Association of Geriatric Depressive Symptoms and Government-Initiated Senior Employment Program: A Population-Based Study.

OBJECTIVE: The impact of the government-initiated senior employment program (GSEP) on geriatric depressive symptoms is underexplored. Unearthing this connection could facilitate the planning of future senior employment programs and geriatric depression interventions. In the present study, we aimed to elucidate the possible association between geriatric depressive symptoms and GSEP in older adults. METHODS: This study employed data from 9,287 participants aged 65 or older, obtained from the 2020 Living Profiles of Older People Survey. We measured depressive symptoms using the Korean version of the 15-item Geriatric Depression Scale. The principal exposure of interest was employment status and GSEP involvement. Data analysis involved multiple linear regression. RESULTS: Employment, independent of income level, showed association with decreased depressive symptoms compared to unemployment (p<0.001). After adjustments for confounding variables, participation in GSEP jobs showed more significant reduction in depressive symptoms than non-GSEP jobs (ß=-0.968, 95% confidence interval [CI]=-1.197 to -0.739, p<0.001 for GSEP jobs, ß=-0.541, 95% CI=-0.681 to -0.401, p<0.001 for non-GSEP jobs). Notably, the lower income tertile in GSEP jobs showed a substantial reduction in depressive symptoms compared to all income tertiles in non-GSEP jobs. CONCLUSION: The lower-income GSEP group experienced lower depressive symptoms and life dissatisfaction compared to non-GSEP groups regardless of income. These findings may provide essential insights for the implementation of government policies and community-based interventions.

Factors in turnover intention of cardiothoracic surgery residents.

Increasing numbers of cardiothoracic surgery residents are resigning, without completing their training. This study analyzes how their turnover intention is related to the training environment, and individual psychological factors. Responses by 57 Korean cardiothoracic surgery residents were analyzed. Their levels of depression, anxiety, grit, and empathy, working conditions, the effect of someone's presence to discuss their concerns with, burnout, and turnover intention were identified as the research variables. Descriptive statistical analysis, correlation analysis, and structural equation modeling were used for data analysis. Burnout has the most significant relationship with turnover intention. It has a mediating effect on the influence of depression, grit (sustained interest), and working conditions, over turnover intention. Empathy, and the presence of someone to discuss concerns with, also affect turnover intention directly. The study also confirmed that grit and work satisfaction affect turnover intention indirectly, through burnout. The study identified both individual- and systemic-level factors for an effective training environment, to reduce cardiothoracic surgery residents' tendencies of leaving the residency program, and supporting them for greater satisfaction with their career choice. In order to resolve negative emotions such as burnout and depression, and foster empathy, a human resource development program for the residents' psychological support must be prepared. The program director should be adequately educated to take charge of the training program, oversee the residents' education and welfare, and perform the roles of role-model and mentor.

Mediating effect of depressive symptoms on the relationship between digital literacy and cognitive function in older adults.

Introduction: Although several studies have examined the individual relationships among digital literacy, cognitive function, and depressive symptoms, few have integrated all three factors into a single model. This study aimed to address this gap by investigating the mediating effect of depressive symptoms on the relationship between digital literacy and cognition. In doing so, we hoped to contribute to a more comprehensive understanding of the complex interplay among these variables and their implications for mental health and well-being. Methods: Participants were 7,988 older adults (65 years or older) who participated in the Living Profiles of Older People Survey 2020. The main type of exposure was digital literacy (communication, information, media, and online transaction literacy). The main outcomes were depressive symptoms measured using the Short Geriatric Depression Scale of Korean version and cognitive function measured using the Mini-Mental State Examination score. Multiple linear regression and mediation analyses were also performed. Results: After adjusting for covariates, our analysis found a significant association between digital literacy and both depressive symptoms and cognitive function (ß of four types of digital literacy and depressive symptoms = -0.123, -0.172, -0.702, and - 0.639, respectively; ß of four types of digital literacy and cognitive function = 2.102, 2.217, 1.711, and 1.436, respectively). Moreover, our study showed that depressive symptoms play a mediating role in the relationship between media and online transaction literacy and cognitive function (95% CI of indirect effects = 0.0647-0.1212 and 0.0639-0.1277, respectively), implying an indirect pathway (digital literacy, depressive symptoms, and cognitive function). Discussion: This study sheds light on the relationship between digital literacy, depressive symptoms, and cognitive function in older adults. We found that depressive symptoms mediated the association between specific aspects of digital literacy (online transaction and media literacy) and cognitive function. Our results indicate that community-based digital literacy programs could be effective in reducing depression and preserving or improving cognitive function in older adults.

