This study examined whether genetically predicted SES determinants cause an increase in the incidence of MI using a two-sample MR method. Four large GWASs chose candidate IVs for TID, education, family income, and heavy physical work. The IVs had a better possibility of correctly predicting the SES components (F-statistics > 10). We demonstrated that genetically predicted socioeconomic status, including full-time completed education age, family income, and heavy physical work directly associated with MI risk (Fig. 2). Genetically predicated TID was not associated with MI risk.
According to reports, due to their heightened vulnerability, individuals with ST-segment elevation myocardial infarction (STEMI) in rural areas and lower SES groups require specific attention[19]. Additionally, a large observational study revealed that those with low SES had a greater frequency of cardiovascular illness[20]. Social determinants of health have been studied in the incidence and survival of myocardial infarctions, including socioeconomic situation (money, education), disadvantaged neighborhoods, immigration status, social network, and social support[21, 22].
Our work offers a deeper understanding of earlier observational findings using the two-sample MR methodology[23, 24, 5]. The following variables may influence the connection between SES and myocardial infarction. Living in a place with fewer financial means may make it more challenging to get routine or follow-up care or start treating an acute sickness immediately. Additionally, a few feasible socio-behavioral channels by which SES may still influence health outcomes for older people, including the absence of social networks, persistent stress, and access to health information[25]. Educational achievement is an additional crucial factor because, in high-income nations, there is a long-established negative relationship between educational attainment and cardiovascular disease[26]. Chaix et al. studied 52,084 people in Sweden to investigate the relationship between neighborhood socioeconomic disadvantage and the risk of MI. As the level of socioeconomic deprivation in the area rose, so did the incidence of myocardial infarction. A hazard ratio of 1.7 was found for high vs. low neighborhood socioeconomic disadvantage (95%CI = 1.4-2.0)[27].
According to our research, the risk of MI is inversely correlated with family income. The results of this investigation are consistent with earlier observational studies. Stjarne et al. researched the association between SES and MI in Stockholm County, Sweden. They found that the relative risk of MI depends on the environment and the availability of socioeconomic resources. In low-income areas, the incidence rate ratio was 1.88 for women and 1.52 for males compared to high-income categories[28]. Population-based cohort research conducted in four American towns examined the relationship between neighborhood median family income and risk of MI. There was a higher risk of myocardial infarction among inhabitants of low household income areas than among residents of higher household income neighborhoods, regardless of race or gender[29]. Our MR analysis confirmed these earlier findings and showed a causal relationship between family income and MI risk. Our results indicated that residents with low incomes should receive special consideration.
An established association between educational attainment and CVD has existed for many years, and recent population-based research continues to show the importance of this link and its underlying mechanisms. Numerous reports have demonstrated that persons with lesser levels of education are more susceptible to AMI[30–32]. In a thorough survey by Woodward et al., nearly 90,000 persons in Australia and New Zealand were investigated. It was discovered that those with elementary education had a higher risk of CVD, cardiovascular mortality, and all-cause death than those with tertiary education[26]. Poorer long-term outcomes following AMI are also predicted by lower educational attainment[33]. Likewise, our study confirmed that lower completed full-time education age could increase the risk of MI. Health may be impacted by education in several different ways. More CVD risk factors are often present in those with lower levels of education. Therefore, People with limited education should be given more consideration.
From 1984 to 2000, a cohort of 1755 males from eastern Finland, ranging in age from 42 to 65, was examined. According to the study, there is an elevated risk of cardiovascular disease in males who engage in heavy physical work[34]. The Norwegian HUNT study included 37,300 male and female participants, with an average follow-up of 12.4 years. The research showed that people with heavy physical work and metabolic syndrome had an HR of 3.02 (95%CI = 1.93–4.75) for cardiovascular death. Our MR analysis showed that backbreaking physical work was causally associated with MI risk.
The aforementioned observational study on the association between SES variables and MI is not a causal link because of bias. Different levels of adjustment have been made for these risk factors in earlier research. Previous observational studies had a tough time eliminating confounding risk factor violations. However, using MR analysis and a superior study design, we definitively revealed causation apart from bias in the current studies.
Our study has several advantages. Our study may initially approximate randomized controlled trials in observational settings due to the MR method. Randomized controlled trials are frequently used to investigate causes but are often expensive and challenging. However, while SNPs are given randomly, confounding bias can be avoided in MR investigations. Unlike previous observational research, MR additionally controls the impact of the reverse causal chain. Secondly, according to our results, individuals with low socioeconomic status should be paid more attention to prevent myocardial infarction. The government should pay more attention to increasing investment in education, improving people's living standards, improving the level of social-environmental governance, and reducing the risk of myocardial infarction.
There were, however, certain restrictions. Initially, the European population served as the source of all GWAS data. If our results would hold in other individuals remained to be seen. Second, since MR analyses developed causal hypotheses by taking advantage of the genetic variations' random distribution, it proved challenging to separate mediation and pleiotropy using MR techniques entirely. Our genome's significant variations likely impact one or more phenotypes. Thirdly, this study was a lack of mediator analysis.