Depression is associated with metabolic alterations including lipid dysregulation, whereby associations may vary across individual symptoms. Evaluating these associations using a network perspective yields a more complete insight than single outcome-single predictor models.

We used data from the Netherlands Study of Depression and Anxiety (N = 2498) and leveraged networks capturing associations between 30 depressive symptoms (Inventory of Depressive Symptomatology) and 46 metabolites. Analyses involved 4 steps: creating a network with Mixed Graphical Models; calculating centrality measures; bootstrapping for stability testing; validating central, stable associations by extra covariate-adjustment; and validation using another data wave collected 6 years later.

The network yielded 28 symptom-metabolite associations. There were 15 highly-central variables (8 symptoms, 7 metabolites), and 3 stable links involving the symptoms Low energy (fatigue), and Hypersomnia. Specifically, fatigue showed consistent associations with higher mean diameter for VLDL particles and lower estimated degree of (fatty acid) unsaturation. These remained present after adjustment for lifestyle and health-related factors and using another data wave.

The somatic symptoms Fatigue and Hypersomnia and cholesterol and fatty acid measures showed central, stable, and consistent relationships in our network. The present analyses showed how metabolic alterations are more consistently linked to specific symptom profiles.

Mental health was only modestly affected in adults during the early months of the COVID-19 pandemic on the group level, but interpersonal variation was large.

We aim to investigate potential predictors of the differences in changes in mental health.

Data were aggregated from three Dutch ongoing prospective cohorts with similar methodology for data collection. We included participants with pre-pandemic data gathered during 2006–2016, and who completed online questionnaires at least once during lockdown in The Netherlands between 1 April and 15 May 2020. Sociodemographic, clinical (number of mental health disorders and personality factors) and COVID-19-related variables were analysed as predictors of relative changes in four mental health outcomes (depressive symptoms, anxiety and worry symptoms, and loneliness), using multivariate linear regression analyses.

We included 1517 participants with (n = 1181) and without (n = 336) mental health disorders. Mean age was 56.1 years (s.d. 13.2), and 64.3% were women. Higher neuroticism predicted increases in all four mental health outcomes, especially for worry (β = 0.172, P = 0.003). Living alone and female gender predicted increases in depressive symptoms and loneliness (β = 0.05–0.08), whereas quarantine and strict adherence with COVID-19 restrictions predicted increases in anxiety and worry symptoms (β = 0.07–0.11).Teleworking predicted a decrease in anxiety symptoms (β = −0.07) and higher age predicted a decrease in anxiety (β = −0.08) and worry symptoms (β = −0.10).

Our study showed neuroticism as a robust predictor of adverse changes in mental health, and identified additional sociodemographic and COVID-19-related predictors that explain longitudinal variability in mental health during the COVID-19 pandemic.

First responders to disasters are at risk of developing post-traumatic stress disorder (PTSD). The trajectories of post-traumatic stress symptom severity differ among individuals, even if they are exposed to similar events. These trajectories have not yet been reported in non-Western first responders.

We aimed to explore post-traumatic stress symptom severity trajectories and their risk factors in first responders to the 2011 Great East Japan Earthquake (GEJE) – a historically large earthquake that resulted in a tsunami and a nuclear disaster.

A total of 55 632 Japan Ground Self-Defense Force (JGSDF) personnel dispatched to the GEJE were enrolled in this 7-year longitudinal cohort study. PTSD symptom severity was measured using the Impact of Event Scale-Revised. Trajectories were identified using latent growth mixture models (LGMM). Nine potential risk factors for the symptom severity trajectories were analysed using multinomial logistic regression.

Five symptom severity trajectories were identified: ‘resilient’ (54.8%), ‘recovery’ (24.6%), ‘incomplete recovery’ (10.7%), ‘late-onset’ (5.7%), and ‘chronic’ (4.3%). The main risk factors for the four non-resilient trajectories were older age, personal disaster experiences and working conditions. These working conditions included duties involving body recovery or radiation exposure risk, longer deployment length, later or no post-deployment leave and longer post-deployment overtime.

The majority of first responders to GEJE were resilient and developed few or no PTSD symptoms. A substantial minority experienced late-onset and chronic symptom severity trajectories. The identified risk factors can inform policies for prevention, early detection and intervention in individuals at risk of developing symptomatic trajectories.

Aggression and violent incidents are a major concern in psychiatric in-patient care. Nutritional supplementation has been found to reduce aggressive incidents and rule violations in forensic populations and children with behavioural problems.

To assess whether multivitamin, mineral and n-3 polyunsaturated fatty acid supplementation would reduce the number of aggressive incidents among long-stay psychiatric in-patients.

The trial was a pragmatic, multicentre, randomised, double-blind placebo-controlled study. Data were collected from 25 July 2016 to 29 October 2019, at eight local sites for mental healthcare in The Netherlands and Belgium. Participants were randomised (1:1) to receive 6-month treatment with either three supplements containing multivitamins, minerals and n-3 polyunsaturated fatty acid, or placebo. The primary outcome was the number of aggressive incidents, determined by the Staff Observation Aggression Scale – Revised (SOAS-R). Secondary outcomes were patient quality of life, affective symptoms and adverse events.

