Biomarkers Distinguish Between Subtypes of Myocardial Infarction and Injury

Close Up Of Female Body With Hand On Chest. Woman Suffering From Painful Feeling, Having Health Issues.
Researchers assessed the value of a biomarker model in distinguishing subtypes of myocardial infarction and injury.

Among 29 biomarkers identified, 7 were found to distinguish between myocardial infarction (MI) subtypes and myocardial injury, according to research results published in Journal of the American College of Cardiology.

The female sex, lack of typical radiating chest pain, and lower baseline troponin concentration were previously identified as predictors of type 2 MI; however, the ability of the model to distinguish MI subtypes based on these variables was found to be limited.

To address this clinical need, the researchers of the current study analyzed a large panel of 29 biomarkers in a contemporary cohort of patients with suspected MI.

Data from the Biomarkers in Acute Cardiac Care (BACC) study ( Identifier: NCT02355457) — an ongoing, prospective cohort study — were used. Adult patients were enrolled at the emergency department of a single center in Germany between 2013 and 2018. Participants underwent standardized collection of clinical variables, electrocardiogram, vital parameters, and blood samples at admission.

A total of 748 participants had available biomarker measurements, of whom 138 were diagnosed with MI (type 1 MI, n=107, type 2 MI, n=31); 221 patients were diagnosed with myocardial injury. Median age was 64 years and 63.1% of patients were men. A total of 65.9% of patients had hypertension and 37% had hyperlipoproteinemia; 12.7% had diabetes, 27.1% were current smokers, and 16.6% had a history of MI.

Compared with patients diagnosed with MI, those diagnosed with myocardial injury were older and had worse renal function.

The majority of biomarkers demonstrated low or moderate correlations. Strong positive correlations were identified for Apo C-I and Apo A-II, Apo C-I and Apo C-II, myoglobin and FABP, TNFR2 and thrombomodulin, TNFR2 and tissue inhibitor of metalloproteinases 2, and TNFR2 and vascular cell adhesion molecule 1.

Using a weighted gene coexpression network analysis, the investigators identified 4 clusters of biomarkers. The first module included transthyretin, Apo H, Apo A-I, Apo A-II, Apo C-I, and Apo C-III. The second model included hs-cTnI, FABP, and myoglobin. The third module included EN-RAGE and lectin-like oxidase low-density lipoprotein receptor 1, and the fourth model included copeptin, CRP, adiponectin, alpha-2-macroglobulin, ferritin, kidney injury molecule-1, midkine, NT-proBNP, osteopontin, PARC, tissue inhibitor of metalloproteinases 1, thrombomodulin, TNFR2, vascular cell adhesion molecule-1, and soluble urokinase-type plasminogen activator receptor.

Results of a univariable logistic regression analysis showed adiponectin, Apo A-I, Apo A-II, and hs-cTnI as significant distinguishers between type 1 and type 2 MI. Following backward selection, 4 biomarkers were identified of which 2 were significant discriminators: hs-cTnI and NT-proBNP (odds ratios [ORs], 2.15 and 0.52, respectively).

In 1 model, the combination of these biomarkers resulted in an area under the curve (AUC) of 0.82. Internal cross-validation confirmed these findings.

When evaluating each selected biomarker alone, hs-cTnI resulted in the highest AUC (0.74), which increased to 0.77 and 0.81 after the addition of NT-proBNP and copeptin, respectively.

After adjustment for sex, minor changes were noted and the male sex was not identified as an independent distinguisher.

Additional results of univariable logistic regression analyses showed that a number of biomarkers, including adiponectin, FABP, kidney injury molecule-1, myoglobin, and NT-proBNP, were significant distinguishers. Backward selection identified 6 biomarkers, 4 of which were statistically significant: hs-cTnI, NT-proBNP, copeptin, and PARC (ORs, 1.78, 0.66, 1.59, and 0.47, respectively).

The combination of these selected biomarkers in a model resulted in an AUC of 0.84, confirmed after internal cross-validation.

Study limitations included the small sample size and the limited number of patients with type 2 MI, as well as a lack of options for external validation and the knowledge that many of these investigated biomarkers were not routinely used in clinical practice.

“We identified 7 out of 29 biomarkers as the most relevant discriminators for subtypes of MI or myocardial injury,” the researchers concluded. “Regression models based on these biomarkers allowed a good discrimination and could improve diagnostic evaluation in emergency departments.”

Disclosure: This clinical trial was supported by Abbott Diagnostics. Please see the original reference for a full list of authors’ disclosures.


Neumann JT, Weimann J, Sörensen NA, et al. A biomarker model to distinguish types of myocardial infarction and injury. J Am Coll Cardiol. 2021;78(8):781-790. doi:10.1016/j.jacc.2021.06.027