Associations Between COVID-19, Delirium, and 1-Year Mortality: Exploring Influences on Delirium Incidence in COVID-19 Patients.

BACKGROUND: This study investigated the relationship between coronavirus disease 2019 (COVID-19), delirium, and 1-year mortality. Factors associated with delirium in COVID-19 patients were identified, along with the influence of psychotropic medications on delirium. METHODS: The study used the South Korean National Health Insurance Service database. Adult COVID-19 patients diagnosed between October 2020 and December 2021 were included, with a propensity score-matched control group. Time-dependent Cox regression assessed associations among COVID-19, delirium, and mortality. Logistic regression analyzed the impact of psychotropic medications on delirium incidence. RESULTS: The study included 832,602 individuals, with 416,301 COVID-19 patients. COVID-19 (hazard ratio [HR], 3.03; 95% confidence interval [CI], 2.92-3.13) and delirium (HR, 2.33; 95% CI, 2.06-2.63) were independent risk factors for 1-year mortality. Comorbidities, insurance type, and residence were also related to mortality. Among COVID-19 patients, antipsychotic use was associated with lower delirium incidence (odds ratio [OR], 0.38; 95% CI, 0.30-0.47), while mood stabilizers (OR, 1.77; 95% CI, 1.40-2.21) and benzodiazepines (OR, 8.62; 95% CI, 7.46-9.97) were linked to higher delirium incidence. CONCLUSION: COVID-19 and delirium are risk factors for 1-year mortality. Some factors associated with delirium in COVID-19 patients are modifiable and can be targeted in preventive and therapeutic interventions.

The Impact of Morningness-Eveningness on Depression through a Serial Mediation Model of Resilience and Anxiety.

OBJECTIVE: Resilience has been recently considered one of the possible mechanisms for the association between morningness-eveningness and depression. Meanwhile, anxiety is closely associated with mood disorder, but its association with morningness-eveningness is unclear. Therefore, this study aimed to explore the mediating effects of resilience and anxiety on morningness-eveningness and depression as the possible mechanisms. METHODS: This study included patient group and nonpatient group. Patient group consists of 743 patients with mood disorders [Major Depressive Disorder (MDD), 233; Bipolar Disorder Ⅰ (BDⅠ), 113; Bipolar Disorder Ⅱ (BDⅡ), 397] whereas nonpatient group consists of 818 individuals without mood disorder. The Composite Scale of Morningness, Connor-Davidson Resilience Scale, Self-Rating Depression Scale, and Beck Anxiety Inventory were used to evaluate morningness-eveningness, resilience, anxiety, and depression, respectively. RESULTS: Our model provided a good fit for the data. The association between morningness-eveningness and depression symptoms was partially serially mediated by resilience and anxiety in both the patient and nonpatient groups. The patient group exhibited significantly stronger morningness-eveningness toward resilience and anxiety than the nonpatient group. In the indirect effect of morningness-eveningness on depression, group differences exist only through each mediation of resilience and anxiety, not through serial mediation. CONCLUSION: Our results expand on the mechanism underlying the association between morningness-eveningness and depression. They highlight the importance of morningness-eveningness modification to increase resilience and the need to consider anxiety jointly in this process.

Network Structure of Depressive Symptomatology in Elderly with Cognitive Impairment.

