Hypothesis We hypothesize that several distinct dynamic biological processes are active in the months leading up to a myocardial infarction, and that biomarkers of these processes are identifiable during that time.
   
Aim   To develop methods to find people who are at risk of an imminent myocardial infarction.
   
Design     Case-cohort study of multiple large high-quality European cohorts.
 
Sample  Persons in general population cohorts that develop a first myocardial infarction within the first six months after the baseline examination of the cohort, and up to four cohort representatives per case.
   
Methods  Data and samples are gathered from study participants’ baseline examinations. Data are harmonized to enable individual participant meta-analysis. Samples from all study participants are analyzed for several hundred proteins and metabolites.
   
Timeline Samples collection and proteomics analyses performed during 2017-2018, Harmonization performed in 2018-2019. Metabolomics analyses performed in 2019. Statistical analyses in 2019-2020.
   
Contact  For further information please contact us.

Rationale

The costly failure to identify persons at high risk of a myocardial infarction

Myocardial infarction is the leading cause of death globally, and is increasing as a cause of death.1 Prevention of these deaths is highly prioritized,2 but has hitherto had limited success. An inherent problem is the difficulty to identify the persons that are at highest risk of an imminent myocardial infarction. Sudden cardiac death is sometimes the first manifestation of ischemic heart disease, and traditional risk factors can be identified beforehand in a minority of these persons.3 Population strategies are one way of preventing a fraction of all myocardial infarctions, but the high-risk strategy, as it is applied today, is clearly insufficient.4 This may be due not to a deficiency of the high-risk approach per se, but to the tools available for identification of persons at high risk. More specifically, the development may have been hampered by the way risk markers have traditionally been identified.

Previous strategies for finding biomarkers too crude

The term risk factor was coined fifty years ago,5 and soon thereafter it was established that the majority of cardiovascular events can be predicted over the long term using a mere handful of risk factors. These include a few biochemical markers such as total and HDL-cholesterol, but markers such as age, sex, blood pressure, diabetes and smoking account for the largest part of the equation. Ever since, people have strived to improve risk prediction for cardiovascular events above and beyond a score calculated using these few markers.6 Three decades ago, more than 200 biomarkers had been suggested to be useful for identifying persons with high risk of coronary disease,7 and the number has been growing exponentially since then. So with that smorgasbord of biomarkers, why has the identification of people at high risk of a myocardial infarction not improved substantially in the last half century?

Natural variability in a biomarker over time introduces random misclassification. Part of the reason that the risk factors identified half a century ago are still useful is that they (apart from blood pressure) are very stable over time. The problem is that the period leading up to a myocardial infarction is highly dynamic. For instance, traumatic events such as a cancer diagnosis or loss of a spouse dramatically increase the risk of a myocardial infarction acutely.8, 9 And the degree of stenosis in the culprit coronary artery lesion appears to increase in the months just before the myocardial infarction.10 Previous population-based studies have largely missed this temporal connection, as most biomarkers have been investigated in settings of several years of follow-up11-13 due to the low number of patients with a myocardial infarction shortly after baseline. Nevertheless, the general population without a previous myocardial infarction is the relevant target population, as patients with established ischemic heart disease should already be highly prioritized for preventive measures. Hence, a radically new approach is needed.

The window of opportunity just before a myocardial infarction

In order to investigate biomarkers signalling an impending myocardial infarction, a large population-based sample of people with blood samples drawn in the months before their first myocardial infarction is needed. It has to be large, in order to provide the statistical power needed for robust and replicable findings, especially if using -omics discovery techniques. No such single sample exists. Hence, a collaborative meta-analytic strategy is the only solution to the problem. With the MIMI study, we are creating such an opportunity.

The MIMI study has the potential to create new knowledge about the processes leading up to a myocardial infarction. If translation to clinical practice is possible, identifying risk factors that act in the short term may completely change the playing field for prevention. Patients and doctors will likely accept more aggressive preventive strategies if they know that the risk of a myocardial infarction in the short term is increased.

References

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