Cohort profile: A longitudinal multi‐omics cohort of patients with acute coronary syndrome

The longitudinal multi-omics cohort of patients with acute coronary syndrome (LM-ACS) is designed as a real-world prospective cohort of patients with ACS requiring coronary angiography. This study aims to enroll 50,000 participants, with a thorough collection of phenotypic data and multi-omics analyses performed on biological samples. Additionally, long-term follow-up will be conducted to track the incidence of major adverse cardiovascular events (MACEs) over the participants' lifetimes. For this purpose, three follow-up scenarios have been established for ACS patients, differentiated based on whether the patients had acute myocardial infarction (AMI) or unstable angina (UA), and whether they underwent percutaneous coronary intervention (PCI) surgery. The LM-ACS cohort seeks to create a unique resource to advance our understanding of the etiology and clinical outcomes of ACS. Acute coronary syndrome (ACS) remains a leading cause of global morbidity and mortality, with over seven million new cases annually and an anticipated surge to 23 million cases in China by 2030 [1, 2]. Despite significant advances, the risk burden for ACS patients remains high. These patients continue to experience elevated 30-day readmission rates post-discharge [3], particularly those with acute myocardial infarction (AMI). For patients with ST-segment elevation myocardial infarction (MI), the first-year mortality rate is approximately 7%−18% [4]. Survivors of acute events face increased risks of major adverse cardiovascular events (MACEs), including thromboembolic events and postischemia heart failure [5, 6]. Understanding the mechanisms and risk factors associated with ACS and MACEs is crucial. The cardiac repair process following myocardial ischemia involves complex inflammatory and reparative phases influenced by factors such as concurrent risk factors, lifestyles, the gut microbiome, and genetic predispositions [7-9]. However, translating large-scale therapeutic strategies into clinical practice remains challenging due to the intricate nature of the healing process and our incomplete understanding of it. Taking advantage of the recent advancements in omics technologies and big data analytics, it has become feasible to obtain a more comprehensive understanding of the factors and physiology involved in the onset and prognosis of ACS. Notably, proteomics has identified several biomarkers consistently predicting outcomes after AMI [10]. Metabolomics, by analyzing metabolic profiles, offered valuable insights for early diagnosis and understanding the molecular pathogenesis of ACS. Integrating the findings from multi-omics facilitates the elucidation of dynamic changes in metabolic pathways associated with ACS, providing crucial cues for prognosis assessment, timely intervention, and the development of personalized treatment strategies [11]. Nevertheless, no research to date has systematically monitored the recovery process of patients with ACS and the factors correlated to the risk of MACEs through comprehensive longitudinal follow-up cohorts and multi-omics analyses. Further investigations, particularly in large-scale longitudinal clinical cohorts, are imperative to deepen our comprehension of the intricate mechanisms involved in the prognostic course of ACS, with the ultimate goal of improving outcomes. As summarized in Table S1, numerous cohorts worldwide have been established to investigate the risk factors contributing to the prevalence and prognosis of coronary artery disease. Despite the fact that the majority of patients with ACS have underlying coronary artery disease, not all individuals with coronary artery disease experience acute events [12]. Existing evidence suggests that coronary artery disease is associated with ACS, but there may also be additional factors that specifically promote plaque rupture and thrombosis. Current cohort studies focusing on ACS patients are primarily based on clinical registry systems, lacking cohorts with longitudinal follow-up and diverse biological samples that enable multi-omics profiling. These unexplored areas hold significant potential for advancing our understanding of the molecular basis of ACS occurrence and prognosis, thereby enhancing our knowledge of its pathophysiology and opening new avenues for targeted interventions. The LM-ACS cohort was designed as a real-world prospective cohort of patients with ACS. Patients diagnosed with ACS requiring coronary angiography were consecutively enrolled. As illustrated in Figure 1, which details the design infrastructure of this cohort, the primary aims of this cohort are to explore the factors and mechanisms contributing to the risk of MACEs, including a composite endpoint of cardiac death, nonfatal MI, nonfatal ischemic stroke, heart failure, and peripheral artery revascularization [5, 6]. Postinfarction cardiac remodeling and heart failure are the leading cause of MI-driven death during the past decades [13]. We also aim to take advantage of the development of multiple-omics and longitudinal multiple-time follow-up to characterize complex biological processes of postischemic cardiac remodeling and heart failure. In addition, we believe that the rich data set with deep phenotypes and multiple-omics analysis of patients will facilitate the study of more cardiovascular-related scientific questions, such as the mechanisms related to the onset of ACS. To explore the factors and mechanisms that contributed to the risk of MACEs after ACS. To explore the risk factors for premature death after ACS. To help map the spectrum of cardiac remodeling and heart failure development after acute cardiac ischemia. To provide a basis for analyzing the role of drug treatment formulation in long-term outcomes of ACS patients. To create a unique resource to explore the risk factors and mechanisms contributing to ACS. Cardiologists recruited patients diagnosed with ACS who required coronary angiography at four hospitals in Shandong Province, China (Figure 1B). The clinical