Macrophage migration inhibitory factor (MIF) has been reported as an inflammatory cytokine in many inflammatory diseases, including rheumatoid arthritis and ischemic diseases.

However, dynamic changes of MIF within the first 24 hours on admission and potential prognostic significance following ST-elevation myocardial infarction (STEMI) have been little known. 

MIF level and its potential diagnostic and prognostic value  

In this study, they examined the dynamic change of MIF level and its potential diagnostic and prognostic value after the onset of STEMI. Plasma MIF levels were evaluated in symptomatic subjects who received coronary angiogram with a median 27 months follow-up for the development of major adverse cardiovascular events (MACEs).

Myocardial infarction (MI) is 1 of the clinical manifestations of coronary heart disease (CHD). By 2030, it is estimated 23.3 million will die annually from cardiovascular disease.

ST-segment elevation myocardial infarction (STEMI) is a severe heart attack caused by a prolonged period of blocked blood supply that affects a large area of myocardium and is linked to high incidence of persistent and total coronary occlusion.

Of all 993 subjects, patients with STEMI showed a significantly higher MIF levels than in patients with non-ST elevation acute coronary syndrome, stable angina, and normal coronary artery, respectively (P < .01).

Plasma MIF levels elevated as early as 12 hours post-onset of STEMI and peaked rapidly within 24 hours, and remained elevated from about day 5 till day 9 during hospitalization.

In multivariate analysis, MIF was associated with a decreased risk of MACEs occurrence in STEMI patients after adjustment for traditional cardiovascular risk factors [hazard ratio 0.81, (0.72–0.90), P < .001].

The ROC curve for MACEs was 0.72 (95% CI 0.62–0.80, P < .001) and 0.85 (95% CI 0.80–0.90, P < .001) using Framingham risk factors only and combined with MIF, individually.

Measurement of MIF adds potential information for the early diagnosis of acute STEMI and significantly improves risk prediction of MACEs when added to a prognostic model with traditional Framingham risk factors.