Cardiometabolic and endocrine comorbidities in women with bipolar disorder: A systematic review.

INTRODUCTION: Bipolar Disorder (BD) is known to be equally distributed among males and females. The well-documented increased risk of medical comorbidities in patients with BD, in comparison to BD patients without medical comorbidities, shows a negative impact on the course of illness. There is some evidence suggesting that women with BD have higher psychiatric and medical comorbidities in comparison to men with BD, however there is no evidence in comparison to women without BD or other major psychiatric illness. These comorbidities, along with various psychosocial factors, are known to affect the course of BD. METHODS: We aimed to systematically review the literature on cardiovascular, metabolic and endocrine comorbidities in women with BD in comparison to men with BD and control women. A comprehensive search of electronic databases including PubMed, PsycINFO, Embase, and SCOPUS was conducted, and a total of 61 identified studies were included in this review. RESULTS: Women with BD had higher rates of cardiovascular risk factors/mortality, diabetes mellitus II and thyroid disorders compared to women in the general population. In comparison to men with BD, women with BD had comparable cardiovascular risk but higher prevalence of metabolic and thyroid disorders. LIMITATIONS: Gender specific data was limited in multiple studies. CONCLUSIONS: Results present a need for gender-specific screening and interventions for various medical comorbidities in patients with BD.

The Relationship between DNA Methylation and Antidepressant Medications: A Systematic Review.

Major depressive disorder (MDD) is the leading cause of disability worldwide and is associated with high rates of suicide and medical comorbidities. Current antidepressant medications are suboptimal, as most MDD patients fail to achieve complete remission from symptoms. At present, clinicians are unable to predict which antidepressant is most effective for a particular patient, exposing patients to multiple medication trials and side effects. Since MDD's etiology includes interactions between genes and environment, the epigenome is of interest for predictive utility and treatment monitoring. Epigenetic mechanisms of antidepressant medications are incompletely understood. Differences in epigenetic profiles may impact treatment response. A systematic literature search yielded 24 studies reporting the interaction between antidepressants and eight genes (BDNF, MAOA, SLC6A2, SLC6A4, HTR1A, HTR1B, IL6, IL11) and whole genome methylation. Methylation of certain sites within BDNF, SLC6A4, HTR1A, HTR1B, IL11, and the whole genome was predictive of antidepressant response. Comparing DNA methylation in patients during depressive episodes, during treatment, in remission, and after antidepressant cessation would help clarify the influence of antidepressant medications on DNA methylation. Individuals' unique methylation profiles may be used clinically for personalization of antidepressant choice in the future.

Corticosterone oscillations during mania induction in the lateral hypothalamic kindled rat-Experimental observations and mathematical modeling.

Changes in the hypothalamic-pituitary-adrenal (HPA) axis activity constitute a key component of bipolar mania, but the extent and nature of these alterations are not fully understood. We use here the lateral hypothalamic-kindled (LHK) rat model to deliberately induce an acute manic-like episode and measure serum corticosterone concentrations to assess changes in HPA axis activity. A mathematical model is developed to succinctly describe the entwined biochemical transformations that underlay the HPA axis and emulate by numerical simulations the considerable increase in serum corticosterone concentration induced by LHK. Synergistic combination of the LHK rat model and dynamical systems theory allows us to quantitatively characterize changes in HPA axis activity under controlled induction of acute manic-like states and provides a framework to study in silico how the dynamic integration of neurochemical transformations underlying the HPA axis is disrupted in these states.