Abstract
BACKGROUND: Myocardial infarction (MI) incidence is increasing in adults aged < 40 years, however, as many as 25% may occur with no traditional risk factors. AIM: To look at non-traditional risk factors for early onset MI. METHODS: Based on the guidance of PRISMA-2020, search of PubMed, EMBASE, Web of Science, Scopus and Cochrane Central Register of Controlled Trials between April 2015 and April 2020 for observational studies focusing on the association between non-traditional risk factors and MI in adults aged 18-40 years. Two reviewers independently screened studies, extracted data and checked quality using Newcastle-Ottawa Scale or Agency for Healthcare Research and Quality checklist. The protocol was registered in PROSPERO, No. CRD420251061098. RESULTS: Thirteen studies (7 cohort, 4 case-control, 2 cross-sectional) from 11 countries met inclusion criteria with a sample size ranging from 154 participants in a pilot case-control study to 5.7 million people in a United States National Inpatient Sample analysis. Psychosocial factors showed consistent associations: Depression showed an MI risk 1.6-3.1-fold higher and being unpartnered was associated with a post-MI readmission risk that was 28%-31% higher. Autoimmune conditions had the greatest associations, with human immunodeficiency virus infection quadrupling odds of MI (4.06), and the risk of systemic lupus erythematosus doubling (2.12). Obstructive sleep apnea increased major adverse cardiovascular events by almost four times (hazard ratio = 3.87). Adhering to the Mediterranean diet was protective (odds ratio = 0.55). Accelerated biological aging (shortening of telomeres) separated young patients with MI from controls. Traditional risk factors did not account for up to 30% of MI cases in each of the cohorts. Most studies were of moderate to high quality, although causes of heterogeneity in design and age stratification of participants mixtures limited causality inference. CONCLUSION: Non-traditional psychosocial, autoimmune, inflammatory, and lifestyle factors play an important role in the risk of MI in young adults. Integrating these factors into risk prediction models could improve the early identification of high-risk individuals and target prevention strategies for this vulnerable population.