Acute Myocardial Infarction Clinical Cohort

Acute Myocardial Infarction

This prospective, multicenter, observational cohort study aims to establish a comprehensive clinical database and high-quality biobank for patients with Acute Myocardial Infarction (AMI) in China. The study plans to enroll 2,000 AMI patients across four major medical centers to collect standardized clinical data, multi-modality imaging, and biological samples. A key focus of this study is the deep phenotyping of high-risk subgroups, including patients with vulnerable plaques, Myocardial Infarction with Non-obstructive Coronary Arteries (MINOCA), and borderline coronary lesions. By integrating advanced multi-omics sequencing (Whole Genome Sequencing, RNA-seq, single-cell RNA sequencing, and Olink proteomics) with cutting-edge AI-driven imaging radiomics (CCTA, OCT, IVUS, and novel intracoronary fluorescence imaging), the study seeks to elucidate the molecular mechanisms of AMI. The ultimate goal is to discover novel biomarkers for early warning, develop precise risk prediction models for Major Adverse Cardiovascular Events (MACE), and facilitate the development of personalized diagnostic and therapeutic strategies for AMI patients.

Background: Despite advancements in cardiovascular medicine, Acute Myocardial Infarction (AMI) remains a leading cause of morbidity and mortality. Current risk stratification models and therapeutic strategies often lack population-specific precision, particularly for the Chinese demographic. Furthermore, specific high-risk subgroups-such as those with vulnerable plaques, MINOCA, and borderline coronary lesions-require deeper investigation to understand their unique pathophysiological mechanisms. This project initiates a Translational Research Cohort (TRC) to bridge the gap between basic multi-omics research, clinical imaging, and medical device innovation. Study Design and Population: This is a prospective, multicenter, observational study led by Zhongshan Hospital, Fudan University, in collaboration with three other major tertiary hospitals in Shanghai. The study will enroll 2,000 patients diagnosed with Coronary Heart Disease (CHD) and AMI. Data Collection and Biobanking: For all enrolled participants, the study will construct a holographic database encompassing structured electronic medical records (EMR), baseline demographics, laboratory tests, multi-modality imaging (ECG, Echocardiography, Coronary Angiography), and follow-up data. Peripheral blood samples (plasma, serum, and PBMCs) will be systematically collected from 1,200 patients, processed under strict Standard Operating Procedures (SOPs), and stored in a centralized, automated biobank. Multi-omics and High-Risk Subgroup Analysis: A targeted sub-cohort of 200 patients representing distinct clinical phenotypes (approximately 70 with vulnerable plaques, 60 with MINOCA, and 70 with borderline lesions) will undergo comprehensive multi-omics profiling. This includes: 1. Genomics: Whole Genome Sequencing (WGS) to identify genetic variants associated with lipid metabolism and AMI susceptibility. 2. Transcriptomics: RNA-seq to map gene expression profiles of immune cells. 3. Single-cell RNA-seq (scRNA-seq): Conducted on 80 high-risk patients to analyze the heterogeneity of peripheral immune cells and inflammatory responses. 4. Proteomics: Olink multiplex assay to quantify over 1,000 circulating proteins to identify early warning biomarkers for plaque rupture and myocardial injury. Advanced Imaging Radiomics and AI Integration: The study incorporates state-of-the-art cardiovascular imaging analysis, including Non-invasive CCTA radiomics, Optical Coherence Tomography (OCT), Intravascular Ultrasound (IVUS), Quantitative Flow Ratio (QFR), and novel Intracoronary Fluorescence Imaging. By utilizing artificial intelligence (AI) and machine learning algorithms, the study will extract multidimensional imaging and functional features to assess plaque vulnerability, vascular hemodynamics, and stent healing. Study Objectives: 1. To build a standardized, multi-dimensional clinical and multi-omics database for Chinese AMI patients. 2. To identify and validate novel circulating biomarkers and therapeutic targets for AMI and its complications. 3. To develop and validate AI-driven, multi-modality diagnostic models for accurate risk stratification and prediction of Major Adverse Cardiovascular Events (MACE). 4. To provide robust clinical evidence and high-quality data to accelerate the translation of innovative cardiovascular medical devices and personalized therapies.

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