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.

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.

Decreased hemoglobin levels increase the risk of developing dementia among the elderly. However, the underlying mechanisms that link decreased hemoglobin levels to incident dementia still remain unclear, possibly due to the fact that few studies have reported on the relationship between low hemoglobin levels and neuroimaging markers. We, therefore, investigated the relationships between decreased hemoglobin levels, cerebral small-vessel disease (CSVD), and cortical atrophy in cognitively healthy women and men.

Cognitively normal women (n = 1,022) and men (n = 1,018) who underwent medical check-ups and magnetic resonance imaging (MRI) were enrolled at a health promotion center. We measured hemoglobin levels, white matter hyperintensities (WMH) scales, lacunes, and microbleeds. Cortical thickness was automatically measured using surface based methods. Multivariate regression analyses were performed after controlling for possible confounders.

Decreased hemoglobin levels were not associated with the presence of WMH, lacunes, or microbleeds in women and men. Among women, decreased hemoglobin levels were associated with decreased cortical thickness in the frontal (Estimates, 95% confidence interval, −0.007, (−0.013, −0.001)), temporal (−0.010, (−0.018, −0.002)), parietal (−0.009, (−0.015, −0.003)), and occipital regions (−0.011, (−0.019, −0.003)). Among men, however, no associations were observed between hemoglobin levels and cortical thickness.

Our findings suggested that decreased hemoglobin levels affected cortical atrophy, but not increased CSVD, among women, although the association is modest. Given the paucity of modifiable risk factors for age-related cognitive decline, our results have important public health implications.

There is increasing evidence of a relationship between underweight or obesity and dementia risk. Several studies have investigated the relationship between body weight and brain atrophy, a pathological change preceding dementia, but their results are inconsistent. Therefore, we aimed to evaluate the relationship between body mass index (BMI) and cortical atrophy among cognitively normal participants.

We recruited cognitively normal participants (n = 1,111) who underwent medical checkups and detailed neurologic screening, including magnetic resonance imaging (MRI) in the health screening visits between September 2008 and December 2011. The main outcome was cortical thickness measured using MRI. The number of subjects with five BMI groups in men/women was 9/9, 148/258, 185/128, 149/111, and 64/50 in underweight, normal, overweight, mild obesity, and moderate to severe obesity, respectively. Linear and non-linear relationships between BMI and cortical thickness were examined using multiple linear regression analysis and generalized additive models after adjustment for potential confounders.

Among men, underweight participants showed significant cortical thinning in the frontal and temporal regions compared to normal weight participants, while overweight and mildly obese participants had greater cortical thicknesses in the frontal region and the frontal, temporal, and occipital regions, respectively. However, cortical thickness in each brain region was not significantly different in normal weight and moderate to severe obesity groups. Among women, the association between BMI and cortical thickness was not statistically significant.

Our findings suggested that underweight might be an important risk factor for pathological changes in the brain, while overweight or mild obesity may be inversely associated with cortical atrophy in cognitively normal elderly males.

Epidemiological studies have reported that higher education (HE) is associated with a reduced risk of incident Alzheimer's disease (AD). However, after the clinical onset of AD, patients with HE levels show more rapid cognitive decline than patients with lower education (LE) levels. Although education level and cognition have been linked, there have been few longitudinal studies investigating the relationship between education level and cortical decline in patients with AD. The aim of this study was to compare the topography of cortical atrophy longitudinally between AD patients with HE (HE-AD) and AD patients with LE (LE-AD).

We prospectively recruited 36 patients with early-stage AD and 14 normal controls. The patients were classified into two groups according to educational level, 23 HE-AD (>9 years) and 13 LE-AD (≤9 years).

As AD progressed over the 5-year longitudinal follow-ups, the HE-AD showed a significant group-by-time interaction in the right dorsolateral frontal and precuneus, and the left parahippocampal regions compared to the LE-AD.

Our study reveals that the preliminary longitudinal effect of HE accelerates cortical atrophy in AD patients over time, which underlines the importance of education level for predicting prognosis.