Loneliness has become a major public health issue of the recent decades due to its severe impact on health and mortality. Little is known about the relation between loneliness and social anxiety. This study aimed (1) to explore levels of loneliness and social anxiety in the general population, and (2) to assess whether and how loneliness affects symptoms of social anxiety and vice versa over a period of five years.

The study combined data from the baseline assessment and the five-year follow-up of the population-based Gutenberg Health Study. Data of N = 15 010 participants at baseline (Mage = 55.01, s.d.age = 11.10) were analyzed. Multiple regression analyses with loneliness and symptoms of social anxiety at follow-up including sociodemographic, physical illnesses, and mental health indicators at baseline were used to test relevant covariates. Effects of loneliness on symptoms of social anxiety over five years and vice versa were analyzed by autoregressive cross-lagged structural equation models.

At baseline, 1076 participants (7.41%) showed symptoms of social anxiety and 1537 (10.48%) participants reported feelings of loneliness. Controlling for relevant covariates, symptoms of social anxiety had a small significant effect on loneliness five years later (standardized estimate of 0.164, p < 0.001). Vice versa, there was no significant effect of loneliness on symptoms of social anxiety taking relevant covariates into account.

Findings provided evidence that symptoms of social anxiety are predictive for loneliness. Thus, prevention and intervention efforts for loneliness need to address symptoms of social anxiety.

In response to the COVID-19 pandemic, we rapidly implemented a plasma coordination center, within two months, to support transfusion for two outpatient randomized controlled trials. The center design was based on an investigational drug services model and a Food and Drug Administration-compliant database to manage blood product inventory and trial safety.

A core investigational team adapted a cloud-based platform to randomize patient assignments and track inventory distribution of control plasma and high-titer COVID-19 convalescent plasma of different blood groups from 29 donor collection centers directly to blood banks serving 26 transfusion sites.

We performed 1,351 transfusions in 16 months. The transparency of the digital inventory at each site was critical to facilitate qualification, randomization, and overnight shipments of blood group-compatible plasma for transfusions into trial participants. While inventory challenges were heightened with COVID-19 convalescent plasma, the cloud-based system, and the flexible approach of the plasma coordination center staff across the blood bank network enabled decentralized procurement and distribution of investigational products to maintain inventory thresholds and overcome local supply chain restraints at the sites.

The rapid creation of a plasma coordination center for outpatient transfusions is infrequent in the academic setting. Distributing more than 3,100 plasma units to blood banks charged with managing investigational inventory across the U.S. in a decentralized manner posed operational and regulatory challenges while providing opportunities for the plasma coordination center to contribute to research of global importance. This program can serve as a template in subsequent public health emergencies.

Pre-diagnostic stages of psychotic illnesses, including ‘clinical high risk’ (CHR), are marked by sleep disturbances. These sleep disturbances appear to represent a key aspect in the etiology and maintenance of psychotic disorders. We aimed to examine the relationship between self-reported sleep dysfunction and attenuated psychotic symptoms (APS) on a day-to-day basis.

Seventy-six CHR young people completed the Experience Sampling Methodology (ESM) component of the European Union Gene-Environment Interaction Study, collected through PsyMate® devices, prompting sleep and symptom questionnaires 10 times daily for 6 days. Bayesian multilevel mixed linear regression analyses were performed on time-variant ESM data using the brms package in R. We investigated the day-to-day associations between sleep and psychotic experiences bidirectionally on an item level. Sleep items included sleep onset latency, fragmentation, and quality. Psychosis items assessed a range of perceptual, cognitive, and bizarre thought content common in the CHR population.

Two of the seven psychosis variables were unidirectionally predicted by previous night's number of awakenings: every unit increase in number of nightly awakenings predicted a 0.27 and 0.28 unit increase in feeling unreal or paranoid the next day, respectively. No other sleep variables credibly predicted next-day psychotic symptoms or vice-versa.

In this study, the relationship between sleep disturbance and APS appears specific to the item in question. However, some APS, including perceptual disturbances, had low levels of endorsement amongst this sample. Nonetheless, these results provide evidence for a unidirectional relationship between sleep and some APS in this population.

We identify a set of essential recent advances in climate change research with high policy relevance, across natural and social sciences: (1) looming inevitability and implications of overshooting the 1.5°C warming limit, (2) urgent need for a rapid and managed fossil fuel phase-out, (3) challenges for scaling carbon dioxide removal, (4) uncertainties regarding the future contribution of natural carbon sinks, (5) intertwinedness of the crises of biodiversity loss and climate change, (6) compound events, (7) mountain glacier loss, (8) human immobility in the face of climate risks, (9) adaptation justice, and (10) just transitions in food systems.

