Безопасность и риск фармакотерапии (Mar 2022)
Circulating MicroRNAs Are Promising Biomarkers for Assessing the Risk of Antipsychotic-Induced Metabolic Syndrome (Review): Part 1
Abstract
INTRODUCTION. Antipsychotic-induced metabolic syndrome (AIMetS) is a common adverse reaction to the pharmacotherapy of psychiatric and addiction disorders. However, interindividual variability in the metabolism of antipsychotics may limit the sensitivity and specificity of known blood-based biochemical biomarkers of AIMetS for assessing the safety of psychopharmacotherapy and the risk of AIMetS in patients with schizophrenia spectrum disorders. In recent years, circulating microRNAs have been considered as new and promising epigenetic biomarkers of AIMetS.AIM. This study aimed to evaluate the potential of circulating microRNAs as epigenetic biomarkers for the prediction and early diagnosis of AIMetS.DISCUSSION. The authors analysed the results of academic and clinical research published from 2012 to 2024 with a focus on the role of circulating microRNAs involved in the key AIMetS pathogenesis and progression pathways. This review presents novel international approaches to using primary and additional clinical and biochemical biomarkers of AIMetS and demonstrates the advantages of microRNAs as epigenetic biomarkers of AIMetS. The article summarises data on the roles of microRNAs in the mechanisms of AIMetS development (oxidative stress, systemic inflammation, adipocyte differentiation, lipid and glucose metabolism, appetite regulation, and changes in neuropeptide Y and orexin expression, leptin sensitivity, and testosterone, thyroid and parathyroid hormone levels).CONCLUSIONS. Detecting changes in the expression of circulating microRNAs in easily accessible samples (blood, saliva, urine, etc.) is a promising alternative method for predicting and diagnosing AIMetS. The second part of this review will explore the role of circulating microRNAs as epigenetic biomarkers for developing the main manifestations of MetS and AIMetS and will classify microRNA signatures according to the risk of developing AIMetS.
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