Dataset Information

Prediction of Major Adverse Cardiovascular Events in Patients With Hypertrophic Cardiomyopathy Using Proteomics Profiling.

ABSTRACT:

Background

Hypertrophic cardiomyopathy often causes major adverse cardiovascular events (MACE), for example, arrhythmias, stroke, heart failure, and sudden cardiac death. Currently, there are no models available to predict MACE. Furthermore, it remains unclear which signaling pathways mediate MACE. Therefore, we aimed to prospectively determine protein biomarkers that predict MACE in hypertrophic cardiomyopathy and to identify signaling pathways differentially regulated in patients who subsequently develop MACE.

Methods

In this multi-centre prospective cohort study of patients with hypertrophic cardiomyopathy, we conducted plasma proteomics profiling of 4979 proteins upon enrollment. We developed a proteomics-based model to predict MACE using data from one institution (training set). We tested the predictive ability in independent samples from the other institution (test set) and performed time-to-event analysis. Additionally, we executed pathway analysis of predictive proteins using a false discovery rate threshold of <0.001.

Results

The study included 245 patients (n=174 in the training set and n=71 in the test set). Using the proteomics-based model to predict MACE derived from the training set, the area under the receiver-operating-characteristic curve was 0.81 (95% CI, 0.68-0.93) in the test set. In the test set, the high-risk group determined by the proteomics-based predictive model had a significantly higher rate of developing MACE (hazard ratio, 13.6 [95% CI, 1.7-107]; P=0.01). The Ras-MAPK (mitogen-activated protein kinase) pathway was upregulated in patients who subsequently developed MACE (false discovery rate<1.0×10^-7). Pathways involved in inflammation and fibrosis-for example, the TGF (transforming growth factor)-β pathway-were also upregulated.

Conclusions

This study serves as the first to demonstrate the ability of proteomics profiling to predict MACE in hypertrophic cardiomyopathy, exhibiting both novel (eg, Ras-MAPK) and known (eg, TGF-β) pathways differentially regulated in patients who subsequently experience MACE.

SUBMITTER: Shimada YJ

PROVIDER: S-EPMC9771902 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature

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Publications

Prediction of Major Adverse Cardiovascular Events in Patients With Hypertrophic Cardiomyopathy Using Proteomics Profiling.

Shimada Yuichi J YJ Raita Yoshihiko Y Liang Lusha W LW Maurer Mathew S MS Hasegawa Kohei K Fifer Michael A MA Reilly Muredach P MP

Circulation. Genomic and precision medicine 20221011 6

<h4>Background</h4>Hypertrophic cardiomyopathy often causes major adverse cardiovascular events (MACE), for example, arrhythmias, stroke, heart failure, and sudden cardiac death. Currently, there are no models available to predict MACE. Furthermore, it remains unclear which signaling pathways mediate MACE. Therefore, we aimed to prospectively determine protein biomarkers that predict MACE in hypertrophic cardiomyopathy and to identify signaling pathways differentially regulated in patients who s ...[more]

PMID: 36252118

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Project description:BackgroundAcute myocardial infarction (AMI) remains a major cause of mortality and morbidity globally, with a high incidence of major adverse cardiovascular events (MACE) post-primary percutaneous coronary intervention (PPCI). The DETERMINE score, derived from electrocardiographic (ECG) markers, has shown promise as a predictor of adverse outcomes, but its clinical utility requires further validation.ObjectiveTo evaluate the predictive value of the DETERMINE score for MACE and provide early clinical warnings for high-risk patients.MethodsThis bidirectional cohort study included AMI patients from the Second Affiliated Hospital of Anhui Medical University between 2019 and 2023. The training cohort comprised 545 patients between January 2019 and January 2023, while the validation cohort consisted of 122 patients between February 2023 and July 2023. The primary endpoint was MACE within one-year post-PPCI. The relationship between the DETERMINE score and MACE was analyzed using Cox regression, trend tests, and restricted cubic splines to assess linear and nonlinear associations. Patients were stratified into risk groups based on tertiles or optimal cutoffs, and Kaplan-Meier survival curves compared MACE incidence across groups. Predictive accuracy was evaluated through time-dependent C-index, ROC curves, decision curve analysis, and calibration, and compared to other prognostic scores, including the Selvester, GRACE, and SYNTAX scores, as well as left ventricular ejection fraction (LVEF). Subgroup analyses by sex, age, and culprit artery involvement were also conducted.ResultsCox multivariate regression indicated that the DETERMINE score was an independent risk factor for MACE (HR = 1.56, 95% CI 1.38-1.75, P < 0.001). Trend test and RCS showed a positive correlation and non-linear relationship between the DETERMINE score and MACE (P-trend < 0.001, P-overall < 0.001, P-nonlinear: 0.003). Kaplan-Meier survival analysis revealed that, in both the training and validation datasets, groups with a higher DETERMINE score showed a higher cumulative risk of MACE. The DETERMINE score outperformed traditional prognostic scores (Selvester, GRACE, SYNTAX) in terms of predictive accuracy, with an AUROC of 0.840 at 12 months in the training cohort. The score also provided a substantial clinical net benefit, particularly over longer follow-up periods. Subgroup analyses confirmed its predictive power across different demographics and clinical presentations.ConclusionThe DETERMINE score has outstanding predictive power for MACE post-PPCI, which can guide the early identification of high-risk patients with poor prognosis of AMI in clinical practice.

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Dataset Information

Prediction of Major Adverse Cardiovascular Events in Patients With Hypertrophic Cardiomyopathy Using Proteomics Profiling.

Background

Methods

Results

Conclusions

Publications

Prediction of Major Adverse Cardiovascular Events in Patients With Hypertrophic Cardiomyopathy Using Proteomics Profiling.

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