Efficacy and Safety of Antidepressants in Patients With Comorbid Depression and Medical Diseases: An Umbrella Systematic Review and Meta-Analysis.

Importance: Every third to sixth patient with medical diseases receives antidepressants, but regulatory trials typically exclude comorbid medical diseases. Meta-analyses of antidepressants have shown small to medium effect sizes, but generalizability to clinical settings is unclear, where medical comorbidity is highly prevalent. Objective: To perform an umbrella systematic review of the meta-analytic evidence and meta-analysis of the efficacy and safety of antidepressant use in populations with medical diseases and comorbid depression. Data Sources: PubMed and EMBASE were searched from inception until March 31, 2023, for systematic reviews with or without meta-analyses of randomized clinical trials (RCTs) examining the efficacy and safety of antidepressants for treatment or prevention of comorbid depression in any medical disease. Study Selection: Meta-analyses of placebo- or active-controlled RCTs studying antidepressants for depression in individuals with medical diseases. Data Extraction and Synthesis: Data extraction and quality assessment using A Measurement Tool for the Assessment of Multiple Systematic Reviews (AMSTAR-2 and AMSTAR-Content) were performed by pairs of independent reviewers following PRISMA guidelines. When several meta-analyses studied the same medical disease, the largest meta-analysis was included. Random-effects meta-analyses pooled data on the primary outcome (efficacy), key secondary outcomes (acceptability and tolerability), and additional secondary outcomes (response and remission). Main Outcomes and Measures: Antidepressant efficacy presented as standardized mean differences (SMDs) and tolerability (discontinuation for adverse effects) and acceptability (all-cause discontinuation) presented as risk ratios (RRs). Results: Of 6587 references, 176 systematic reviews were identified in 43 medical diseases. Altogether, 52 meta-analyses in 27 medical diseases were included in the evidence synthesis (mean [SD] AMSTAR-2 quality score, 9.3 [3.1], with a maximum possible of 16; mean [SD] AMSTAR-Content score, 2.4 [1.9], with a maximum possible of 9). Across medical diseases (23 meta-analyses), antidepressants improved depression vs placebo (SMD, 0.42 [95% CI, 0.30-0.54]; I2 = 76.5%), with the largest SMDs for myocardial infarction (SMD, 1.38 [95% CI, 0.82-1.93]), functional chest pain (SMD, 0.87 [95% CI, 0.08-1.67]), and coronary artery disease (SMD, 0.83 [95% CI, 0.32-1.33]) and the smallest for low back pain (SMD, 0.06 [95% CI, 0.17-0.39]) and traumatic brain injury (SMD, 0.08 [95% CI, -0.28 to 0.45]). Antidepressants showed worse acceptability (24 meta-analyses; RR, 1.17 [95% CI, 1.02-1.32]) and tolerability (18 meta-analyses; RR, 1.39 [95% CI, 1.13-1.64]) compared with placebo. Antidepressants led to higher rates of response (8 meta-analyses; RR, 1.54 [95% CI, 1.14-1.94]) and remission (6 meta-analyses; RR, 1.43 [95% CI, 1.25-1.61]) than placebo. Antidepressants more likely prevented depression than placebo (7 meta-analyses; RR, 0.43 [95% CI, 0.33-0.53]). Conclusions and Relevance: The results of this umbrella systematic review of meta-analyses found that antidepressants are effective and safe in treating and preventing depression in patients with comorbid medical disease. However, few large, high-quality RCTs exist in most medical diseases.

Monitoring of COVID-19 pandemic-related psychopathology using machine learning.

The COVID-19 pandemic is believed to have a major negative impact on global mental health due to the viral disease itself as well as the associated lockdowns, social distancing, isolation, fear, and increased uncertainty. Individuals with preexisting mental illness are likely to be particularly vulnerable to these conditions and may develop outright 'COVID-19-related psychopathology'. Here, we trained a machine learning model on structured and natural text data from electronic health records to identify COVID-19 pandemic-related psychopathology among patients receiving care in the Psychiatric Services of the Central Denmark Region. Subsequently, applying this model, we found that pandemic-related psychopathology covaries with the pandemic pressure over time. These findings may aid psychiatric services in their planning during the ongoing and future pandemics. Furthermore, the results are a testament to the potential of applying machine learning to data from electronic health records.

Investigating the association between body fat and depression via Mendelian randomization.

Obesity and depression are major public health concerns that are both associated with substantial morbidity and mortality. There is a considerable body of literature linking obesity to the development of depression. Recent studies using Mendelian randomization indicate that this relationship is causal. Most studies of the obesity-depression association have used body mass index as a measure of obesity. Body mass index is defined as weight (measured in kilograms) divided by the square of height (meters) and therefore does not distinguish between the contributions of fat and nonfat to body weight. To better understand the obesity-depression association, we conduct a Mendelian randomization study of the relationship between fat mass, nonfat mass, height, and depression, using genome-wide association study results from the UK Biobank (n = 332,000) and the Psychiatric Genomics Consortium (n = 480,000). Our findings suggest that both fat mass and height (short stature) are causal risk factors for depression, while nonfat mass is not. These results represent important new knowledge on the role of anthropometric measures in the etiology of depression. They also suggest that reducing fat mass will decrease the risk of depression, which lends further support to public health measures aimed at reducing the obesity epidemic.