The article provides the overview of biomarkers currently being studied as potential methods of diagnostics of various neuropsychiatric disorders, including schizophrenic and affective spectrum disorders. In addition to blood-based biomarkers (which is less traumatic than the use of cerebrospinal fluid), it is possible to use the data obtained with modern neuroimaging methods (diffuse-tenser tractography and voxel-based morphometry). The literature data on epigenetic regulation in the mechanisms of development of psychiatric pathology are presented. The role of metabolomics in the study of mechanisms of development of mental disorders is noted. Modern approaches include mass spectrometry, which can reveal specific changes in the ways of signal transmission and interactions at the protein level. Analyzing the literature data, the authors come to the conclusion that in the near future we can hardly expect the appearance of biomarkers specific for certain disorders. The scope of their use is rather to obtain information about the nature of pathophysiology and help in the choice of therapy, because the specificity and sensitivity of the abovementioned tests are not enough for their independent use for diagnostic purposes. Due to the fact that the majority of mental disorders manifest as a result of the interaction of many genetic and environmental factors, their nature is extremely heterogeneous. One should not expect the creation of simple diagnostic tests. Instead of searching for biomarkers derived from clinical symptoms of disorders, a new alternative biological classification based on molecular markers should be considered.
Translated title of the contributionPRESENT AND FUTURE OF BIOMARKERS IN DIAGNOSTICS OF ENDOGENOUS NEUROPSYCHIATRIC DISORDERS
Original languageRussian
Pages (from-to)289-296
JournalПСИХИАТРИЯ, ПСИХОТЕРАПИЯ И КЛИНИЧЕСКАЯ ПСИХОЛОГИЯ
Volume11
Issue number2
StatePublished - 2020

    Research areas

  • BIOMARKERS, DIAGNOSTICS, PSYCHOSES, SCHIZOPHRENIA, BIPOLAR AFFECTIVE DISORDER, METABOLOMICS, Neural networks

ID: 70636835