Predicting the course of depression is necessary for personalized treatment. Impaired glucose metabolism (IGM) was introduced as a promising depression biomarker, but no consensus was made. This study aimed to predict IGM at the time of depression diagnosis and examine the relationship between long-term prognosis and predicted results.

Clinical data were extracted from four electronic health records in South Korea. The study population included patients with depression, and the outcome was IGM within 1 year. One database was used to develop the model using three algorithms. External validation was performed using the best algorithm across the three databases. The area under the curve (AUC) was calculated to determine the model’s performance. Kaplan–Meier and Cox survival analyses of the risk of hospitalization for depression as the long-term outcome were performed. A meta-analysis of the long-term outcome was performed across the four databases.

A prediction model was developed using the data of 3,668 people, with an AUC of 0.781 with least absolute shrinkage and selection operator (LASSO) logistic regression. In the external validation, the AUCs were 0.643, 0.610, and 0.515. Through the predicted results, survival analysis and meta-analysis were performed; the hazard ratios of risk of hospitalization for depression in patients predicted to have IGM was 1.20 (95% confidence interval [CI] 1.02–1.41, p = 0.027) at a 3-year follow-up.

We developed prediction models for IGM occurrence within a year. The predicted results were related to the long-term prognosis of depression, presenting as a promising IGM biomarker related to the prognosis of depression.

Mood disorders require consistent management of symptoms to prevent recurrences of mood episodes. Circadian rhythm (CR) disruption is a key symptom of mood disorders to be proactively managed to prevent mood episode recurrences. This study aims to predict impending mood episodes recurrences using digital phenotypes related to CR obtained from wearable devices and smartphones.

The study is a multicenter, nationwide, prospective, observational study with major depressive disorder, bipolar disorder I, and bipolar II disorder. A total of 495 patients were recruited from eight hospitals in South Korea. Patients were followed up for an average of 279.7 days (a total sample of 75 506 days) with wearable devices and smartphones and with clinical interviews conducted every 3 months. Algorithms predicting impending mood episodes were developed with machine learning. Algorithm-predicted mood episodes were then compared to those identified through face-to-face clinical interviews incorporating ecological momentary assessments of daily mood and energy.

Two hundred seventy mood episodes recurred in 135 subjects during the follow-up period. The prediction accuracies for impending major depressive episodes, manic episodes, and hypomanic episodes for the next 3 days were 90.1, 92.6, and 93.0%, with the area under the curve values of 0.937, 0.957, and 0.963, respectively.

We predicted the onset of mood episode recurrences exclusively using digital phenotypes. Specifically, phenotypes indicating CR misalignment contributed the most to the prediction of episodes recurrences. Our findings suggest that monitoring of CR using digital devices can be useful in preventing and treating mood disorders.

There are growing concerns about the impact of the COVID-19 pandemic on the mental health of older adults. We examined the effect of the pandemic on the risk of depression in older adults.

We analyzed data from the prospective cohort study of Korean older adults, which has been followed every 2 years. Among the 2308 participants who completed both the third and the fourth follow-up assessments, 58.4% completed their fourth follow-up before the outbreak of COVID-19 and the rest completed it during the pandemic. We conducted face-to-face diagnostic interviews using Mini International Neuropsychiatric Interview and used Geriatric Depression Scale. We performed generalized estimating equations and logistic regression analyses.

The COVID-19 pandemic was associated with increased depressive symptoms in older adults [b (standard error) = 0.42 (0.20), p = 0.040] and a doubling of the risk for incident depressive disorder even in euthymic older adults without a history of depression (odds ratio = 2.44, 95% confidence interval 1.18–5.02, p = 0.016). Less social activities, which was associated with the risk of depressive disorder before the pandemic, was not associated with the risk of depressive disorder during the pandemic. However, less family gatherings, which was not associated with the risk of depressive disorder before the pandemic, was associated with the doubled risk of depressive disorder during the pandemic.

The COVID-19 pandemic significantly influences the risk of late-life depression in the community. Older adults with a lack of family gatherings may be particularly vulnerable.

This study aims to identify factors associated with divorce following breast cancer diagnosis and measures the impact of divorce on the quality of life (QoL) of patients.

We used cross-sectional survey data collected at breast cancer outpatient clinics in South Korea from November 2018 to April 2019. Adult breast cancer survivors who completed active treatment without any cancer recurrence at the time of the survey (N = 4,366) were included. The participants were classified into two groups: “maintaining marriage” and “being divorced,” between at the survey and at the cancer diagnosis. We performed logistic regression and linear regression to identify the factors associated with divorce after cancer diagnosis and to compare the QoL of divorced and nondivorced survivors.

