Major depressive disorder (MDD) is the leading cause of disability globally, with moderate heritability and well-established socio-environmental risk factors. Genetic studies have been mostly restricted to European settings, with polygenic scores (PGS) demonstrating low portability across diverse global populations.

This study examines genetic architecture, polygenic prediction, and socio-environmental correlates of MDD in a family-based sample of 10 032 individuals from Nepal with array genotyping data. We used genome-based restricted maximum likelihood to estimate heritability, applied S-LDXR to estimate the cross-ancestry genetic correlation between Nepalese and European samples, and modeled PGS trained on a GWAS meta-analysis of European and East Asian ancestry samples.

We estimated the narrow-sense heritability of lifetime MDD in Nepal to be 0.26 (95% CI 0.18–0.34, p = 8.5 × 10−6). Our analysis was underpowered to estimate the cross-ancestry genetic correlation (rg = 0.26, 95% CI −0.29 to 0.81). MDD risk was associated with higher age (beta = 0.071, 95% CI 0.06–0.08), female sex (beta = 0.160, 95% CI 0.15–0.17), and childhood exposure to potentially traumatic events (beta = 0.050, 95% CI 0.03–0.07), while neither the depression PGS (beta = 0.004, 95% CI −0.004 to 0.01) or its interaction with childhood trauma (beta = 0.007, 95% CI −0.01 to 0.03) were strongly associated with MDD.

Estimates of lifetime MDD heritability in this Nepalese sample were similar to previous European ancestry samples, but PGS trained on European data did not predict MDD in this sample. This may be due to differences in ancestry-linked causal variants, differences in depression phenotyping between the training and target data, or setting-specific environmental factors that modulate genetic effects. Additional research among under-represented global populations will ensure equitable translation of genomic findings.

Depression, particularly in early adulthood, presents a significant mental health challenge with far-reaching implications. Innovative approaches to address and alleviate depressive symptoms are of paramount importance in this context. One such approach involves the utilization of technology, specifically chatbot-based programs, to target specific cognitive biases associated with depression.

The central objective is to empirically examine whether this program can effectively influence depressive mood and negative cognition in individuals grappling with depressive symptoms.

To ascertain the program’s efficacy, participants were divided into two groups: the CBM-I group (n=20), which underwent interpretation bias modification training, and the Mood Check group(n=20), which served as a control and engaged in a simple mood-checking exercise. A battery of psychological measures was employed, including assessments of depression, interpretation bias, suicidal ideation, resilience, and attention control.

Analysis results showed that the CBM-I group had a significant reduction in depression (PHQ-9, CES-D) compared to the Mood Check group in the post-measurement. Moreover, resilience (CD-RISC) and attention control (ACQ) significantly improved in the CBM-I group.

This research serves as a stepping stone towards a deeper understanding of how chatbot-based interventions can contribute to the management of early adulthood depression, offering new perspectives and possibilities in the realm of mental health support and treatment.

None Declared

Majority of international guidelines for bipolar disorders are based on evidences from clinical trials. In contrast, the Korean Medication Algorithm Project for Bipolar Disorder (KMAP-BP) was developed to adopt an expert-consensus paradigm which was more practical and specific to the atmosphere in Korea.

In this study, preferred medication strategies for acute mania over six consecutively published KMAP-BP (2002, 2006, 2010, 2014, 2018, and 2022) were investigated.

A written survey using a nine-point scale was asked to Korean experts about the appropriateness of various treatment strategies and treatment agents. A written survey asked about the appropriateness of various treatment strategies and treatment agents commonly used by clinicians as the first-line.

The most preferred option for the initial treatment of mania was a combination of a mood stabilizer (MS) and an atypical antipsychotic (AAP) in every edition. Preference for combined treatment for euphoric mania increased, peaked in KMAP-BP 2010, and declined slightly. Either MS or AAP monotherapy was also considered a first-line strategy for mania, but not for all types of episodes, including mixed/psychotic mania. Among MSs, lithium and valproate are almost equally preferred except in the mixed subtype where valproate is the most recommended MS. The preference of valproate showed reverse U-shaped curve. This preference change of valproate may indicate the concern about teratotoxicity in women. Quetiapine, aripiprazole, and olanzapine were the preferred AAP for acute mania since 2014. This change might depend on the recent evidences and safety profile. In cases of unsatisfactory response to initial medications, switching or adding another first-line agent was recommended. The most notable changes over time included the increasing preference for AAPs.

The Korean experts have been increasingly convinced of the effectiveness of a combination therapy for acute mania. There have been evident preference changes: increased for AAP and decreased for carbamazepine.

None Declared

Major Depressive Disorder (MDD) stands as a prevalent psychiatric condition within the general population. Despite extensive research efforts, the identification of definitive diagnostic biomarkers for depressive disorders remains elusive. Currently, machine learning methods are gaining prominence in the diagnosis of medical illnesses.