Objective and objectives: Patients with cognitive disorders such as Alzheimer's disease (AD) and mild cognitive impairment (MCI) frequently exhibit depressive symptoms. Depressive symptoms can be evaluated with various measures and questionnaires. The geriatric depression scale (GDS) is a scale that can be used to measure symptoms in geriatric age. Many questionnaires sum up symptom scales. However, core symptoms of depression in these patients and connections between these symptoms have not been fully explored yet. Thus, the objectives of this study were (1) to determine core symptoms of two cognitive disorders, Alzheimer's disease and mild cognitive impairment, and (2) to investigate the network structure of depressive symptomatology in individuals with cognitive impairment in comparison with those with Alzheimer's disease. Materials and Methods: This study encompassed 5354 patients with cognitive impairments such as Alzheimer's disease (n 1889) and mild cognitive impairment (n = 3464). The geriatric depression scale, a self-administered questionnaire, was employed to assess depressive symptomatology. Using exploratory graph analysis (EGA), a network analysis was conducted, and the network structure was evaluated through regularized partial correlation models. To determine the centrality of depressive symptoms within each cohort, network parameters such as strength, betweenness, and closeness were examined. Additionally, to explore differences in the network structure between Alzheimer's disease and mild cognitive impairment groups, a network comparison test was performed. Results: In the analysis of centrality indices, "worthlessness" was identified as the most central symptom in the geriatric depression scale among patients with Alzheimer's disease, whereas "emptiness" was found to be the most central symptom in patients with mild cognitive impairment. Despite these differences in central symptoms, the comparative analysis showed no statistical difference in the overall network structure between Alzheimer's disease and mild cognitive impairment groups. Conclusions: Findings of this study could contribute to a better understanding of the manifestation of depressive symptoms in patients with cognitive impairment. These results are expected to aid in identifying and prioritizing core symptoms in these patients. Further research should be conducted to explore potential interventions tailored to these core symptoms in patients with Alzheimer's disease and mild cognitive impairment. Establishing core symptoms in those groups might have clinical importance in that appropriate treatment for neuropsychiatric symptoms in patients with cognitive impairment could help preclude progression to further impairment.

Understanding of Depressive Symptomatology across Major Depressive Disorder and Bipolar Disorder: A Network Analysis.

Background and Objectives: Depressive symptoms are prominent in both major depressive disorder (MDD) and bipolar disorder (BD). However, comparative research on the network structure of depressive symptoms in these two diagnostic groups has been limited. This study aims to compare the network structure of depressive symptoms in MDD and BD, providing a deeper understanding of the depressive symptomatology of each disorder. Materials and Methods: The Zung Self-Rating Depressive Scale, a 20-item questionnaire, was administered to assess the depressive symptoms in individuals with MDD (n = 322) and BD (n = 516). A network analysis was conducted using exploratory graph analysis (EGA), and the network structure was analyzed using regularized partial correlation models. To validate the dimensionality of the Zung SDS, principal component analysis (PCA) was adopted. Centrality measures of the depressive symptoms within each group were assessed, followed by a network comparison test between the two groups. Results: In both diagnostic groups, the network analysis revealed four distinct categories, aligning closely with the PCA results. "Depressed affect" emerged as the most central symptom in both MDD and BD. Furthermore, non-core symptoms, "Personal devaluation" in MDD and "Confusion" in BD, displayed strong centrality. The network comparison test did not reveal significant differences in the network structure between MDD and BD. Conclusions: The absence of significant differences in the network structures between MDD and BD suggests that the underlying mechanisms of depressive symptoms may be similar across these disorders. The identified central symptoms, including "Depressed affect", in both disorders and the distinct non-core symptoms in each highlight the complexity of the depressive symptomatology. Future research should focus on validating these symptoms as therapeutic targets and incorporate various methodologies, including non-metric dimension reduction techniques or canonical analysis.

Distinguishing Affective Temperament Profiles in Major Depressive Disorder and Bipolar Disorder Through the Short Version of TEMPS-A: Cross-Sectional Study Using Latent Profile Analysis.

OBJECTIVE: This study aimed to elucidate the distinct response patterns exhibited by patients diagnosed with bipolar disorder (BD) and those with major depressive disorder (MDD) through the application of the short version of the Temperament Evaluation of Memphis, Pisa, Paris, and San Diego Autoquestionnaire (TEMPS-A-SV). METHODS: A total of 2,458 participants consisting of patients with MDD (n=288), BD (BD I, n=111; BD II, n=427), and control group (n=1,632) completed the TEMPS-A-SV. The response patterns of the participants were classified into distinct profiles using latent profile analysis. The study further examined the impact of covariates such as age, sex, and diagnostic group on derived latent profile memberships. RESULTS: The following three latent profiles were identified: High Affective Temperament Group (17.86%), Low Affective Temperament Group (41.25%), and Middle Affective Temperament Group (40.89%). Compared with the patient group with MDD and BD, the control group was more likely to belong in the Low Affective Temperament Group, which showed a higher score on hyperthymic temperament than the Middle Affective Temperament Group. Furthermore, compared with the patients with BD, the MDD patients were more likely to be in the Low Affective Temperament Group rather than the Middle Affective Temperament Group. CONCLUSION: These results indicate that different affective temperaments exist between patients with MDD and BD. Attempting to classify response patterns using the TEMPS-A-SV can help diagnose MDD and BD correctly.

