AMI patients.

Project description:ObjectivesThe risk of adverse events and prognostic factors are changing in different time phases after acute myocardial infarction (AMI). The incidence of adverse events is considerable in the early period after AMI hospitalisation. Therefore, dynamic risk prediction is needed to guide postdischarge management of AMI. This study aimed to develop a dynamic risk prediction instrument for patients following AMI.DesignA retrospective analysis of a prospective cohort.Setting108 hospitals in China.ParticipantsA total of 23 887 patients after AMI in the China Acute Myocardial Infarction Registry were included in this analysis.Primary outcome measuresAll-cause mortality.ResultsIn multivariable analyses, age, prior stroke, heart rate, Killip class, left ventricular ejection fraction (LVEF), in-hospital percutaneous coronary intervention (PCI), recurrent myocardial ischaemia, recurrent myocardial infarction, heart failure (HF) during hospitalisation, antiplatelet therapy and statins at discharge were independently associated with 30-day mortality. Variables related to mortality between 30 days and 2 years included age, prior renal dysfunction, history of HF, AMI classification, heart rate, Killip class, haemoglobin, LVEF, in-hospital PCI, HF during hospitalisation, HF worsening within 30 days after discharge, antiplatelet therapy, β blocker and statin use within 30 days after discharge. The inclusion of adverse events and medications significantly improved the predictive performance of models without these indexes (likelihood ratio test p<0.0001). These two sets of predictors were used to establish dynamic prognostic nomograms for predicting mortality in patients with AMI. The C indexes of 30-day and 2-year prognostic nomograms were 0.85 (95% CI 0.83-0.88) and 0.83 (95% CI 0.81-0.84) in derivation cohort, and 0.79 (95% CI 0.71-0.86) and 0.81 (95% CI 0.79-0.84) in validation cohort, with satisfactory calibration.ConclusionsWe established dynamic risk prediction models incorporating adverse event and medications. The nomograms may be useful instruments to help prospective risk assessment and management of AMI.Trial registration numberNCT01874691.

Dataset Information

Identification and validation of six acute myocardial infarction-associated variants, including a novel prognostic marker for cardiac mortality.

Background

Materials and methods

Results

Conclusion

Publications

Identification and validation of six acute myocardial infarction-associated variants, including a novel prognostic marker for cardiac mortality.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets