Affective dysfunction mediates the link between neuroimmune markers and the default mode network functional connectivity, and the somatic symptoms in somatic symptom disorder.

OBJECTIVE: Somatic symptom disorder (SSD) is characterized by physical symptoms and associated functional impairments that are often comorbid with depression and anxiety disorders. In this study, we explored relationships between affective symptoms and the functional connectivity of the default mode network (DMN) in SSD patients, as well as the impact of peripheral inflammation. We employed mediation analyses to investigate the potential pathways between these factors. METHODS: We recruited a total of 119 individuals (74 unmedicated SSD patients and 45 healthy controls), who were subjected to comprehensive psychiatric and clinical evaluations, blood tests, and resting-state functional magnetic resonance imaging scanning. We assessed neuroimmune markers (interleukin-6 (IL-6), high-sensitivity C-reactive protein (hs-CRP), tumor necrosis factor-α (TNF-α), tryptophan, serotonin, and 5-hydroxyindoleacetic acid (5-HIAA)), clinical indicators of somatic symptoms, depression, anxiety, anger, alexithymia, and functional connectivity (FC) within the DMN regions. Data were analyzed using correlation and mediation analysis, with a focus on exploring potential relations between clinical symptoms, blood indices, and DMN FCs. RESULTS: Patients with SSD had higher clinical scores as well as IL-6 and TNF-α levels compared with those in the control group (P < 0.05). The SSD group exhibited lower FC strength between the left inferior parietal lobule and left prefrontal cortex (Pfalse discovery rate (FDR) < 0.05). Exploratory correlation analysis revealed that somatic symptom scores were positively correlated with affective symptom scores, negatively correlated with the FC strength between the intra prefrontal cortex regions, and correlated with levels of IL-6, TNF- α, and tryptophan (uncorrected P < 0.01). Mediation analysis showed that levels of anxiety and trait anger significantly mediated the relations between DMN FC strength and somatic symptoms. In addition, the DMN FC mediated the level of trait anger with respect to somatic symptoms (all PFDR < 0.05). The levels of depression and trait anger exhibited significant mediating effects as suppressors of the relations between the level of 5-HIAA and somatic symptom score (all PFDR < 0.05). Further, the level of 5-HIAA had a mediating effect as a suppressor on the relation between DMN FC and state anger. Meanwhile, the levels of hs-CRP and IL-6 had full mediating effects as suppressors when explaining the relations of DMN FC strengths with the level of depression (all PFDR < 0.05). The patterns of valid mediation pathways were different in the control group. CONCLUSIONS: Affective symptoms may indirectly mediate the associations between DMN connectivity, somatic symptoms, and neuroimmune markers. Inflammatory markers may also mediate the impact of DMN connectivity on affective symptoms. These results emphasize the importance of affective dysregulation in understanding the mechanisms of SSD and have potential implications for the development of tailored therapeutic approaches for SSD patients with affective symptoms. Furthermore, in SSD research using DMN FC or neuroimmune markers, considering and incorporating such mediating effects of affective symptoms suggests the possibility of more accurate prediction and explanation.

Psychosocial Factors Associated with dizziness and chronic dizziness: a nationwide cross-sectional study.

BACKGROUND: Dizziness is a common symptom in adults, and chronic dizziness, such as persistent postural-perceptual dizziness, is also frequently reported and affects the quality of life of patients. This study aimed to identify psychosocial factors related to dizziness and chronic dizziness in a large-scale nationwide cohort. METHODS: This population-based cross-sectional study used the database of the Eighth Korea National Health and Nutrition Examination Survey in 2020. Data from 4,147 adults over 40 years old were analyzed, and 1,102 adults who experienced dizziness were included in the dizziness cohort. Demographic data, medical conditions, comorbidities, functional status variables, nutritional variables and psychological variables were collected. The pattern of depressive symptoms according to the severity of dizziness was analyzed by network analysis. RESULTS: The prevalence rate of dizziness was 24.6% in the general population, and chronic dizziness (≥ 3 months) developed in 210 of 1,102 (17.1%) individuals who experienced dizziness. Multiple logistic regression analysis revealed that female sex, stress, and depression were associated with dizziness. Chronic dizziness was related to tympanic abnormalities, diabetes, short sleep duration, and higher levels of stress and depression. Psychomotor retardation/agitation was a central symptom of depression in patients with chronic dizziness. CONCLUSIONS: This study found sex differences in factors associated with dizziness and identified psychosocial factors linked to chronic dizziness. Focusing on somatic factors rather than depressive symptoms may benefit patients with chronic dizziness.

