Objective: Adult attachment style is linked to how an individual responds to threats or stress and is known to be related to the onset of psychiatric symptoms such as depression. However, as the current assessment of attachment type mainly relies on self-report questionnaires and can be prone to bias, there is a need to incorporate physiological factors along with psychological symptoms and history in this process. We aimed to predict the measurement of two important types of adult attachment with heart rate variability (HRV), early life stress experience, and subjective psychiatric symptoms. Methods: Five hundred eighty-two subjects with depressive disorder were recruited retrospectively from January 2015 to June 2021. The experience of early life stress and psychiatric symptoms were collected, and HRV measures were obtained as input for an ensembled Voting Regressor model of machine learning-based regression models, including linear regression, ElasticNet, Support Vector Machine (SVM), Random Forest, and Extreme Gradient Boosting (XGBoost). Results: Model performances evaluated with R-squared score averaged across 30 seeds were 0.377 and 0.188 for anxious- and avoidant-attachment, respectively. Mean absolute error averaged to 13.251 and 12.083, respectively. Shapley value importance analysis indicated that for both attachment types, the most important feature was the trait-anxiety, followed by emotional abuse, state-anxiety or self-reported depressive symptoms, and fear or helplessness felt in the moment of an early life stressor. Conclusion Our results provide the evidence base that may be utilized in clinical settings to predict the degree of attachment type using bio-psychological factors.

Objective: Adult attachment style is linked to how an individual responds to threats or stress and is known to be related to the onset of psychiatric symptoms such as depression. However, as the current assessment of attachment type mainly relies on self-report questionnaires and can be prone to bias, there is a need to incorporate physiological factors along with psychological symptoms and history in this process. We aimed to predict the measurement of two important types of adult attachment with heart rate variability (HRV), early life stress experience, and subjective psychiatric symptoms. Methods: Five hundred eighty-two subjects with depressive disorder were recruited retrospectively from January 2015 to June 2021. The experience of early life stress and psychiatric symptoms were collected, and HRV measures were obtained as input for an ensembled Voting Regressor model of machine learning-based regression models, including linear regression, ElasticNet, Support Vector Machine (SVM), Random Forest, and Extreme Gradient Boosting (XGBoost). Results: Model performances evaluated with R-squared score averaged across 30 seeds were 0.377 and 0.188 for anxious- and avoidant-attachment, respectively. Mean absolute error averaged to 13.251 and 12.083, respectively. Shapley value importance analysis indicated that for both attachment types, the most important feature was the trait-anxiety, followed by emotional abuse, state-anxiety or self-reported depressive symptoms, and fear or helplessness felt in the moment of an early life stressor. Conclusion Our results provide the evidence base that may be utilized in clinical settings to predict the degree of attachment type using bio-psychological factors.

Objective: Adult attachment style is linked to how an individual responds to threats or stress and is known to be related to the onset of psychiatric symptoms such as depression. However, as the current assessment of attachment type mainly relies on self-report questionnaires and can be prone to bias, there is a need to incorporate physiological factors along with psychological symptoms and history in this process. We aimed to predict the measurement of two important types of adult attachment with heart rate variability (HRV), early life stress experience, and subjective psychiatric symptoms. Methods: Five hundred eighty-two subjects with depressive disorder were recruited retrospectively from January 2015 to June 2021. The experience of early life stress and psychiatric symptoms were collected, and HRV measures were obtained as input for an ensembled Voting Regressor model of machine learning-based regression models, including linear regression, ElasticNet, Support Vector Machine (SVM), Random Forest, and Extreme Gradient Boosting (XGBoost). Results: Model performances evaluated with R-squared score averaged across 30 seeds were 0.377 and 0.188 for anxious- and avoidant-attachment, respectively. Mean absolute error averaged to 13.251 and 12.083, respectively. Shapley value importance analysis indicated that for both attachment types, the most important feature was the trait-anxiety, followed by emotional abuse, state-anxiety or self-reported depressive symptoms, and fear or helplessness felt in the moment of an early life stressor. Conclusion Our results provide the evidence base that may be utilized in clinical settings to predict the degree of attachment type using bio-psychological factors.

