Predictive Model for Acute Heart Failure in Patients with Acute Myocardial Infarction and Type 2 Diabetes Mellitus Based on Energy and Adipokine Metabolism Indicators

Mariia Коteliukh

doi:10.3889/oamjms.2022.10090

Authors

Mariia Коteliukh Department of Internal Medicine No. 2, Clinical Immunology and Allergology, Kharkiv National Medical University, Kharkiv, Ukraine

DOI:

https://doi.org/10.3889/oamjms.2022.10090

Keywords:

Diabetes, Metabolism, Myocardial infarction, Heart failure, Prognosis

Abstract

BACKGROUND: Acute heart failure (AHF) is one of the early complications of acute myocardial infarction (AMI) in diabetic patients. Evaluation of biomarkers of energy and adipokine metabolism can help in the early identification of diabetic patients at risk of AHF.

AIM: The present study is aimed to predict the development of AHF in diabetic patients with AMI based on energy and adipokine metabolism parameters.

METHODS: A total of 74 diabetic patients with AMI were examined between September 1, 2018, and December 31, 2020. Serum adropin, irisin, and C1q/TNF-related protein 3 (CTRP3) levels were measured by enzyme-linked immunosorbent assay. To predict AHF development in AMI patients, generalized linear mixed model (GLMM) was applied.

RESULTS: The serum concentrations of adropin, irisin, and CTRP3 have been found to be reduced in diabetic patients with AMI and AHF. The accuracy of predicting AHF Killip Class 1 was 96.7%, and the accuracy of prediction for AHF Killip Class 2 was 57.1%, that is, the model was poorly sensitive to this level of complications. The prediction accuracy for AHF Killip Class 3 was 80%, that is, the model was highly sensitive to complications of this level, and for AHF Killip Class 4 – 100% being the maximum level of the model sensitivity.

CONCLUSIONS: Low serum concentrations of adropin, irisin, and CTRP3 indicate an imbalance in energy and adipokine homeostasis. The constructed model predicts the probability of AHF development with high accuracy of 91.9% in diabetic patients with AMI.

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