In total, 176 participants were randomised (supplements, n = 87; placebo, n = 89). Participants were on average 49.3 years old (s.d. 14.5) and 64.2% were male. Most patients had a psychotic disorder (60.8%). The primary outcome of SOAS-R incidents was similar in supplement (1.03 incidents per month, 95% CI 0.74–1.37) and placebo groups (0.90 incidents per month, 95% CI 0.65–1.19), with a rate ratio of 1.08 (95% CI 0.67–1.74, P = 0.75). Differential effects were not found in sensitivity analyses on the SOAS-R or on secondary outcomes.

Six months of nutritional supplementation did not reduce aggressive incidents among long-stay psychiatric in-patients.

Observational studies suggest that hormonal contraceptive use may increase depressive symptoms in women, but it is unclear whether the effect is causal.

To quantitatively examine the evidence from randomised clinical trials for the link between hormonal contraceptive use and depressive symptoms.

We performed a systematic review and network meta-analysis of randomised clinical trials comparing women randomised to any form of a hormonal contraceptive with women randomised to any other form of a (non-)hormonal contraceptive or placebo. We searched the Cochrane Central Register of Controlled Trials (CENTRAL), PubMed, Web of Science, PsycINFO, EMCare and EMBASE, from inception to 1 May 2020. Certainty of the evidence was assessed with the Grading of Recommendations Assessment, Development and Evaluation approach. A random-effect Bayesian network meta-analysis was conducted, with change in depressive symptoms between baseline and three cycles as outcome.

This review identified 3492 records, of which 14 trials were eligible and 12 could be included in the network meta-analysis. These trials included 5833 participants (mean age per study range: 16.8–32.4 years) and compared 10 different interventions. Compared with placebo, hormonal contraceptive use did not cause worsening of depressive symptoms (standardised mean difference: median, −0.04; range, −0.17 [95% credible interval −0.46 to 0.13] to 0.13 [95% credible interval −0.28 to 0.56]).

This study suggests that hormonal contraceptive use does not lead to an increase in depressive symptoms in adult women. Future studies should include first-time users, to confirm the results in young women.

Considering the heterogeneity of depression, distinct depressive symptom dimensions may be differentially associated with more objective actigraphy-based estimates of physical activity (PA), sleep and circadian rhythm (CR). We examined the association between PA, sleep, and CR assessed with actigraphy and symptom dimensions (i.e. mood/cognition, somatic/vegetative, sleep).

Fourteen-day actigraphy data of 359 participants were obtained from the Netherlands Study of Depression and Anxiety. PA, sleep, and CR estimates included gross motor activity (GMA), sleep duration (SD), sleep efficiency (SE), relative amplitude between daytime and night-time activity (RA) and sleep midpoint. The 30-item Inventory of Depressive Symptomatology was used to assess depressive symptoms, which were categorised in three depression dimensions: mood/cognition, somatic/vegetative, and sleep.

GMA and RA were negatively associated with higher score on all three symptom dimensions: mood/cognition (GMA: β = −0.155, p < 0.001; RA: β = −0.116, p = 0.002), somatic/vegetative (GMA: β = −0.165, p < 0.001; RA: β = −0.133, p < 0.001), sleep (GMA: β = −0.169, p < 0.001; RA: β = −0.190, p < 0.001). The association with sleep was more pronounced for two depression dimensions: longer SD was linked to somatic/vegetative (β = 0.115, p = 0.015) dimension and lower SE was linked to sleep (β = −0.101, p = 0.011) dimension.

As three symptom dimensions were associated with actigraphy-based low PA and dampened CR, these seem to be general indicators of depression. Sleep disturbances appeared more linked to the somatic/vegetative and sleep dimensions; the effectiveness of sleep interventions in patients reporting somatic/vegetative symptoms may be explored, as well as the potential of actigraphy to monitor treatment response to such interventions.

Disease trajectories of patients with anxiety disorders are highly diverse and approximately 60% remain chronically ill. The ability to predict disease course in individual patients would enable personalized management of these patients. This study aimed to predict recovery from anxiety disorders within 2 years applying a machine learning approach.

In total, 887 patients with anxiety disorders (panic disorder, generalized anxiety disorder, agoraphobia, or social phobia) were selected from a naturalistic cohort study. A wide array of baseline predictors (N = 569) from five domains (clinical, psychological, sociodemographic, biological, lifestyle) were used to predict recovery from anxiety disorders and recovery from all common mental disorders (CMDs: anxiety disorders, major depressive disorder, dysthymia, or alcohol dependency) at 2-year follow-up using random forest classifiers (RFCs).

At follow-up, 484 patients (54.6%) had recovered from anxiety disorders. RFCs achieved a cross-validated area-under-the-receiving-operator-characteristic-curve (AUC) of 0.67 when using the combination of all predictor domains (sensitivity: 62.0%, specificity 62.8%) for predicting recovery from anxiety disorders. Classification of recovery from CMDs yielded an AUC of 0.70 (sensitivity: 64.6%, specificity: 62.3%) when using all domains. In both cases, the clinical domain alone provided comparable performances. Feature analysis showed that prediction of recovery from anxiety disorders was primarily driven by anxiety features, whereas recovery from CMDs was primarily driven by depression features.

The current study showed moderate performance in predicting recovery from anxiety disorders over a 2-year follow-up for individual patients and indicates that anxiety features are most indicative for anxiety improvement and depression features for improvement in general.