Short-term exposure to ambient air pollution and injuries due to external causes according to intentions and mechanisms.

Although injuries are a leading cause of death and affect the life expectancy of individuals who live with disabilities globally, the potential role of air pollution exposure on injuries due to external causes has received little scientific attention, especially compared with that given to the association of air pollution and non-external causes of morbidity and mortality. We investigated the association between emergency department visits for externally caused injuries and short-term exposure to major ambient air pollutants, with focus on the intentions and mechanisms of injuries. We identified 2,049,855 injured patients in Seoul, South Korea between 2008 and 2016 using the National Emergency Database. Daily short-term exposure to air pollution including particles <10 µm (PM10) and <2.5 µm (PM2.5), nitrogen dioxide (NO2), sulfur dioxide (SO2), carbon monoxide (CO), and ozone (O3) was estimated based on hourly concentrations. We employed a time-stratified case-crossover study design using a conditional Poisson regression model adjusted for meteorological variables, influenza epidemics, and holidays. Immediate exposure (lag 0) to most pollutants significantly increased the risk of total injuries (PM2.5, 0.42 %; NO2, 0.68 %; SO2, 1.05 %; CO, 0.57 %; O3, 1.86 % per interquartile range increment), and the associations differed according to the intention and mechanism of injury. Unintentional and assault injuries were significantly associated with air pollution exposure, whereas self-harm injuries showed no association. In mechanism-specific analyses, injuries caused by falls, blunt objects, penetration, traffic accidents, machinery, and slips were associated with specific air pollutants, even in the co-pollutant models. The associations were stronger in injured patients aged <15 years, and in males than in their counterparts. Our results suggest that short-term air pollution exposure might play a role in the risk of externally caused injuries and the association may differ depending on the intention and mechanism of injury, which provide important evidence for injury prevention and air quality strategies.

Integrated proteomic and genomic analysis to identify predictive biomarkers for valproate response in bipolar disorder: a 6-month follow-up study.

BACKGROUND: Several genetic studies have been undertaken to elucidate the intricate interplay between genetics and drug responses in bipolar disorder (BD). However, there has been notably limited research on biomarkers specifically linked to valproate, with only a few studies investigating integrated proteomic and genomic factors in response to valproate treatment. Therefore, this study aimed to identify biological markers for the therapeutic response to valproate treatment in BD. Patients with BD in remission were assessed only at baseline, whereas those experiencing acute mood episodes were evaluated at three points (baseline, 8 ± 2 weeks, and 6 ± 1 months). The response to valproate treatment was measured using the Alda scale, with individuals scoring an Alda A score ≥ 5 categorized into the acute-valproate responder (acute-VPAR) group. We analyzed 158 peptides (92 proteins) from peripheral blood samples using multiple reaction monitoring mass spectrometry, and proteomic result-guided candidate gene association analyses, with 1,627 single nucleotide variants (SNVs), were performed using the Korean chip. RESULTS: The markers of 37 peptides (27 protein) showed temporal upregulation, indicating possible association with response to valproate treatment. A total of 58 SNVs in 22 genes and 37 SNVs in 16 genes showed nominally significant associations with the Alda A continuous score and the acute-VPAR group, respectively. No SNVs reached the genome-wide significance threshold; however, three SNVs (rs115788299, rs11563197, and rs117669164) in the secreted phosphoprotein 2 gene reached a gene-based false discovery rate-corrected significance threshold with response to valproate treatment. Significant markers were associated with the pathophysiological processes of bipolar disorders, including the immune response, acute phase reaction, and coagulation cascade. These results suggest that valproate effectively suppresses mechanisms associated with disease progression. CONCLUSIONS: The markers identified in this study could be valuable indicators of the underlying mechanisms associated with response to valproate treatment.

Large-scale cross-ancestry genome-wide meta-analysis of serum urate.