This study aims to construct a machine learning-based prediction model for Major Depressive Disorder (MDD) by harnessing diffusion tensor imaging (DTI) data.

The DTI datasets comprising MDD (N=83) and Healthy Control (N=70) groups were procured from the cohort study of Anxiety and Depression conducted at the National Center for Mental Health in South Korea. A machine learning method using a decision tree algorithm was employed to select relevant brain regions and establish a robust diagnostic model. Features associated with white matter (WM) tracts were chosen through recursive feature elimination.

Demographic characteristics, including age, sex, and handedness, displayed no significant differences between the MDD and Healthy Control groups. However, the total score of the Beck Depression Inventory was notably higher in individuals with MDD compared to Healthy Controls. A diagnostic model was crafted using the decision tree algorithms to distinguish between the two groups. The model demonstrated the following classification performance metrics: accuracy (65.6% ± 8.5), sensitivity (66.6% ± 12.5), and specificity (64.7% ± 13.6). Furthermore, through recursive feature elimination, specific neuroanatomical features tied to brain structures such as the inferior cerebellar peduncle, posterior thalamic radiation, cingulum (hippocampus), uncinate fasciculus, and tapetum were identified.

Despite of limited performance of classification, a machine learning-based approach could provide insights into the development of a diagnostic model for MDD using neuroimaging data. Furthermore, these features, derived from DTI-derived data, may have implications for understanding the neural underpinnings of major depressive disorder.

None Declared

Understanding characteristics of healthcare personnel (HCP) with SARS-CoV-2 infection supports the development and prioritization of interventions to protect this important workforce. We report detailed characteristics of HCP who tested positive for SARS-CoV-2 from April 20, 2020 through December 31, 2021.

CDC collaborated with Emerging Infections Program sites in 10 states to interview HCP with SARS-CoV-2 infection (case-HCP) about their demographics, underlying medical conditions, healthcare roles, exposures, personal protective equipment (PPE) use, and COVID-19 vaccination status. We grouped case-HCP by healthcare role. To describe residential social vulnerability, we merged geocoded HCP residential addresses with CDC/ATSDR Social Vulnerability Index (SVI) values at the census tract level. We defined highest and lowest SVI quartiles as high and low social vulnerability, respectively.

Our analysis included 7,531 case-HCP. Most case-HCP with roles as certified nursing assistant (CNA) (444, 61.3%), medical assistant (252, 65.3%), or home healthcare worker (HHW) (225, 59.5%) reported their race and ethnicity as either non-Hispanic Black or Hispanic. More than one third of HHWs (166, 45.2%), CNAs (283, 41.7%), and medical assistants (138, 37.9%) reported a residential address in the high social vulnerability category. The proportion of case-HCP who reported using recommended PPE at all times when caring for patients with COVID-19 was lowest among HHWs compared with other roles.

To mitigate SARS-CoV-2 infection risk in healthcare settings, infection prevention, and control interventions should be specific to HCP roles and educational backgrounds. Additional interventions are needed to address high social vulnerability among HHWs, CNAs, and medical assistants.

Individuals who have recovered from the acute stage of SARS-CoV-2 infection may be at risk of developing post-COVID-19 condition (PCC), characterised by a spectrum of persisting, non-specific, and functionally impairing symptoms across multiple organ systems. Obesity has been implicated as a risk factor for PCC, mediated by chronic systemic inflammation. The foregoing has also been separately reported to mediate cognitive dysfunction in PCC.

This is a post-hoc analysis of a randomised, double-blinded, placebo-controlled clinical trial evaluating vortioxetine treatment for cognitive impairments in persons with PCC who received vortioxetine or placebo for eight weeks. This analysis comprises baseline data, examining the impact of BMI on cognitive functioning measured by the Digit Symbol Substitution Test (DSST) and Trails Making Tests (TMT)-A/B, as well as inflammation, via serum c-reactive protein (CRP) and erythrocyte sedimentation rate (ESR).

Complete data from 70 participants were statistically analysed and adjusted for age and sex. BMI was negatively correlated with performance on the DSST (β = −0.003, p = 0.047), TMT-A (β = −0.006, p = 0.025), and TMT-B (β = −0.006, p = 0.002). BMI was positively correlated with serum CRP (unstandardized β = 0.193, standardized β = 0.612, p < 0.001) and ESR (β = 0.039, p < 0.001) levels.

We observed a significant negative correlation between BMI and cognitive functioning, and a significant positive correlation between BMI and inflammation in persons with PCC, suggesting a bidirectional interplay between BMI, PCC, and cognitive function; individuals with an elevated BMI may be at a greater risk of developing PCC and/or presenting with greater cognitive deficits mediated by chronic systemic inflammation.