Objective: Adult attachment style is linked to how an individual responds to threats or stress and is known to be related to the onset of psychiatric symptoms such as depression. However, as the current assessment of attachment type mainly relies on self-report questionnaires and can be prone to bias, there is a need to incorporate physiological factors along with psychological symptoms and history in this process. We aimed to predict the measurement of two important types of adult attachment with heart rate variability (HRV), early life stress experience, and subjective psychiatric symptoms. Methods: Five hundred eighty-two subjects with depressive disorder were recruited retrospectively from January 2015 to June 2021. The experience of early life stress and psychiatric symptoms were collected, and HRV measures were obtained as input for an ensembled Voting Regressor model of machine learning-based regression models, including linear regression, ElasticNet, Support Vector Machine (SVM), Random Forest, and Extreme Gradient Boosting (XGBoost). Results: Model performances evaluated with R-squared score averaged across 30 seeds were 0.377 and 0.188 for anxious- and avoidant-attachment, respectively. Mean absolute error averaged to 13.251 and 12.083, respectively. Shapley value importance analysis indicated that for both attachment types, the most important feature was the trait-anxiety, followed by emotional abuse, state-anxiety or self-reported depressive symptoms, and fear or helplessness felt in the moment of an early life stressor. Conclusion Our results provide the evidence base that may be utilized in clinical settings to predict the degree of attachment type using bio-psychological factors.

Objective: Adult attachment style is linked to how an individual responds to threats or stress and is known to be related to the onset of psychiatric symptoms such as depression. However, as the current assessment of attachment type mainly relies on self-report questionnaires and can be prone to bias, there is a need to incorporate physiological factors along with psychological symptoms and history in this process. We aimed to predict the measurement of two important types of adult attachment with heart rate variability (HRV), early life stress experience, and subjective psychiatric symptoms. Methods: Five hundred eighty-two subjects with depressive disorder were recruited retrospectively from January 2015 to June 2021. The experience of early life stress and psychiatric symptoms were collected, and HRV measures were obtained as input for an ensembled Voting Regressor model of machine learning-based regression models, including linear regression, ElasticNet, Support Vector Machine (SVM), Random Forest, and Extreme Gradient Boosting (XGBoost). Results: Model performances evaluated with R-squared score averaged across 30 seeds were 0.377 and 0.188 for anxious- and avoidant-attachment, respectively. Mean absolute error averaged to 13.251 and 12.083, respectively. Shapley value importance analysis indicated that for both attachment types, the most important feature was the trait-anxiety, followed by emotional abuse, state-anxiety or self-reported depressive symptoms, and fear or helplessness felt in the moment of an early life stressor. Conclusion Our results provide the evidence base that may be utilized in clinical settings to predict the degree of attachment type using bio-psychological factors.

Objective: Low-intensity transcranial focused ultrasound (tFUS) has emerged as a promising non-invasive brain stimulation modality with high spatial selectivity and the ability to reach deep brain areas. The present study aimed to investigate the safety and effectiveness of low-intensity tFUS in treating major depressive disorder. Methods: Participants were recruited in an outpatient clinic and randomly assigned to either the verum tFUS or sham stimulation group. The intervention group received six sessions of tFUS stimulation to the left dorsolateral prefrontal cortex over two weeks. Neuropsychological assessments were conducted before and after the sessions. Resting-state functional magnetic resonance imaging (rsfMRI) was also performed to evaluate changes in functional connectivity (FC). The primary outcome measure was the change in depressive symptoms, assessed with the Montgomery–Åsberg Depression Rating Scale (MADRS). Results: The tFUS stimulation sessions were well tolerated without any undesirable side effects. The analysis revealed a significant main effect of session sequence on the MADRS scores and significant interactions between the session sequences and groups. The rsfMRI analysis showed a higher FC correlation between the right superior part of the subgenual anterior cingulate cortex (sgACC) and several other brain regions in the verum group compared with the sham group. Conclusion Our results reveal that tFUS stimulation clinically improved MADRS scores with network-level modulation of a sgACC subregion. This randomized, sham-controlled clinical trial, the first study of its kind, demonstrated the safety and probable efficacy of tFUS stimulation for the treatment of depression.