Relationships of walking activity with depressed mood and suicidal ideation among the middle-aged Korean population: a nationwide cross-sectional study.

Introduction: The suicide rate of middle-aged adults has increased rapidly, which is a significant public health concern. A depressed mood and suicidal ideation are significant risk factors for suicide, and non-pharmacological interventions such as exercise therapy have been suggested as potential treatments. Walking is a feasible and accessible form of exercise therapy for middle-aged adults. Methods: We conducted a study based on the Seventh Korea National Health and Nutrition Examination Survey (2016-2018) data of 6,886 general middle-aged adults in South Korea to investigate the relationships of walking exercise with depressed mood and suicidal ideation. Multiple logistic regression analysis was used to adjust for confounding variables. Sampling weights were applied to obtain estimates for the general Korean population. Results: Participants who walked ≥5 days per week had a significantly lower odds ratio (OR) for depressed mood [OR = 0.625, 95% confidence interval (CI): 0.424-0.921, p = 0.018] and suicidal ideation (OR = 0.252, 95% CI: 0.125-0.507, p < 0.001) compared to those who never walked, regardless of the duration of exercise. The same results were obtained for males after stratifying the data by sex and suicidal ideation was associated with walking in females. Conclusion: Regular walking exercise was associated with diminished mental health problems in middle-aged adults. Light walks may serve as a useful starting point for patients with serious mental health issues, such as suicidal ideation.

Moderators of the Association Between Contact Frequency With Non-Cohabitating Adult Children and Depressive Symptoms Among Community-Dwelling Older Adults.

OBJECTIVE: Contact frequency with adult children plays a critical role in late-life depression. However, evidence on possible moderators of this association remains limited. Moreover, considering alterations in contact modes after the coronavirus disease-2019 pandemic, there is a need to investigate this association post-pandemic to develop effective therapeutic interventions. METHODS: This study included 7,573 older adults who completed the Living Profiles of the Older People Survey in Korea. Participants' contact frequency and depressive symptoms were analyzed. Regression analysis was performed after adjusting for covariates. The moderating effects of variables were verified using a process macro. RESULTS: Multivariable logistic regression analysis revealed that infrequent face-to-face (odd ratio [OR]=1.86, 95% confidence interval [CI]=1.55-2.22) and non-face-to-face contact (OR=1.23, 95% CI=1.04-1.45) in the non-cohabitating adult children group was associated with a higher risk of late-life depression compared to that in the frequent contact group. Linear regression analysis indicated consistent results for face-to-face and non-face-to-face contact (estimate=0.458, standard error [SE]=0.090, p<0.001 and estimate=0.236, SE= 0.074, p=0.001, respectively). Moderation analysis revealed that the association between late-life depression and frequency of face-toface contact was moderated by age, household income quartiles, number of chronic diseases, physical activity frequency, presence of spouse, nutritional status, and whether the effect of frequency of non-face-to-face contact on late-life depression was increased by participation in social activity, frequent physical activity, and good cognitive function (p for interaction<0.05). CONCLUSION: Frequent contact with non-cohabitating children lowers the risk of depression later in life. Several variables were identified as significant moderators of contact frequency and depression symptoms.

Trans-ethnic genomic informed risk assessment for Alzheimer's disease: An International Hundred K+ Cohorts Consortium study.

BACKGROUND: As a collaboration model between the International HundredK+ Cohorts Consortium (IHCC) and the Davos Alzheimer's Collaborative (DAC), our aim was to develop a trans-ethnic genomic informed risk assessment (GIRA) algorithm for Alzheimer's disease (AD). METHODS: The GIRA model was created to include polygenic risk score calculated from the AD genome-wide association study loci, the apolipoprotein E haplotypes, and non-genetic covariates including age, sex, and the first three principal components of population substructure. RESULTS: We validated the performance of the GIRA model in different populations. The proteomic study in the participant sites identified proteins related to female infertility and autoimmune thyroiditis and associated with the risk scores of AD. CONCLUSIONS: As the initial effort by the IHCC to leverage existing large-scale datasets in a collaborative setting with DAC, we developed a trans-ethnic GIRA for AD with the potential of identifying individuals at high risk of developing AD for future clinical applications.

Comparison of automated quantification of amyloid deposition between PMOD and Heuron.

Several programs are widely used for clinical and research purposes to automatically quantify the degree of amyloid deposition in the brain using positron emission tomography (PET) images. Given that very few studies have investigated the use of Heuron, a PET image quantification software approved for clinical use, this study aimed to compare amyloid deposition values quantified from 18F-flutemetamol PET images using PMOD and Heuron. Amyloid PET data obtained from 408 patients were analysed using each quantitative program; moreover, the standardized uptake value ratios (SUVRs) of target areas were obtained by dividing the standardized uptake value (SUV) of the target region by the SUV of cerebellar grey matter as a reference. Compared with PMOD, Heuron yielded significantly higher SUVRs for all target areas (paired sample t-test, p < 0.001), except for the PC/PCC (p = 0.986). However, the Bland-Altman plot analysis indicated that the two quantitative methods may be used interchangeably. Moreover, receiver operating characteristic curve analysis revealed no significant between-method difference in the performance of the SUVRs in evaluating the visual positivity of amyloid deposits (p = 0.948). In conclusion, Heuron and PMOD have comparable performance in quantifying the degree of amyloid deposits in PET images.

Relationships of Physical Activity, Depression, and Sleep with Cognitive Function in Community-Dwelling Older Adults.

This cross-sectional, observational study aimed to integrate the analyses of relationships of physical activity, depression, and sleep with cognitive function in community-dwelling older adults using a single model. To this end, physical activity, sleep, depression, and cognitive function in 864 community-dwelling older adults from the Suwon Geriatric Mental Health Center were assessed using the International Physical Activity Questionnaire, Montgomery-Asberg Depression Rating Scale, Pittsburgh Sleep Quality Index, and Mini-Mental State Examination for Dementia Screening, respectively. Their sociodemographic characteristics were also recorded. After adjusting for confounders, multiple linear regression analysis was performed to investigate the effects of physical activity, sleep, and depression on cognitive function. Models 4, 5, 7, and 14 of PROCESS were applied to verify the mediating and moderating effects of all variables. Physical activity had a direct effect on cognitive function (effect = 0.97, p < 0.01) and indirect effect (effect = 0.36; confidence interval: 0.18, 0.57) through depression. Moreover, mediated moderation effects of sleep were confirmed in the pathways where physical activity affects cognitive function through depression (F-coeff = 13.37, p < 0.001). Furthermore, these relationships differed with age. Thus, the associations among physical activity, depression, and sleep are important in interventions for the cognitive function of community-dwelling older adults. Such interventions should focus on different factors depending on age.

The moderating effect of cognitive function on the association between social support and depressive symptoms among community-dwelling older adults: Cross-sectional and longitudinal analyses.

BACKGROUND: The moderating effect of cognitive function on the association between social support and late-life depressive symptoms has not been thoroughly investigated. Identifying cognitive function as a possible moderator of this association might help plan community-based interventions for late-life depressive symptoms. METHODS: Participants were community-dwelling older adults who visited a community-based mental health center. The ENRICHD Social Support Instrument and the Montgomery-Asberg Depression Rating Scale were used to evaluate social support and depressive symptoms, respectively. Cognitive function was assessed using the Korean version of the Mini-Mental State Examination. Data from 1088 and 506 participants were included in the cross-sectional and longitudinal analyses, respectively. Multiple linear regression analysis was performed to assess the effects of social support on depressive symptoms and the possible moderating effect of cognition. RESULTS: After adjusting for possible confounders, greater social support at baseline was associated with fewer depressive symptoms in both cross-sectional (estimate = -0.25 standard error [SE] = 0.03, P < 0.001) and longitudinal analyses (estimate = -0.11, SE = 0.05, P = 0.014). Moreover, the association between social support and depressive symptoms was significantly moderated by cognitive function (P for interaction < 0.001 for cross-sectional analysis, and P for interaction = 0.011 for longitudinal analysis). LIMITATIONS: The tool for assessing social support was self-reported. There may have been a selection bias in the study sample. CONCLUSIONS: Greater social support was associated with fewer late-life depressive symptoms in both analyses. However, social support may have less benefits for alleviating depressive symptoms in older adults with cognitive decline.