Hyperuricemia is an essential causal risk factor for gout and is associated with cardiometabolic diseases. Given the limited contribution of East Asian ancestry to genome-wide association studies of serum urate, the genetic architecture of serum urate requires exploration. A large-scale cross-ancestry genome-wide association meta-analysis of 1,029,323 individuals and ancestry-specific meta-analysis identifies a total of 351 loci, including 17 previously unreported loci. The genetic architecture of serum urate control is similar between European and East Asian populations. A transcriptome-wide association study, enrichment analysis, and colocalization analysis in relevant tissues identify candidate serum urate-associated genes, including CTBP1, SKIV2L, and WWP2. A phenome-wide association study using polygenic risk scores identifies serum urate-correlated diseases including heart failure and hypertension. Mendelian randomization and mediation analyses show that serum urate-associated genes might have a causal relationship with serum urate-correlated diseases via mediation effects. This study elucidates our understanding of the genetic architecture of serum urate control.

Shared genetic architectures of educational attainment in East Asian and European populations.

Educational attainment (EduYears), a heritable trait often used as a proxy for cognitive ability, is associated with various health and social outcomes. Previous genome-wide association studies (GWASs) on EduYears have been focused on samples of European (EUR) genetic ancestries. Here we present the first large-scale GWAS of EduYears in people of East Asian (EAS) ancestry (n = 176,400) and conduct a cross-ancestry meta-analysis with EduYears GWAS in people of EUR ancestry (n = 766,345). EduYears showed a high genetic correlation and power-adjusted transferability ratio between EAS and EUR. We also found similar functional enrichment, gene expression enrichment and cross-trait genetic correlations between two populations. Cross-ancestry fine-mapping identified refined credible sets with a higher posterior inclusion probability than single population fine-mapping. Polygenic prediction analysis in four independent EAS and EUR cohorts demonstrated transferability between populations. Our study supports the need for further research on diverse ancestries to increase our understanding of the genetic basis of educational attainment.

Genome-wide association analyses using machine learning-based phenotyping reveal genetic architecture of occupational creativity and overlap with psychiatric disorders.

Creativity is known to be heritable and exhibits familial aggregation with psychiatric disorders; however, the complex nature of their relationship has not been well-established. In the present study, we demonstrate that using an expanded and validated machine learning (ML)-based phenotyping of occupational creativity (OC) can allow us to further understand the trait of creativity, which was previously difficult to define and study. We conducted the largest genome-wide association study (GWAS) on OC with 241,736 participants from the UK Biobank and identified 25 lead variants that have not yet been reported and three candidate causal genes that were previously associated with educational attainment and psychiatric disorders. We found extensive genetic overlap between OC and psychiatric disorders with mixed effect direction through various post-GWAS analyses, including the bivariate causal mixture model. In addition, we discovered a strongly genetic correlation between our original GWAS and the GWAS adjusted for education years (rg = 0.95). Our GWAS analysis via ML-based phenotyping contributes to the understanding of the genetic architecture of creativity, which may inform genetic discovery and genetic prediction in human cognition and psychiatric disorders.

Defining Suicidal Thought and Behavior Phenotypes for Genetic Studies.

Background: Standardized definitions of suicidality phenotypes, including suicidal ideation (SI), attempt (SA), and death (SD) are a critical step towards improving understanding and comparison of results in suicide research. The complexity of suicidality contributes to heterogeneity in phenotype definitions, impeding evaluation of clinical and genetic risk factors across studies and efforts to combine samples within consortia. Here, we present expert and data-supported recommendations for defining suicidality and control phenotypes to facilitate merging current/legacy samples with definition variability and aid future sample creation. Methods: A subgroup of clinician researchers and experts from the Suicide Workgroup of the Psychiatric Genomics Consortium (PGC) reviewed existing PGC definitions for SI, SA, SD, and control groups and generated preliminary consensus guidelines for instrument-derived and international classification of disease (ICD) data. ICD lists were validated in two independent datasets (N = 9,151 and 12,394). Results: Recommendations are provided for evaluated instruments for SA and SI, emphasizing selection of lifetime measures phenotype-specific wording. Recommendations are also provided for defining SI and SD from ICD data. As the SA ICD definition is complex, SA code list recommendations were validated against instrument results with sensitivity (range = 15.4% to 80.6%), specificity (range = 67.6% to 97.4%), and positive predictive values (range = 0.59-0.93) reported. Conclusions: Best-practice guidelines are presented for the use of existing information to define SI/SA/SD in consortia research. These proposed definitions are expected to facilitate more homogeneous data aggregation for genetic and multisite studies. Future research should involve refinement, improved generalizability, and validation in diverse populations.