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The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, with its impact on our way of life, is affecting our experiences and mental health. Notably, individuals with mental disorders have been reported to have a higher risk of contracting SARS-CoV-2. Personality traits could represent an important determinant of preventative health behaviour and, therefore, the risk of contracting the virus.
We examined overlapping genetic underpinnings between major psychiatric disorders, personality traits and susceptibility to SARS-CoV-2 infection.
Linkage disequilibrium score regression was used to explore the genetic correlations of coronavirus disease 2019 (COVID-19) susceptibility with psychiatric disorders and personality traits based on data from the largest available respective genome-wide association studies (GWAS). In two cohorts (the PsyCourse (n = 1346) and the HeiDE (n = 3266) study), polygenic risk scores were used to analyse if a genetic association between, psychiatric disorders, personality traits and COVID-19 susceptibility exists in individual-level data.
We observed no significant genetic correlations of COVID-19 susceptibility with psychiatric disorders. For personality traits, there was a significant genetic correlation for COVID-19 susceptibility with extraversion (P = 1.47 × 10−5; genetic correlation 0.284). Yet, this was not reflected in individual-level data from the PsyCourse and HeiDE studies.
We identified no significant correlation between genetic risk factors for severe psychiatric disorders and genetic risk for COVID-19 susceptibility. Among the personality traits, extraversion showed evidence for a positive genetic association with COVID-19 susceptibility, in one but not in another setting. Overall, these findings highlight a complex contribution of genetic and non-genetic components in the interaction between COVID-19 susceptibility and personality traits or mental disorders.
Depression and coronary heart disease (CHD) are highly comorbid conditions. Brain-derived neurotrophic factor (BDNF) plays an important role in cardiovascular processes. Depressed patients typically show decreased BDNF concentrations. We analysed the relationship between BDNF and depression in a sample of patients with CHD and additionally distinguished between cognitive-affective and somatic depression symptoms. We also investigated whether BDNF was associated with somatic comorbidity burden, acute coronary syndrome (ACS) or congestive heart failure (CHF).
The following variables were assessed for 225 hospitalised patients with CHD: BDNF concentrations, depression [Patient Health Questionnaire-9 (PHQ-9)], somatic comorbidity (Charlson Comorbidity Index), CHF, ACS, platelet count, smoking status and antidepressant treatment.
Regression models revealed that BDNF was not associated with severity of depression. Although depressed patients (PHQ-9 score >7) had significantly lower BDNF concentrations compared to non-depressed patients (p = 0.04), this was not statistically significant after controlling for confounders (p = 0.15). Cognitive-affective symptoms and somatic comorbidity burden each closely missed a statistically significant association with BDNF concentrations (p = 0.08, p = 0.06, respectively). BDNF was reduced in patients with CHF (p = 0.02). There was no covariate-adjusted, significant association between BDNF and ACS.
Serum BDNF concentrations are associated with cardiovascular dysfunction. Somatic comorbidities should be considered when investigating the relationship between depression and BDNF.
Deficits in the Span of Apprehension (SOA) task have been discussed controversially as a trait marker of schizophrenia. The task was administered to 47 schizophrenia and 48 depressed patients as well as to 46 controls. Results indicate a non-specificity of the SOA sum scores but differential abnormalities in spatial visual processing.
Electroconvulsive therapy (ECT) is a fast-acting intervention for major depressive disorder. Previous studies indicated neurotrophic effects following ECT that might contribute to changes in white matter brain structure. We investigated the influence of ECT in a non-randomized prospective study focusing on white matter changes over time.
Twenty-nine severely depressed patients receiving ECT in addition to inpatient treatment, 69 severely depressed patients with inpatient treatment (NON-ECT) and 52 healthy controls (HC) took part in a non-randomized prospective study. Participants were scanned twice, approximately 6 weeks apart, using diffusion tensor imaging, applying tract-based spatial statistics. Additional correlational analyses were conducted in the ECT subsample to investigate the effects of seizure duration and therapeutic response.
Mean diffusivity (MD) increased after ECT in the right hemisphere, which was an ECT-group-specific effect. Seizure duration was associated with decreased fractional anisotropy (FA) following ECT. Longitudinal changes in ECT were not associated with therapy response. However, within the ECT group only, baseline FA was positively and MD negatively associated with post-ECT symptomatology.
Our data suggest that ECT changes white matter integrity, possibly reflecting increased permeability of the blood–brain barrier, resulting in disturbed communication of fibers. Further, baseline diffusion metrics were associated with therapy response. Coherent fiber structure could be a prerequisite for a generalized seizure and inhibitory brain signaling necessary to successfully inhibit increased seizure activity.
Patients with major depression show reduced hippocampal volume compared to healthy controls. However, the contribution of patients’ cumulative illness severity to hippocampal volume has rarely been investigated. It was the aim of our study to find a composite score of cumulative illness severity that is associated with hippocampal volume in depression.
We estimated hippocampal gray matter volume using 3-tesla brain magnetic resonance imaging in 213 inpatients with acute major depression according to DSM-IV criteria (employing the SCID interview) and 213 healthy controls. Patients’ cumulative illness severity was ascertained by six clinical variables via structured clinical interviews. A principal component analysis was conducted to identify components reflecting cumulative illness severity. Regression analyses and a voxel-based morphometry approach were used to investigate the influence of patients’ individual component scores on hippocampal volume.
Principal component analysis yielded two main components of cumulative illness severity: Hospitalization and Duration of Illness. While the component Hospitalization incorporated information from the intensity of inpatient treatment, the component Duration of Illness was based on the duration and frequency of illness episodes. We could demonstrate a significant inverse association of patients’ Hospitalization component scores with bilateral hippocampal gray matter volume. This relationship was not found for Duration of Illness component scores.
Variables associated with patients’ history of psychiatric hospitalization seem to be accurate predictors of hippocampal volume in major depression and reliable estimators of patients’ cumulative illness severity. Future studies should pay attention to these measures when investigating hippocampal volume changes in major depression.
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