Independent replication of advanced brain age in mild cognitive impairment and dementia: detection of future cognitive dysfunction.

We previously developed a novel machine-learning-based brain age model that was sensitive to amyloid. We aimed to independently validate it and to demonstrate its utility using independent clinical data. We recruited 650 participants from South Korean memory clinics to undergo magnetic resonance imaging and clinical assessments. We employed a pretrained brain age model that used data from an independent set of largely Caucasian individuals (n = 757) who had no or relatively low levels of amyloid as confirmed by positron emission tomography (PET). We investigated the association between brain age residual and cognitive decline. We found that our pretrained brain age model was able to reliably estimate brain age (mean absolute error = 5.68 years, r(650) = 0.47, age range = 49-89 year) in the sample with 71 participants with subjective cognitive decline (SCD), 375 with mild cognitive impairment (MCI), and 204 with dementia. Greater brain age was associated with greater amyloid and worse cognitive function [Odds Ratio, (95% Confidence Interval {CI}): 1.28 (1.06-1.55), p = 0.030 for amyloid PET positivity; 2.52 (1.76-3.61), p < 0.001 for dementia]. Baseline brain age residual was predictive of future cognitive worsening even after adjusting for apolipoprotein E e4 and amyloid status [Hazard Ratio, (95% CI): 1.94 (1.33-2.81), p = 0.001 for total 336 follow-up sample; 2.31 (1.44-3.71), p = 0.001 for 284 subsample with baseline Clinical Dementia Rating ≤ 0.5; 2.40 (1.43-4.03), p = 0.001 for 240 subsample with baseline SCD or MCI]. In independent data set, these results replicate our previous findings using this model, which was able to delineate significant differences in brain age according to the diagnostic stages of dementia as well as amyloid deposition status. Brain age models may offer benefits in discriminating and tracking cognitive impairment in older adults.

The effectiveness of a motivational enhancement smartphone application promoting lifestyle improvement for brain health: A randomized controlled trial.

Multidomain lifestyle modification is considered an effective intervention for dementia prevention due to its multifactorial nature. Recognizing that participants' activity adherence is crucial for successful lifestyle modification, our team developed a smartphone application to enhance motivation toward brain health behavior based on gamification theory, which influences behaviors by enhancing motivation. The developed smartphone application has two main functions: delivering supporting videos from family, friends, and medical staff, and self-evaluation. We assessed the effectiveness of this smartphone application with regard to brain health behavior. In this eight-week randomized controlled trial, 40 participants were randomly assigned to the smartphone application intervention group or control group. The primary outcome reflected participants' brain health behavior in three categories: physical activity, cognitive activity, and healthy diet. Each brain health behavior was measured by the Korean version of the Global Physical Activity Questionnaire, Cognitive Activity Score, and Mediterranean DASH Intervention for Neurodegenerative Delay Diet Score. Furthermore, we investigated the change in motivation, measured by the Situational Motivation Scale. Additionally, we reviewed participants' self-record diary during the first, fourth, and eighth week of intervention for evaluation of adherence. The intervention group was found to have a positive association with moderate metabolic equivalent activities (P = 0.01) and intrinsic motivation change (P = 0.01). There was a significant difference between the intervention and control groups regarding average physical activity at week 8 (P = 0.037). An eight-week intervention with the smartphone application induced physical activity of moderate intensity through intrinsic motivation enhancement. We suggest that the motivation enhancement application could be an efficient option for maintaining and promoting psychosocial health behavior. This smartphone application can be applied to any other disease that needs behavioral change. Through the application, a broader spectrum of the population, regardless of time, space, and human resources, can benefit from community health services. Trial registration: Korean National Clinical Trial Registry CRIS identifier: KCT0005231.