Primary aldosteronism (PA) is a common, yet underdiagnosed cause of secondary hypertension. It is characterized by an overproduction of aldosterone, leading to hypertension and/or hypokalemia. Despite affecting between 5.9% and 34% of patients with hypertension, PA is frequently missed due to a lack of clinical awareness and systematic screening, which can result in significant cardiovascular complications. To address this, medical societies have developed clinical practice guidelines to improve the management of hypertension and PA. The Korean Endocrine Society, drawing on a wealth of research, has formulated new guidelines for PA. A task force has been established to prepare PA guidelines, which encompass epidemiology, pathophysiology, clinical presentation, diagnosis, treatment, and follow-up care. The Korean clinical guidelines for PA aim to deliver an evidence-based protocol for PA diagnosis, treatment, and patient monitoring. These guidelines are anticipated to ease the burden of this potentially curable condition.

Objective: The aim of this study was to investigate the association between the use of statins and the occurrence of delirium in a large cohort of patients in the intensive care unit (ICU), considering disease severity and statin properties. Methods: We obtained clinical and demographical information from 3,604 patients admitted to the ICU from January 2013 to April 2020. This included information on daily statin use and delirium state, as assessed by the Confusion Assessment Method for ICU. We used inverse probability of treatment weighting and categorized the patients into four groups based on the Acute Physiology and Chronic Health Evaluation II score (group 1: 0−10 - mild; group 2: 11−20 -mild to moderate; group 3: 21−30 - moderate to severe; group 4: ＞ 30 - severe). We analyzed the association between the use of statin and the occurrence of delirium in each group, while taking into account the properties of statins. Results: Comparisons between statin and non-statin patient groups revealed that only in group 2, patients who were administered statin showed significantly higher occurrence of delirium (p = 0.004, odds ratio [OR] = 1.58) compared to the patients who did not receive statin. Regardless of whether statins were lipophilic (p = 0.036, OR = 1.47) or hydrophilic (p = 0.032, OR = 1.84), the occurrence of delirium was higher only in patients from group 2. Conclusion The use of statins may be associated with the increases in the risk of delirium occurrence in patients with mild to moderate disease severity, irrespective of statin properties.

Purpose: Delirium in the intensive care unit (ICU) poses a significant safety and socioeconomic burden to patients and caregivers.However, invasive interventions for managing delirium have severe drawbacks. To reduce unnecessary interventions during ICU hospitalization, we aimed to investigate the features of delirium among ICU patients according to the occurrence of hypoactive symptoms, which are not expected to require invasive intervention. Materials and Methods: Psychiatrists assessed all patients with delirium in the ICU during hospitalization. Patients were grouped into two groups: a “non-hypoactive” group that experienced the non-hypoactive motor subtype once or more or a “hypoactive only” group that only experienced the hypoactive motor subtype. Clinical variables routinely gathered for clinical management were collected from electronic medical records. Group comparisons and logistic regression analyses were conducted. Results: The non-hypoactive group had longer and more severe delirium episodes than the hypoactive only group. Although the non-hypoactive group was prescribed more antipsychotics and required restraints longer, the hypoactive only group also received both interventions. In multivariable logistic regression analysis, BUN [odds ratio (OR): 0.993, pH OR: 0.202], sodium (OR: 1.022), RASS score (OR: 1.308) and whether restraints were applied [OR: 1.579 (95% confidence interval 1.194–2.089), p<0.001] were significant predictors of hypoactive only group classification. Conclusion Managing and predicting delirium patients based on whether patients experienced non-hypoactive delirium may be clinically important. Variables obtained during the initial 48 hours can be used to determine which patients are likely to require invasive interventions.

Objective: This study investigated whether sleep and stress mutually interact to induce changes in white matter integrity. Methods: Diffusion tensor imaging (DTI) was conducted on 36 participants (male=22, female=14; mean age=38.33±12.78 years). Participants were divided into three groups depending on their sleep quality and stress levels: poor sleepers with stress, poor sleepers without stress, and good sleepers. Sleep quality and stress level were evaluated using the Pittsburgh Sleep Quality Index and the Life Experiences Survey, respectively. Fractional anisotropy (FA) values were calculated employing DTI tractography. Results: After controlling for age and sex, poor sleepers with stress exhibited a lower FA of the left inferior cerebellar peduncle (ICP) than did poor sleepers without stress (t=2.81, p=0.02). Poor sleepers without stress showed a higher FA of the right middle longitudinal fasciculus (MdLF) than did good sleepers (t=3.35, p=0.006). Conclusion The current study reports the effects of sleep, stress, and their interaction on the white matter integrities of the ICP and MdLF. ICP change seems to be associated with sleep disturbances related to stress, while MdLF change would be associated with sleep disturbances unrelated to stress.