Understanding who responds well to treatment for depression is important both scientifically (to help develop better treatments) and clinically (to more efficiently prescribe effective treatments to individuals). Many attempts to predict treatment outcomes have focused on mechanistic pathways (e.g., genetic and brain imaging measures). However, these may not be particularly useful clinically, where such measures are typically not available to clinicians making treatment decisions. A better alternative might be to use routinely- or readily-collected behavioural and self-report data, such as demographic variables and symptom scores.
Chekroud and colleagues (2015) report the results of a machine learning approach to predicting treatment outcome in depression, using clinical (rather than mechanistic) predictors.
Women have twice the risk of developing major depression compared to men. This difference is most noticeable during the reproductive period years (Soares et al, 2008) (e.g. premenstrual, during pregnancy and postpartum, and perimenopause) when women are subject to large fluctuations of ovarian hormones.
Additionally, oestrogens are believed to utilise neuroprotective and antidepressive actions within the the brain (Arevalo et al, 2015), and transitioning to the postmenopausal period is associated with a large drop in oestrogen production (Burger al al., 2007).
Therefore, the authors, Georgakis et al (2016), are using ‘age at menopause’ and ‘duration of reproductive age’ as two markers of lifelong oestrogen exposure to measure the association with risk of depression in postmenopausal women.
Combining antidepressants (ADs) for therapy of acute depression is frequently employed, but randomized studies have yielded conflicting results. We conducted a systematic review and meta-analysis aimed at determining efficacy and tolerability of combination therapy. Login using your SSSFT NHS OpenAthens for full text. SSOTP - request a copy of the article from the library - www.sssft.nhs.uk/library
Cognitive therapies could be an effective alternative to medication in the treatment of depression, according to NIHR research featured in a new Highlight from the NIHR Dissemination Centre.
The Highlight brings together four studies funded by the NIHR that shed light on when, how and for whom, cognitive therapies might be effective. These are accompanied by blogs and interviews with charities, clinicians, researchers and patients.
Depression in older adults is often under recognised despite it being the most common mental health illness in this age group. An increasing older adult population highlights the need for improved diagnostic rates. Brief versions (15 items or less) of the Geriatric Depression Scale (GDS), which are suitable for busy clinical practice, could improve detection rates. Login using your SSSFT NHS OpenAthens for full text. SSOTP - request a copy of the article from the library - www.sssft.nhs.uk/library
Adolescence is a period of increased risk for mental health problems and development of associated lifestyle risk behaviours. This study examined cross-sectional and longitudinal associations between obesogenic risk factors, weight status, and depressive symptomatology in a cohort of Australian adolescents. Open Access Article
Antidepressant treatment failure is a common problem worldwide. In this study, we assess whether or not an important aspect of depression, cognitive impairment, is untreated by antidepressants by studying the effect of acute antidepressant treatment on a range of cognitive domains. Please contact the library to receive a copy of this article - http://bit.ly/1Xyazai
Cognitive–behavioural therapy (CBT) is effective for treating anxiety disorders and is offered in most mental health services around the world. However, a relatively large number of patients with anxiety disorders do not benefit from CBT, experience relapses or drop out. Reliable predictors of treatment effects are lacking. The aim of this study is to investigate the predictive value of emotion regulation and attentional control for CBT outcome in a routine setting. To read the full article, log in using your NHS OpenAthens details
The relative contribution of demographic, lifestyle and medication factors to the association between affective disorders and cardiometabolic diseases is poorly understood. Please contact the library to receive a copy of this article - http://bit.ly/1Xyazai
Numerous studies describe the occurrence of post-traumatic stress disorder following disasters, but less is known about the risk of major depression. Please contact the library to receive a copy of this article - http://bit.ly/1Xyazai
Common mental disorders (CMD) such as anxiety and depression during the maternal period can cause significant morbidity to the mother in addition to disrupting biological, attachment and parenting processes that affect child development. Pharmacological treatment is a first-line option for moderate to severe episodes. Many women prescribed pharmacological treatments cease them during pregnancy but it is unclear to what extent non-pharmacological options are offered as replacement. There are also concerns that treatments offered may not be proportionate to need in minority ethnic groups, but few data exist on treatment disparities in the maternal period. We examined these questions in a multi-ethnic cohort of women with CMD living in Bradford, England before, during and up to one year after pregnancy.
Editorial. Login at top right hand side of page using your SSSFT NHS Athens for full text. SSOTP - request a copy of the article from the library http://bit.ly/1Xyazai
Due to an editorial error, the legend for figure 2 in this research paper (BMJ 2015;351:h6127, doi:10.1136/bmj.h6127) is incorrect. The control group is represented by the red dotted line and the intervention group corresponds to the solid blue line in the figure, not the other way round as the legend describes. To read the full article, log in using your NHS OpenAthens details
Although there have been tremendous advances in the understanding of human dysfunctions in the brain circuitry for self-reflection, emotion, and cognitive control, a brain-based taxonomy for mental disease is still lacking. As a result, these advances have not been translated into actionable clinical tools, and the language of brain circuits has not been incorporated into training programmes. To address this gap, I present this synthesis of published work, with a focus on functional imaging of circuit dysfunctions across the spectrum of mood and anxiety disorders. Please contact the library to receive a copy of this article - http://bit.ly/1Xyazai