Structural covariance changes in major cortico-basal ganglia and thalamic networks in amyloid-positive patients with white matter hyperintensities.

Synergistic effects of amyloid deposition and cerebral small vessel disease (CSVD) on the systematic disruption of large-scale brain anatomical organization are not well known. We investigated the brain structural covariance network (SCN) in 245 cognitively impaired older adults with the information of amyloid deposition and CSVD represented by white matter hyperintensities (WMH). We stratified the participants into 4 groups based on amyloid burden (A+/A -) and WMH severity (W+/W-). Using source-based morphometry analysis, we selected 13 independent components (ICs) in functional brain networks. SCNs between ICs were defined using Pearson correlations between individual weights; SCNs of the A+W+ group were compared with those of other groups using Fisher's r-to-z transformation. Our results revealed that SCN characteristics related to amyloid burden with CSVD could be represented by decreased intra- and increased cortico-subcortical inter-network connectivity in the salience (SN) and default mode networks (DMN), decreased cortico-subcortical internetwork connectivity in the central executive network (CEN), and altered internetwork connectivity among DMN-SN-CEN. Amyloid deposition and CSVD maybe associated with altered connectivity in structural networks in the brain and should be considered when assessing network disruption.

Differential Effects of Sleep Disturbance and Malnutrition on Late-Life Depression Among Community-Dwelling Older Adults.

Background: Late-life depression is a complex phenomenon that cannot be fully understood simply as depression occurring in older adults, prompting researchers to suggest that it represents a component of geriatric syndrome. Given the inherent complexity and multifactorial nature of geriatric syndrome, understanding the interactions between the comorbid conditions involved is important for establishing appropriate preventive strategies. While sleep disturbance and malnutrition are common manifestations of geriatric syndrome, they have also been regarded as indicators of late-life depression. However, the differential effects of sleep disturbance and malnutrition on late-life depression and their interrelationships remain unclear. Objective: The objective of this study was to examine the effects of sleep disturbance and malnutrition on depression and the interactions between them among community-dwelling older adults. Methods: Sleep disturbance and malnutrition in 1,029 community-dwelling older adults from Suwon Geriatric Mental Health Center were assessed using the Pittsburgh Sleep Quality Index (PSQI) and Mini Nutritional Assessment (MNA), respectively. The Korean version of the Short Form of the Geriatric Depression Scale (SGDS-K) was used to evaluate depressive symptoms. Sociodemographic parameters were recorded. A multiple linear regression analysis was conducted to examine the effects of sleep and nutrition on depressive symptoms after adjusting for covariates. The effect size and conditional effects of sleep disturbance and malnutrition on late-life depression were assessed using Cohen's f2 values and the Johnson-Neyman technique, respectively. Results: After possible confounders were adjusted, the SGDS-K score was positively associated with the PSQI score (standardized beta = 0.166, P < 0.001) and negatively associated with the MNA score (standardized beta = -0.480, P < 0.001). The local effect size of the associations was small for PSQI and medium for MNA. A significant interaction was observed between the PSQI and MNA scores. The result of the Johnson-Neyman technique indicated that the influence of PSQI on SGDS-K became weaker and insignificant as nutritional status worsened. However, the association between the MNA and SGDS-K scores was significant regardless of PSQI. Conclusion: Both sleep disturbance and malnutrition were significantly associated with late-life depression, although malnutrition may be more critically associated with depression than sleep disturbance in community-dwelling older adults.

Associations of actigraphy derived rest activity patterns and circadian phase with clinical symptoms and polysomnographic parameters in chronic insomnia disorders.

