Weighing the risks: new review ranks antidepressants by their physical health side effects

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Antidepressants can help millions of people recover from depression and anxiety, but how do they affect physical health? Out today, a review in The Lancet compared 30 antidepressants to see which ones are most (and least) likely to increase our risk of heart disease, stroke, diabetes etc.

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Antidepressants and suicide – it’s complicated…

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Rina Dutta and Alina Cuhraja summarise a network meta-analysis on antidepressants, suicidal ideation and suicidal behaviours, which suggests that SSRIs may reduce suicidal ideation in the short-term, but this is not sustained even to week 8 of treatment.

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Antidepressants, cognition, and emotional blunting: what’s the evidence?

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Angharad de Cates reviews a recent Danish trial, which finds that escitalopram reduced participants’ reinforcement sensitivity compared to those on placebo. This lower reinforcement sensitivity may be similar to the emotional blunting effect often reported by patients during SSRI treatment.

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Turn on, or tune out? Is psilocybin assisted therapy close to becoming a first-line treatment for depression?

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James Rucker and Sameer Jauhar summarise a recent RCT on the effectiveness of psilocybin assisted therapy versus escitalopram assisted therapy for major depressive disorder.

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In adults with major depression, antidepressants may increase the risk of suicide

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Douglas Badenoch explores a meta-analysis of follow-up data from clinical trials of antidepressants, which found a small but significant increase in suicide risk.

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Medication for generalised anxiety disorder: new network meta-analysis

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A group of UCL Masters Students summarise a recent network meta-analysis of drug treatment for generalised anxiety disorder, which finds that Venlafaxine, Pregabalin, Escitalopram and Duloxetine are all viable alternatives to Benzodiazepines.

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Can a machine learning approach help us predict what specific treatments work best for individuals with depression?

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Marcus Munafo explores a recent study that uses a machine learning approach across two trials (STARD*D and CO-MED) to try and predict treatment outcomes (primarily focusing on the antidepressant citalopram) for depression.

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