Putative biological predictors of treatment response in bipolar disorders

Suzanne Gonzalez, Aislinn Williams, Caren J. Blacker, Jennifer L. Vande Voort, Kathryn M. Schak, Charles B. Nemeroff, Alik S. Widge, Mauricio Tohen

Research output: Contribution to journalArticle

Abstract

Bipolar disorder (BD) is a debilitating illness that affects millions of Americans each year and is the 6th leading cause of disability in the world. Although standard treatments are available for management of BD, approximately half of all BD patients are either non-adherent or poorly adherent with prescribed medication regimens, resulting in decreased quality of life and increases in relapse rates, costs of care, and suicide attempts. Noncompliance in BD is often related to medication side effects and perceived lack of efficacy, which underscores the importance of trying to improve the “trial and error” process of finding optimal individualized treatments. There is a great need for more specific and sensitive biomarkers for the monitoring of BD treatment response, as well as predictive biomarkers to identify who is most likely to respond to these treatments and to avoid adverse effects. Here, we provide a comprehensive review on the utility of peripheral biomarkers for treatment response in bipolar disorder. We focus on the five most promising key areas for biological predictors of treatment response: 1) cell growth, cell survival, and synaptic plasticity (neurotrophins and growth factors), 2) energy metabolism (oxidative stress and mitochondrial function), 3) inflammation (pro- and anti-inflammatory cytokines), 4) stress response (neuroendocrine response), and 5) peripheral gene expression.
Original languageEnglish (US)
Pages (from-to)39-58
JournalPersonalized Medicine in Psychiatry
StatePublished - Mar 2017

    Fingerprint

Cite this

Gonzalez, S., Williams, A., Blacker, C. J., Vande Voort, J. L., Schak, K. M., Nemeroff, C. B., Widge, A. S., & Tohen, M. (2017). Putative biological predictors of treatment response in bipolar disorders. Personalized Medicine in Psychiatry, 39-58. https://www.sciencedirect.com/science/article/pii/S2468171716300138