We explored the associations of actigraphy-derived rest-activity patterns and circadian phase parameters with clinical symptoms and level 1 polysomnography (PSG) results in patients with chronic insomnia to evaluate the clinical implications of actigraphy-derived parameters for PSG interpretation. Seventy-five participants underwent actigraphy assessments and level 1 PSG. Exploratory correlation analyses between parameters derived from actigraphy, PSG, and clinical assessments were performed. First, participants were classified into two groups based on rest-activity pattern variables; group differences were investigated following covariate adjustment. Participants with poorer rest-activity patterns on actigraphy (low inter-day stability and high intra-daily variability) exhibited higher insomnia severity index scores than participants with better rest-activity patterns. No between-group differences in PSG parameters were observed. Second, participants were classified into two groups based on circadian phase variables. Late-phase participants (least active 5-h and most active 10-h onset times) exhibited higher insomnia severity scores, longer sleep and rapid eye movement latency, and lower apnea-hypopnea index than early-phase participants. These associations remained significant even after adjusting for potential covariates. Some actigraphy-derived rest-activity patterns and circadian phase parameters were significantly associated with clinical symptoms and PSG results, suggesting their possible adjunctive role in deriving plans for PSG lights-off time and assessing the possible insomnia pathophysiology.

Modeling Brain Volume Using Deep Learning-Based Physical Activity Features in Patients With Dementia.

There is a proven correlation between the severity of dementia and reduced brain volumes. Several studies have attempted to use activity data to estimate brain volume as a means of detecting reduction early; however, raw activity data are not directly interpretable and are unstructured, making them challenging to utilize. Furthermore, in the previous research, brain volume estimates were limited to total brain volume and the investigators were unable to detect reductions in specific regions of the brain that are typically used to characterize disease progression. We aimed to evaluate volume prediction of 116 brain regions through activity data obtained combining time-frequency domain- and unsupervised deep learning-based feature extraction methods. We developed a feature extraction model based on unsupervised deep learning using activity data from the National Health and Nutrition Examination Survey (NHANES) dataset (n = 14,482). Then, we applied the model and the time-frequency domain feature extraction method to the activity data of the Biobank Innovations for chronic Cerebrovascular disease With ALZheimer's disease Study (BICWALZS) datasets (n = 177) to extract activity features. Brain volumes were calculated from the brain magnetic resonance imaging of the BICWALZS dataset and anatomically subdivided into 116 regions. Finally, we fitted linear regression models to estimate each regional volume of the 116 brain areas based on the extracted activity features. Regression models were statistically significant for each region, with an average correlation coefficient of 0.990 ± 0.006. In all brain regions, the correlation was > 0.964. Particularly, regions of the temporal lobe that exhibit characteristic atrophy in the early stages of Alzheimer's disease showed the highest correlation (0.995). Through a combined deep learning-time-frequency domain feature extraction method, we could extract activity features based solely on the activity dataset, without including clinical variables. The findings of this study indicate the possibility of using activity data for the detection of neurological disorders such as Alzheimer's disease.

Baseline Clinical and Biomarker Characteristics of Biobank Innovations for Chronic Cerebrovascular Disease With Alzheimer's Disease Study: BICWALZS.

OBJECTIVE: We aimed to present the study design and baseline cross-sectional participant characteristics of biobank innovations for chronic cerebrovascular disease with Alzheimer's disease study (BICWALZS) participants. METHODS: A total of 1,013 participants were enrolled in BICWALZS from October 2016 to December 2020. All participants underwent clinical assessments, basic blood tests, and standardized neuropsychological tests (n=1,013). We performed brain magnetic resonance imaging (MRI, n=817), brain amyloid positron emission tomography (PET, n=713), single nucleotide polymorphism microarray chip (K-Chip, n=949), locomotor activity assessment (actigraphy, n=200), and patient-derived dermal fibroblast sampling (n=175) on a subset of participants. RESULTS: The mean age was 72.8 years, and 658 (65.0%) were females. Based on clinical assessments, total of 168, 534, 211, 80, and 20 had subjective cognitive decline, mild cognitive impairment (MCI), Alzheimer's dementia, vascular dementia, and other types of dementia or not otherwise specified, respectively. Based on neuroimaging biomarkers and cognition, 199, 159, 78, and 204 were cognitively normal (CN), Alzheimer's disease (AD)-related cognitive impairment, vascular cognitive impairment, and not otherwise specified due to mixed pathology (NOS). Each group exhibited many differences in various clinical, neuropsychological, and neuroimaging results at baseline. Baseline characteristics of BICWALZS participants in the MCI, AD, and vascular dementia groups were generally acceptable and consistent with 26 worldwide dementia cohorts and another independent AD cohort in Korea. CONCLUSION: The BICWALZS is a prospective and longitudinal study assessing various clinical and biomarker characteristics in older adults with cognitive complaints. Details of the recruitment process, methodology, and baseline assessment results are described in this paper.