Approximately 11.1/1,000 of married breast cancer survivors experienced divorce after cancer diagnosis. Younger age, lower education, and being employed at diagnosis were associated with divorce. Being divorced survivors had significantly lower QoL (Coefficient [Coef] = −7.50; 95% CI = −13.63, −1.36), social functioning (Coef = −9.47; 95% CI = −16.36, −2.57), and body image (Coef = −8.34; 95% CI = −6.29, −0.39) than survivors who remained married. They also experienced more symptoms including pain, insomnia, financial difficulties, and distress due to hair loss.

Identifying risk factors of divorce will ultimately help ascertain the resources necessary for early intervention.

Personality may predispose family caregivers to experience caregiving differently in similar situations and influence the outcomes of caregiving. A limited body of research has examined the role of some personality traits for health-related quality of life (HRQoL) among family caregivers of persons with dementia (PWD) in relation to burden and depression.

Data from a large clinic-based national study in South Korea, the Caregivers of Alzheimer's Disease Research (CARE), were analyzed (N = 476). Path analysis was performed to explore the association between family caregivers’ personality traits and HRQoL. With depression and burden as mediating factors, direct and indirect associations between five personality traits and HRQoL of family caregivers were examined.

Results demonstrated the mediating role of caregiver burden and depression in linking two personality traits (neuroticism and extraversion) and HRQoL. Neuroticism and extraversion directly and indirectly influenced the mental HRQoL of caregivers. Neuroticism and extraversion only indirectly influenced their physical HRQoL. Neuroticism increased the caregiver's depression, whereas extraversion decreased it. Neuroticism only was mediated by burden to influence depression and mental and physical HRQoL.

Personality traits can influence caregiving outcomes and be viewed as an individual resource of the caregiver. A family caregiver's personality characteristics need to be assessed for tailoring support programs to get the optimal benefits from caregiver interventions.

Whether an association exists between cerebral microbleeds (CMBs) and functional recovery after ischemic stroke is unclear. We aimed to evaluate the association between CMBs and functional outcome after acute ischemic stroke.

Consecutive patients with acute stroke were enrolled, and all patients were stratified into good and poor functional outcome groups at discharge and 6 months after ischemic stroke by using a modified Rankin Scale score. Cardiovascular risk factors, CMBs, and white matter hyperintensities were compared between these two outcome groups. Logistic regression analysis was used to estimate the risk of poor functional outcomes.

A total of 225 patients were enrolled, 121 of whom were classified as having a good functional outcome at discharge and 142 as having a good 6-month functional outcome. The presence of CMBs was associated with a poor functional outcome at discharge [CMBs (+) patients in poor vs. good functional group; 48.1% vs. 30.6%; p=0.007] and 6 months [53.0% vs. 30.3%; p=0.001]. After adjustment for confounding factors, only the presence of infratentorial CMBs was associated with a poor functional outcome at discharge and 6 months. The poor functional outcome group had more CMBs than the good outcome group at 6 months.

Infratentorial cerebral microbleeds were significantly associated with worse functional outcomes not only in the early phase of ischemic stroke but also in the chronic phase. These findings suggest that the presence of infratentorial CMBs can predict poor functional outcome after acute ischemic stroke.

Social support programs for dementia caregivers were widely used in order to reduce care burden. We investigated which types of social supports can reduce psychological and non-psychological burdens of dementia caregivers, and explored the mechanism of those social supports.

We evaluated 731 community-dwelling dementia patients and their caregivers from the National Survey of Dementia Care in South Korea. We investigated the five types of social supports (emotional support, informational support, tangible support, positive social interaction, affectionate support) using the Medical Outcomes Study Social Support Survey in each caregiver. The mechanisms of specific types of social support on psychological/non-psychological burden were examined using path analysis.

Positive social interaction and affectionate support reduced psychological burden via direct and indirect paths. Tangible support reduced the non-psychological burden via direct and indirect paths. Informational support and emotional support were not helpful for reducing psychological or non-psychological burden. A maximum of 20% of psychological burden could be relieved by positive social interaction and 10.3% of that could be reduced by affectionate support. Tangible support was associated with a 15.1% maximal improvement in non-psychological burden.

In order to reduce caregiver burden in dementia effectively, psychosocial interventions should be tailored to target type of caregiver burden.

It is controversial whether Borna disease virus (BDV) infects humans and causes psychiatric disorders.

The relationship between BDV infection and schizophrenia with deficit syndrome was investigated.

Using the Schedule for the Deficit Syndrome, 62 schizophrenic in-patients were selected from three psychiatric hospitals. RNA was extracted from peripheral blood mononuclear cells and analyzed using nested reverse transcriptase-polymerase chain reaction with primers to detect BDV p24 and p40.

BDV transcripts were not detected in samples from any of the 62 schizophrenic patients. These data do not support an etiologic association between BDV infection and the deficit form of schizophrenia.