The Intergovernmental Panel on Climate Change Assessment Reports provides the scientific foundation for international climate negotiations and constitutes an unmatched resource for researchers. However, the assessment cycles take multiple years. As a contribution to cross- and interdisciplinary understanding of climate change across diverse research communities, we have streamlined an annual process to identify and synthesize significant research advances. We collected input from experts on various fields using an online questionnaire and prioritized a set of 10 key research insights with high policy relevance. This year, we focus on: (1) the looming overshoot of the 1.5°C warming limit, (2) the urgency of fossil fuel phase-out, (3) challenges to scale-up carbon dioxide removal, (4) uncertainties regarding future natural carbon sinks, (5) the need for joint governance of biodiversity loss and climate change, (6) advances in understanding compound events, (7) accelerated mountain glacier loss, (8) human immobility amidst climate risks, (9) adaptation justice, and (10) just transitions in food systems. We present a succinct account of these insights, reflect on their policy implications, and offer an integrated set of policy-relevant messages. This science synthesis and science communication effort is also the basis for a policy report contributing to elevate climate science every year in time for the United Nations Climate Change Conference.

We highlight recent and policy-relevant advances in climate change research – with input from more than 200 experts.

Schizotypy represents an index of psychosis-proneness in the general population, often associated with childhood trauma exposure. Both schizotypy and childhood trauma are linked to structural brain alterations, and it is possible that trauma exposure moderates the extent of brain morphological differences associated with schizotypy.

We addressed this question using data from a total of 1182 healthy adults (age range: 18–65 years old, 647 females/535 males), pooled from nine sites worldwide, contributing to the Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) Schizotypy working group. All participants completed both the Schizotypal Personality Questionnaire Brief version (SPQ-B), and the Childhood Trauma Questionnaire (CTQ), and underwent a 3D T1-weighted brain MRI scan from which regional indices of subcortical gray matter volume and cortical thickness were determined.

A series of multiple linear regressions revealed that differences in cortical thickness in four regions-of-interest were significantly associated with interactions between schizotypy and trauma; subsequent moderation analyses indicated that increasing levels of schizotypy were associated with thicker left caudal anterior cingulate gyrus, right middle temporal gyrus and insula, and thinner left caudal middle frontal gyrus, in people exposed to higher (but not low or average) levels of childhood trauma. This was found in the context of morphological changes directly associated with increasing levels of schizotypy or increasing levels of childhood trauma exposure.

These results suggest that alterations in brain regions critical for higher cognitive and integrative processes that are associated with schizotypy may be enhanced in individuals exposed to high levels of trauma.

Remitted psychotic depression (MDDPsy) has heterogeneity of outcome. The study's aims were to identify subgroups of persons with remitted MDDPsy with distinct trajectories of depression severity during continuation treatment and to detect predictors of membership to the worsening trajectory.

One hundred and twenty-six persons aged 18–85 years participated in a 36-week randomized placebo-controlled trial (RCT) that examined the clinical effects of continuing olanzapine once an episode of MDDPsy had remitted with sertraline plus olanzapine. Latent class mixed modeling was used to identify subgroups of participants with distinct trajectories of depression severity during the RCT. Machine learning was used to predict membership to the trajectories based on participant pre-trajectory characteristics.

Seventy-one (56.3%) participants belonged to a subgroup with a stable trajectory of depression scores and 55 (43.7%) belonged to a subgroup with a worsening trajectory. A random forest model with high prediction accuracy (AUC of 0.812) found that the strongest predictors of membership to the worsening subgroup were residual depression symptoms at onset of remission, followed by anxiety score at RCT baseline and age of onset of the first lifetime depressive episode. In a logistic regression model that examined depression score at onset of remission as the only predictor variable, the AUC (0.778) was close to that of the machine learning model.

Residual depression at onset of remission has high accuracy in predicting membership to worsening outcome of remitted MDDPsy. Research is needed to determine how best to optimize the outcome of psychotic MDDPsy with residual symptoms.

Schizophrenia is a highly debilitating disorder afflicting more than 24 million individuals worldwide. In Mexico, the Ministry of Health estimates that it affects more than 1 million people. Suicide is one of the main causes of death among people diagnosed with schizophrenia, their risk is 12 times higher than in. the general population

To evaluate the clinical characteristics of schizophrenic patients at risk of suicide in the Latino population.