Virtual reality-based monitoring test for MCI: A multicenter feasibility study.

Objectives: As the significance of the early diagnosis of mild cognitive impairment (MCI) has emerged, it is necessary to develop corresponding screening tools with high ecological validity and feasible biomarkers. Virtual reality (VR)-based cognitive assessment program, which is close to the daily life of the older adults, can be suitable screening tools for MCI with ecological validity and accessibility. Meanwhile, dehydroepiandrosterone (DHEA) has been observed at a low concentration in the older adults with dementia or cognitive decline, indicating its potential as a biomarker of MCI. This study aimed to determine the efficacy and usability of a VR cognitive assessment program and salivary DHEA for screening MCI. Methods: The VR cognitive assessment program and the traditional Montreal Cognitive Assessment (MOCA) test were performed on 12 patients with MCI and 108 healthy older adults. The VR program operates in a situation of caring for a grandchild, and evaluates the memory, attention, visuospatial, and executive functions. An analysis of covariance (ANCOVA), a partial correlation analysis, and receiving operating characteristic (ROC) curve analysis were conducted for statistical analysis. Results: According to the ANCOVA, no significant difference in MOCA scores was found between the normal and MCI groups (F = 2.36, p = 0.127). However, the VR total score of the MCI group was significantly lower than that of the normal group (F = 8.674, p = 0.004). There was a significant correlation between the MOCA and VR scores in the total and matched subdomain scores. The ROC curve analysis also showed a larger area under the curve (AUC) for the VR test (0.765) than for the MOCA test (0.598), and the sensitivity and specificity of the VR program were 0.833 and 0.722, respectively. Salivary DHEA was correlated with VR total (R 2 = 0.082, p = 0.01) and attention scores (R 2 = 0.086, p = 0.009). Conclusion: The VR cognitive test was as effective as the traditional MOCA test in the MCI classification and safe enough for older adults to perform, indicating its potential as a diagnostic tool. It has also been shown that salivary DHEA can be used as a biomarker for MCI.

Cortical thickness is differently associated with ALDH2 rs671 polymorphism according to level of amyloid deposition.

Accumulating evidence indicates that amyloid-beta (Aß) deposition and biogenic aldehyde accumulation contribute to the pathogenesis of neurodegenerative diseases. Human aldehyde dehydrogenase 2 (ALDH2) metabolizes biogenic aldehydes produced in the brain to prevent damage. However, r671G>A, a single nucleotide polymorphism of ALDH2, causes aldehyde accumulation and decreased ALDH2 activity. We aimed to investigate whether Aß deposition and rs671 polymorphism have an interaction effect on cortical thickness (CTh). We grouped 179 participants in the Biobank Innovations for chronic Cerebrovascular disease With ALZheimer's disease Study as follows: amyloid (-) [A(-)] and amyloid (+) [A(+)] groups based on the Aß deposition degree; A-carrier (AC) and GG (GG) groups based on the presence/absence of the rs671 A allele; and their combinations, i.e., A(-)AC, A(-)GG, A(+)AC, and A(+)GG groups. A multiple regression analysis identified nine regions of interest. Compared with the A(-)GG group, the A(-)AC group showed thinner CTh in all regions. There were no significant differences between the A(+)AC and A(+)GG groups. We observed an interaction effect of amyloid deposition and rs671 polymorphism on CTh. The CTh in the A(-) group appeared to be strongly influenced by rs671 polymorphism, which could have contributed to cortical thinning and biogenic aldehyde accumulation in the AC group. Additionally, CTh in the A(+) group appeared to be strongly influenced by amyloid deposition.

