Can Molecular Biology Propose Reliable Biomarkers for Diagnosing Major Depression?

Author(s): Nikolay N. Ivanets, Andrey A. Svistunov, Vladimir N. Chubarev*, Marina A. Kinkulkina, Yuliya G. Tikhonova, Nikita S. Syzrantsev, Susanna S. Sologova, Nelly V. Ignatyeva, Kerim Mutig, Vadim V. Tarasov

Journal Name: Current Pharmaceutical Design

Volume 27 , Issue 2 , 2021

Become EABM
Become Reviewer


Background: Modern medicine has provided considerable knowledge of the pathophysiology of mental disorders at the body, systemic, organ and neurochemical levels of the biological organization of the body. Modern clinical diagnostics of depression have some problems, that is why psychiatric society makes use of diagnostics and taxonomy of different types of depression by implemention of modern molecular biomarkers in diagnostic procedures. But up to now, there are no reliable biomarkers of major depressive disorder (MDD) and other types of depression.

Objective: The purpose of this review is to find fundamentals in pathological mechanisms of depression, which could be a basis for development of molecular and genetic biomarkers, being the most feasible for clinical use.

Method: This review summarizes the published data using PubMed, Science Direct, Google Scholar and Scopus.

Results: In this review, we summarized and discussed findings in molecular biology, genetics, neuroplasticity, neurotransmitters, and neuroimaging that could increase our understanding of the biological foundations of depression and show new directions for the development of reliable biomarkers. We did not find any molecular and genetic biomarker approved for the clinic. But the Genome-Wide Association Study method promises some progress in the development of biomarkers based on SNP in the future. Epigenetic factors also are a promising target for biomarkers. We have found some differences in the etiology of different types of atypical and melancholic depression. This knowledge could be the basis for development of biomarkers for clinical practice in diagnosis, prognosis and selection of treatment.

Conclusion: Depression is not a monoetiological disease. Many pathological mechanisms are involved in depression, thus up to now, there is no approved and reliable biomarker for diagnosis, prognosis and correction of treatment of depression. The structural and functional complexity of the brain, the lack of invasive technology, poor correlations between genetic and clinical manifestation of depression, imperfect psychiatric classification and taxonomy of subtypes of disease are the main causes of this situation. One of the possible ways to come over this situation can be to pay attention to the trigger mechanism of disease and its subtypes. Researchers and clinicians should focus their efforts on searching the trigger mechanism of depression and different types of it . HPA axis can be a candidate for such trigger in depression caused by stress, because it influences the main branches of disease: neuroinflammation, activity of biogenic amines, oxidative and nitrosative stress, epigenetic factors, metabolomics, etc. But before we shall find any trigger mechanism, we need to create complex biomarkers reflecting genetic, epigenetic, metabolomics and other pathological changes in different types of depression. Recently the most encouraging results have been obtained from genetics and neuroimaging. Continuing research in these areas should be forced by using computational, statistical and systems biology approaches, which can allow to obtain more knowledge about the neurobiology of depression. In order to obtain clinically useful tests, search for biomarkers should use appropriate research methodologies with increasing samples and identifying more homogeneous groups of depressed patients.

Keywords: Depression, biomarkers, stress, neuroinflammation, neuroplasticity, neuroimaging, genetics, pharmacology.

Rights & PermissionsPrintExport Cite as

Article Details

Year: 2021
Published on: 04 February, 2021
Page: [305 - 318]
Pages: 14
DOI: 10.2174/1381612826666201124110437
Price: $65

Article Metrics

PDF: 24