UNdeRstAnding Novel Variants in AcutE MyocardiaL Infarction in Young Adults

Cardiovascular disease (CVD) imposes significant mortality and morbidity worldwide. Large population-based studies in Western cohorts have formed the foundations of our knowledge on the traditional risk factors of cardiovascular disease. Despite this, large gaps in our knowledge of CVD still exist. The clinical conundrum of the extremes of spectrums of CVD continues to baffle clinicians and researchers alike. These include patients without any major cardiovascular (CV) risk factors developing acute myocardial infarction (AMI) at a relatively young age (<50-60 years old). At the other end of the spectrum, there are patients with a "full-house" CV risk factors with minor or no coronary artery disease. These suggest the presence of other factors that predispose these patients to AMI.Recent advancements in technology have led to exciting developments in our understanding of the development and prevention of CVD and AMI. The use of "big data" and "deep learning", advancements in genomics, metabolomics, and proteomics have the possibility of transforming this field. Modern prospective population-based studies aim to reclassify CV risk using integrated clinical and molecular biosignatures. However, these studies are based primarily in Western populations. Ethnic differences in CVD exist and currently in Asia, there is a dearth of such advanced data. In addition, in the Singapore Myocardial Infarction Registry (SMIR), it was reported about 1 in 4 of the AMI patients were aged 60 years or less from 2016 to 2020. The average number of AMI cases were about 900 and 2000 in patients aged < 50 years and aged between 50-59 years respectively. In term of ethnic distribution of the AMI population, about 50% of young AMI patients were Chinese, followed by Malays and Indian. In this study, the investigators specifically aim to identify novel gene variants and novel protein/metabolite candidates associated with the onset of MI in relatively young Chinese,Malay and Indian patients with minimal standard CV risk factors such as diabetes, obesity, hypertension and hypercholesterolaemia. Through these omics-based analyses, these may therefore seek to help answer some of these questions and better improve our understanding of the development of CVD and MI, potentially developing novel preventive measures to reduce the risk or delay the onset and tailoring management plans to improve treatment outcomes and reduce adverse events for the young patients.