diagnosis of ACS, including AMI or unstable angina (UA), was determined by a panel of cardiologists (≥2 independent cardiologists) following thorough physical examinations and in accordance with established guidelines [1]. The inclusion and exclusion criteria are detailed in Figure 1C. This study registered at ClinicalTrials.gov (identifiers NCT05699629 and NCT05719545), was approved by the ethical committees of Shandong First Medical University. Patients were recruited from four hospitals in Shandong Province, China, and assessed by trained cardiologists who obtained informed consent. Basic information, including demographics, lifestyle, medication history, chronic disease history, and family disease history, was collected through face-to-face interviews. Nurses measured weight, height, and blood pressure. Fasting blood samples were collected in the morning before coronary angiography and postintervention for patients undergoing percutaneous coronary intervention, while stool samples were collected during hospitalization. For AMI patients, recruitment began after standardized treatment. The collection of clinical data and biological samples is illustrated in Figure 1D. As of October 2023, 8615 ACS patients have been enrolled. To screen for SNPs associated with MACEs post-ACS, with a minimum minor allele frequency of 5%, a statistical significance threshold of p value < 0.05, and an effect value threshold of OR > 1.1, a sample size of approximately 48,000 was calculated using the sample size calculation formula from the Department of Genetic Statistics at the University of Michigan School of Bioinformatics with the GAS calculator software [14]. Considering potential exclusions, the cohort's sample size was set at 50,000, with recruitment expected to be completed by the end of 2026. In alignment with the latest ACS rehabilitation guidelines established by the American Heart Association and the European Society of Cardiology, we devised the following follow-up plan with clinical guidance [15, 16]. All patients received standardized treatment both during hospitalization and during follow-up. Our follow-up strategy integrates both remote consultations and in-person hospital visits to conduct lifestyle investigation, monitor medication adherence, and comprehensively assess cardiac function change at 1-, 4-, 12-, and 24-month post-hospitalization. During the telehealth consultation, patients will be encouraged to address their overall well-being, lifestyle choices, adherence to prescribed medications, and any concerns or symptoms encountered since their last visit. Following this, an in-person hospital visit will be scheduled for a comprehensive assessment, including cardiac evaluations, such as electrocardiogram and echocardiography. Biological samples will be collected following the same procedure as the baseline. Tests for cardiac biomarkers and glucose/lipid profiles will be conducted to monitor overall health status. After 2 years, the patients will be followed up by the health system to monitor changes in their health status. The overview of the follow-up plan and detailed information collected was shown in Figure 1E and Table S2. The primary follow-up endpoint of this cohort is defined as all-cause death or cardiovascular death, while secondary endpoints encompass nonfatal MACEs such as nonfatal ischemic stroke, shock, heart failure, and recurrent ACS. Additionally, changes in cardiac structure and function will be longitudinally tracked at specific time points using echocardiography and cardiac magnetic resonance imaging (CMR), offering valuable data for postischemia cardiac remodeling. Furthermore, we anticipate that the extended follow-up of medication usage will provide comprehensive insights into their long-term impact on cardiovascular outcomes and potential variations in the occurrence rates of specific adverse events in ACS patients. The blood samples and fecal specimens are collected both during hospitalization and follow-up visits. We conducted comprehensive multi-omics research and health assessments to analyze biological samples and evaluate participants' health status. Blood and fecal samples were collected both during hospitalization and follow-up visits, with rigorous standardization applied to sample processing procedures, equipment, and materials to ensure quality control. Fasting blood samples were collected by nurses in the morning and temporarily stored at 4°C. Within 4 h, the samples were transported to the laboratory and centrifuged at 3000 rpm for 15 min. Subsequently, both plasma and blood cell samples were aliquoted and stored at −40°C for further analysis. During hospitalization, fecal samples were collected, aliquoted, and stored at −40°C within 4 h of production. For follow-up visits, fecal samples were collected at home, transported at room temperature to the laboratory, aliquoted, and stored at −40°C within 2 days of collection. All biological samples mentioned above were temporarily stored at −40°C in the hospital and shipped on dry ice to Shandong First Medical University, where they were stored at −80°C. Clinical measurements of blood samples included cell types and counts, blood glucose, lipid profiles, and markers indicative of heart, liver, kidney, and thyroid functions. Additionally, blood samples stored in the Biobank were used to generate multi-omics data, including genomics, proteomics, and metabolomics. Proteomics measurements were performed using the Olink® Explore 3072 platform, and whole-genome sequencing was conducted with the Illumina NovaSeq. 