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.

Exploring the experiences of dancers who have achieved peak performance: on-stage, pre-stage, and post-stage.

The aim of this study is to identify and classify the different attributes that contribute to peak performance among professional dancers, and to understand how these attributes change over time. We conducted an analysis using inductive content analysis on open-ended survey data collected from 42 formally trained professional dancers. Additionally, we analyzed interview data from seven professional dancers who demonstrated outstanding achievements in the field among the survey participants. The main themes that emerged were related to various temporal events of peak performance experience: pre-stage, on-stage, and post-stage. During the on-stage, peak performance was perceived by both internal and external factors. During the pre-stage, emphasis was placed on technical, cognitive, and artistic strategies during practice, whereas just before going on the stage, attention shifted to psychological and physical strategies. During the post-stage, dancers reported immediate changes in their psychological and physical states following the peak performance experience, and thereafter, the peak performance experience was noted to influence psychological, technical, and cognitive aspects. These findings provide valuable insights into the key characteristics that emerge throughout a series of peak performance experiences and are consistent with previous research.

Efficacy of vestibular rehabilitation and its facilitating and hindering factors from real-world clinical data.

Background and purpose: Customized vestibular rehabilitation improved dizziness and imbalance in several randomized controlled trials. In the present study, we determined the efficacy of customized vestibular rehabilitation using real-world observational data. Methods: In this retrospective observational study, we recruited 64 patients (median age = 60, interquartile range = 48-66.3) who completed the customized vestibular rehabilitation from January to December 2022. The outcomes of rehabilitation were evaluated using the dizziness handicap inventory (DHI) or vestibular disorders activities of daily living scale (VADL). The factors associated with outcomes were assessed with a generalized linear model, of which covariates included patients' age, sex, duration of illness, type of vestibular disorders, initial DHI and VADL scores, exercise compliance, and initial hospital anxiety and depression scale (HADS) scores. Results: After the median of 6 (4-6) weeks of rehabilitation, DHI and VADL scores significantly improved in patients with either peripheral or central vestibular disorders (Wilcoxon signed-rank test, p < 0.05). The initial DHI and VADL scores showed a positive while the sum of HADS scores showed a negative correlation with the outcome. In contrast, the age, sex, duration of illness, types of vestibular disorders, and exercise compliance did not affect the outcome. Discussion and conclusion: Customized vestibular rehabilitation is effective for central as well as peripheral disorders, especially when the symptoms are severe and the psychological distress is mild.

Predicting delirium and the effects of medications in hospitalized COVID-19 patients using machine learning: A retrospective study within the Korean Multidisciplinary Cohort for Delirium Prevention (KoMCoDe).

Objective: Delirium is commonly reported from the inpatients with Coronavirus disease 2019 (COVID-19) infection. As delirium is closely associated with adverse clinical outcomes, prediction and prevention of delirium is critical. We developed a machine learning (ML) model to predict delirium in hospitalized patients with COVID-19 and to identify modifiable factors to prevent delirium. Methods: The data set (n = 878) from four medical centers was constructed. Total of 78 predictors were included such as demographic characteristics, vital signs, laboratory results and medication, and the primary outcome was delirium occurrence during hospitalization. For analysis, the extreme gradient boosting (XGBoost) algorithm was applied, and the most influential factors were selected by recursive feature elimination. Among the indicators of performance for ML model, the area under the curve of the receiver operating characteristic (AUROC) curve was selected as the evaluation metric. Results: Regarding the performance of developed delirium prediction model, the accuracy, precision, recall, F1 score, and the AUROC were calculated (0.944, 0.581, 0.421, 0.485, 0.873, respectively). The influential factors of delirium in this model included were mechanical ventilation, medication (antipsychotics, sedatives, ambroxol, piperacillin/tazobactam, acetaminophen, ceftriaxone, and propacetamol), and sodium ion concentration (all p < 0.05). Conclusions: We developed and internally validated an ML model to predict delirium in COVID-19 inpatients. The model identified modifiable factors associated with the development of delirium and could be clinically useful for the prediction and prevention of delirium in COVID-19 inpatients.