International Journal of Molecular Medical Science, 2024, Vol.14, No.4, 216-226 http://medscipublisher.com/index.php/ijmms 216 Review Article Open Access Genomic Studies and Personalized Treatment of Depression Tiantian Li, Jie Zhang Institute of Life Science, Jiyang College of Zhejiang A&F University, Zhuji, 311800, Zhejiang, China Corresponding author: jie.zhang@jicat.org International Journal of Molecular Medical Science, 2024, Vol.14, No.4 doi: 10.5376/ijmms.2024.14.0024 Received: 03 Jun., 2024 Accepted: 07 Jul., 2024 Published: 19 Jul., 2024 Copyright © 2024 Li and Zhang, This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Preferred citation for this article: Li T.T., and Zhang J., 2024, Genomic studies and personalized treatment of depression, International Journal of Molecular Medical Science, 14(4): 216-226 (doi: 10.5376/ijmms.2024.14.0024) Abstract The advent of genomic studies has significantly advanced our understanding of depression and its treatment, paving the way for personalized medicine. This review explores the integration of pharmacogenomics and other omic technologies in the treatment of depression, highlighting the potential for individualized care. Pharmacogenomic studies have identified several single nucleotide polymorphisms (SNPs) that influence the efficacy of antidepressants and mood stabilizers, although their clinical application remains limited. Genome-wide association studies have further elucidated genetic predictors of treatment-resistant depression, suggesting potential biomarkers for personalized treatment. Despite these advancements, the translation of genetic findings into clinical practice has been slow, with current diagnostic and treatment strategies still largely symptom-based. However, the use of pharmacogenomic testing has shown promise in improving treatment outcomes by guiding medication selection. Future research should focus on integrating multi-omic data to develop comprehensive predictive models for treatment response, ultimately enhancing the precision of depression management. Keywords Pharmacogenomics; Personalized medicine; Depression; Genomic studies; Treatment-Resistant depression 1 Introduction Depression, clinically known as Major Depressive Disorder (MDD), is a pervasive mood disorder characterized by persistent feelings of sadness, hopelessness, and a lack of interest or pleasure in daily activities. It is a significant public health concern, affecting individuals across all age groups, including adults, teenagers, and children (Sadeeqa, 2018). The World Health Organization estimates that over 300 million people globally suffer from depression, making it the leading cause of disability worldwide (Neto et al., 2019). The impact of depression extends beyond the individual, affecting families, workplaces, and society at large. It is associated with considerable morbidity, mortality, and economic costs, including heightened risks of suicide (Hyde et al., 2016; Wray et al., 2017). Traditional treatment approaches for depression, which typically include pharmacotherapy and psychotherapy, often yield limited success due to the heterogeneous nature of the disorder (Greden et al., 2019). The variability in treatment response necessitates a more personalized approach to effectively manage and treat depression. Personalized treatment aims to tailor therapeutic strategies to the individual characteristics of each patient, thereby improving treatment outcomes and reducing the trial-and-error process associated with conventional methods (Sekaran and Shanmugam, 2021). This approach is particularly crucial given the complex interplay of genetic, environmental, and psychosocial factors that contribute to the onset and progression of depression (Belmaker and Agam, 2008). Genomic studies have significantly advanced our understanding of the biological underpinnings of depression. Genome-wide association studies (GWAS) have identified numerous genetic variants associated with depression, highlighting the polygenic nature of the disorder (Howard et al., 2019). For instance, a meta-analysis involving over 800 000 individuals identified 102 independent variants and 269 genes associated with depression, underscoring the importance of genetic factors in its etiology (Howard et al., 2019). These findings pave the way for the development of pharmacogenomic-guided treatments, which use genetic information to predict individual responses to medications and tailor treatment plans accordingly (Greden et al.,
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