6000 platform and targeted metabolomics profiling was carried out using the UHPLC-QTRAP MS system. Fecal samples were used for bacterial culturomics and microbiome sequencing, including 16S rRNA gene sequencing, metagenomics, and ITS2 sequences. Transcriptome analysis was conducted using whole blood samples and the Affymetrix Human Transcriptomic Array 2.0. We also conducted comprehensive health assessments using questionnaires and advanced imaging techniques. The questionnaire survey collected data on demographics, lifestyles, dietary quality, and anxiety levels using established tools such as the diet quality score questionnaire, Pittsburgh Sleep Quality Index, Patient Health Questionnaire-9, and Generalized Anxiety Disorder Questionnaire [17, 18]. All questionnaires were completed during face-to-face appointments and managed on an online platform to ensure data reliability and completeness. Cardiac structure and function were assessed using standard echocardiography, measuring parameters such as left ventricular mass, left ventricular mass index, and relative wall thickness, allowing classification into different geometric patterns [19]. Additionally, CMR was used to analyze AMI, assessing myocardial scar, microvascular obstruction, and intra-myocardial hemorrhage. CMR was performed within 7 days of admission and repeated at the 4-month follow-up. Notably, myocardial infarct size was quantified using T2-weighted imaging and late gadolinium enhancement images. These images were analyzed using a 3.0T scanner and CVI42 software to predict adverse cardiac remodeling and MACEs [20]. Up to October 2023, a total of 8615 ACS patients have been enrolled from all four participating centers. Among these patients, 61.53% (5301 out of 8615) were male (Figure 2), with an average age of 62.99 ± 10.68 years. Approximately 20.30% (1749 out of 8615) were younger than 55 years old, classified as early-onset ACS. Tables S3 and S4 present the baseline characteristics of the 2927 patients recruited from Central Hospital Affiliated with Shandong First Medical University, as well as the echocardiographic data of 1904 patients obtained within 7 days of recruitment. Our multicenter prospective cohort provides a real-world perspective on cardiovascular health and has become a foundational resource for advancing our understanding of ACS. We are expanding it into a comprehensive biobank, continuously increasing the sample size and collecting detailed phenotypic follow-up data. This cohort includes a variety of phenotypic data, such as detailed biochemical parameters and comprehensive questionnaires capturing psychosocial and nutritional status. Additionally, we are generating multiple "omics" data sets, including genomics, metabolomics, proteomics, and gut microbiome data. This rich and multi-layered data set serves as a robust resource for clinical research, potentially addressing key questions about the mechanisms underlying ACS onset and recurrence, as well as the dynamic changes in cardiac structure and function following myocardial ischemia. While our cohort offers valuable insights, it does have some limitations, such as the lack of information on patients' conditions before ACS onset. We anticipate that collaboration with other centers will enhance the cohort's size and representativeness, thereby broadening the scope and impact of cardiovascular research. Detailed procedures for biological sample collection, biological sample assessment, questionnaire-based surveys, cardiac echocardiography and definition of left ventricular geometry, and CMR imaging are comprehensively described in the Supporting Information Methods. Rong Huang: Conceptualization; supervision; writing—original draft; funding acquisition; investigation; visualization; writing—review and editing; resources; project administration; data curation; methodology; formal analysis; validation. Jie Li: Investigation; writing—original draft; writing—review and editing; visualization; data curation; formal analysis; validation. Keqing Hu: Methodology; project administration; investigation; data curation. Rui Xu: Project administration; investigation; data curation. Haitao Yuan: Investigation; project administration; data curation. Jiangrong Wang: Investigation; project administration; data curation. Mei Gao: Investigation; project administration; data curation. Qiang Xiao: Investigation; project administration; data curation. Bingqing Dong: Investigation; project administration; data curation. Furen Zhang: Project administration; investigation; data curation. Fengling Lai: Investigation; project administration. Luxiang Shang: Investigation; project administration. Siyu Hou: Investigation; project administration; data curation. Lingjun Tong: Investigation; project administration. Kaixin Zhou: Methodology; project administration. Guohai Su: Methodology; resources; funding acquisition; investigation; project administration; data curation. Tao Xu: Conceptualization; methodology; writing—original draft; writing—review and editing; supervision; funding acquisition; resources. The authors sincerely thank all the subjects involved in the cohort and all the members who participated in the sample and data collection. The authors would like to thank the Medical Science and Technology Innovation Center of Shandong First Medical University for providing facilities for this research. Thank all the hospital staff for their assistance in recruiting in-hospital ACS patients. This work was supported by Academic Promotion Program of Shandong First Medical University (No. 922-001003130185RC), and National Natural Science Foundation of China (No. 32200635). The authors declare no conflict of interest. This cohort was approved by the ethics committee of Shandong First Medical University (No. R202211150140); Central Hospital Affiliated to Shandong First Medical University (No. 2021-206-02) and Qianfoshan Hospital (No. YXLL-KY-2023(022)). Informed consent was obtained from all participants of this study. The data and scripts used in this study can be found at https://github.com/Xiaoshi990730/LM-ACS. Supplementary materials (methods, tables, graphical abstract, slides, videos, Chinese translated version, and updated materials) may be found in the online DOI or iMeta Science http://www.imeta.science/imetaomics/. Table S1: A brief summary of CAD and ACS cohorts. Table S2: Information collected at baseline and during follow-up. Table S3: Baseline characteristics of the involved subjects. Table S4: Baseline cardiac structure parameters of the recruited subjects. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.