Characteristics of Dimensional Psychopathology in Suicidal Patients With Major Psychiatric Disorders and Its Association With the Length of Hospital Stay: Algorithm Validation Study.

BACKGROUND: Suicide has emerged as a serious concern for public health; however, only few studies have revealed the differences between major psychiatric disorders and suicide. Recent studies have attempted to quantify research domain criteria (RDoC) into numeric scores to systematically use them in computerized methods. The RDoC scores were used to reveal the characteristics of suicide and its association with major psychiatric disorders. OBJECTIVE: We intended to investigate the differences in the dimensional psychopathology among hospitalized suicidal patients and the association between the dimensional psychopathology of psychiatric disorders and length of hospital stay. METHODS: This retrospective study enrolled hospitalized suicidal patients diagnosed with major psychiatric disorders (depression, schizophrenia, and bipolar disorder) between January 2010 and December 2020 at a tertiary hospital in South Korea. The RDoC scores were calculated using the patients' admission notes. To measure the differences between psychiatric disorder cohorts, analysis of variance and the Cochran Q test were conducted and post hoc analysis for RDoC domains was performed with the independent two-sample t test. A linear regression model was used to analyze the association between the RDoC scores and sociodemographic features and comorbidity index. To estimate the association between the RDoC scores and length of hospital stay, multiple logistic regression models were applied to each psychiatric disorder group. RESULTS: We retrieved 732 admissions for 571 patients (465 with depression, 73 with schizophrenia, and 33 with bipolar disorder). We found significant differences in the dimensional psychopathology according to the psychiatric disorders. The patient group with depression showed the highest negative RDoC domain scores. In the cognitive and social RDoC domains, the groups with schizophrenia and bipolar disorder scored higher than the group with depression. In the arousal RDoC domain, the depression and bipolar disorder groups scored higher than the group with schizophrenia. We identified significant associations between the RDoC scores and length of stay for the depression and bipolar disorder groups. The odds ratios (ORs) of the length of stay were increased because of the higher negative RDoC domain scores in the group with depression (OR 1.058, 95% CI 1.006-1.114) and decreased by higher arousal RDoC domain scores in the group with bipolar disorder (OR 0.537, 95% CI 0.285-0.815). CONCLUSIONS: This study showed the association between the dimensional psychopathology of major psychiatric disorders related to suicide and the length of hospital stay and identified differences in the dimensional psychopathology of major psychiatric disorders. This may provide new perspectives for understanding suicidal patients.

Gender differences in the effect of depression and cognitive impairment on risk of falls among community-dwelling older adults.

BACKGROUND: The impact of the interrelationship between depression and cognitive impairment on risk of falls remains unclear. In addition, gender differences should be considered to further understand the relationships between depression, cognitive impairment, and risk of falls. METHODS: Older adults who completed the Living Profiles of Older People Survey in Korea were included. In total, 14,055 participants completed the baseline survey, and 7,150 participants completed a follow-up survey after 3 years. We classified participants into 4 groups based on depression and cognitive impairment. RESULTS: Using generalized estimating equation models, older adults with depression but without cognitive impairment had a higher risk of falls (OR=1.49, 95% CI=1.35-1.63), compared to a reference group with neither depression nor cognitive impairment. However, older adults who had cognitive impairment but not depression had a similar risk of falls (OR=1.03, 95% CI=0.91-1.16) to the reference group. Finally, older adults who had both depression and cognitive impairment had the highest risk of falls (OR=1.95, 95% CI=1.73-2.20) compared to the reference group. In addition, depression seemed to have a bigger effect on the risk of falls in men compared to women (p for interaction < 0.001). LIMITATIONS: These results should be interpreted with caution, considering the use of screening tool for defining depression or cognitive impairment, and lack of information on psychotropic medication use. CONCLUSIONS: Depression alone or depression combined with cognitive impairment was associated with higher risk of falls in older adults. In addition, gender differences in the risk of falls was noted.