We included 130 patients recruited from genetics studies in Latino patients with schizophrenia from the outpatient and inpatient psychiatric ward of the University Hospital “Dr José E. González” in Monterrey, Nuevo Leon, Mexico. Beck Depression Inventory (BDI-II), the Convergent Functional Information for Suicidality (CFI-S) were applied to all participants. We compared the sociodemographic and clinical characteristics of patients with suicidal risk (measured by history of suicidal attempt or current suicidal risk) and present depressive episode.

Of the 130 participants, 66.9% were male, the median age was 38 years. We found 11(14.3%) patients with suicidal risk and 119 (91.5%) without suicidal risk. Sociodemographic and clinical characteristics of the study population at risk of suicide are described in graphic 1. Patients with a history of suicide attempt scored higher on the CFI-S scale with a median of 0.5 (q1=0.45; q3=0.54) vs. 0.31 (q1=0.22; q3=0.45) (p= 0.004)(Graphic 2). Based on the BDI-II we found 2.30% patients showed a mild depression, 20.0% moderate depression and 4.61% severe depression (graphic 3). Schizophrenic patients with a previous suicide attempt and depressive episode had higher score range in CFI-S, median .65 (q1=.65; q2=.59, p=0.000). Also, 63.60% were severely depressive (p=0.000) when they compared with patients with low risk of suicide. Schizophrenic patients with suicidal risk were characterized by: age >=60 years old, unemployment, no children, single, without religion, family history of suicide, previous suicide attempt, depressive episodes, substance abuse, auditory hallucinations and referential delusions.

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In our study with Latino population, we observed similar clinical characteristics predictive of suicide risk described in the international studies. Our study is relevant to applied preventive measures in groups of schizophrenic patients with risk factors and their relatives.

None Declared

Response to lithium in patients with bipolar disorder is associated with clinical and transdiagnostic genetic factors. The predictive combination of these variables might help clinicians better predict which patients will respond to lithium treatment.

To use a combination of transdiagnostic genetic and clinical factors to predict lithium response in patients with bipolar disorder.

This study utilised genetic and clinical data (n = 1034) collected as part of the International Consortium on Lithium Genetics (ConLi+Gen) project. Polygenic risk scores (PRS) were computed for schizophrenia and major depressive disorder, and then combined with clinical variables using a cross-validated machine-learning regression approach. Unimodal, multimodal and genetically stratified models were trained and validated using ridge, elastic net and random forest regression on 692 patients with bipolar disorder from ten study sites using leave-site-out cross-validation. All models were then tested on an independent test set of 342 patients. The best performing models were then tested in a classification framework.

The best performing linear model explained 5.1% (P = 0.0001) of variance in lithium response and was composed of clinical variables, PRS variables and interaction terms between them. The best performing non-linear model used only clinical variables and explained 8.1% (P = 0.0001) of variance in lithium response. A priori genomic stratification improved non-linear model performance to 13.7% (P = 0.0001) and improved the binary classification of lithium response. This model stratified patients based on their meta-polygenic loadings for major depressive disorder and schizophrenia and was then trained using clinical data.

Using PRS to first stratify patients genetically and then train machine-learning models with clinical predictors led to large improvements in lithium response prediction. When used with other PRS and biological markers in the future this approach may help inform which patients are most likely to respond to lithium treatment.

Many mental disorders, including depression, bipolar disorder and schizophrenia, are associated with poor dietary quality and nutrient intake. There is, however, a deficit of research looking at the relationship between obsessive–compulsive disorder (OCD) severity, nutrient intake and dietary quality.

This study aims to explore the relationship between OCD severity, nutrient intake and dietary quality.

A post hoc regression analysis was conducted with data combined from two separate clinical trials that included 85 adults with diagnosed OCD, using the Structured Clinical Interview for DSM-5. Nutrient intakes were calculated from the Dietary Questionnaire for Epidemiological Studies version 3.2, and dietary quality was scored with the Healthy Eating Index for Australian Adults – 2013.

Nutrient intake in the sample largely aligned with Australian dietary guidelines. Linear regression models adjusted for gender, age and total energy intake showed no significant associations between OCD severity, nutrient intake and dietary quality (all P > 0.05). However, OCD severity was inversely associated with caffeine (β = −15.50, 95% CI −28.88 to −2.11, P = 0.024) and magnesium (β = −6.63, 95% CI −12.72 to −0.53, P = 0.034) intake after adjusting for OCD treatment resistance.

This study showed OCD severity had little effect on nutrient intake and dietary quality. Dietary quality scores were higher than prior studies with healthy samples, but limitations must be noted regarding comparability. Future studies employing larger sample sizes, control groups and more accurate dietary intake measures will further elucidate the relationship between nutrient intake and dietary quality in patients with OCD.