Explainable multimodal prediction of treatment-resistance in patients with depression leveraging brain morphometry and natural language processing

Although 20 % of patients with depression receiving treatment do not achieve remission, predicting treatment-resistant depression (TRD) remains challenging. In this study, we aimed to develop an explainable multimodal prediction model for TRD using structured electronic medical record data, brain morphometry, and natural language processing. In total, 247 patients with a new depressive episode were included. TRD-predictive models were developed based on the combination of following parameters: selected tabular dataset features, independent components-map weightings from brain T1-weighted magnetic resonance imaging (MRI), and topic probabilities from clinical notes. All models applied the extreme gradient boosting (XGBoost) algorithm via five-fold cross-validation. The model using all data sources showed the highest area under the receiver operating characteristic of 0.794, followed by models that used combined brain MRI and structured data, brain MRI and clinical notes, clinical notes and structured data, brain MRI only, structured data only, and clinical notes only (0.770, 0.762, 0.728, 0.703, 0.684, and 0.569, respectively). Classifications of TRD were driven by several predictors, such as previous exposure to antidepressants and antihypertensive medications, sensorimotor network, default mode network, and somatic symptoms. Our findings suggest that a combination of clinical data with neuroimaging and natural language processing variables improves the prediction of TRD.

Higher HbA1c Is Associated With Greater 2-Year Progression of White Matter Hyperintensities

White matter hyperintensity (WMH) lesions on brain MRI images are surrogate markers of cerebral small vessel disease. Longitudinal studies examining the association between diabetes and WMH progression have yielded mixed results. Thus, in this study, we investigated the association between HbA1c, a biomarker for the presence and severity of hyperglycemia, and longitudinal WMH change after adjusting for known risk factors for WMH progression. We recruited 64 participants from South Korean memory clinics to undergo brain MRI at the baseline and a 2-year follow-up. We found the following. First, higher HbA1c was associated with greater global WMH volume (WMHV) changes after adjusting for known risk factors (β = 7.7 × 10-4; P = 0.025). Second, the association between baseline WMHV and WMHV progression was only significant at diabetic levels of HbA1c (P < 0.05, when HbA1c >6.51%), and non-apolipoprotein E (APOE) ε4 carriers had a stronger association between HbA1c and WMHV progression (β = -2.59 × 10-3; P = 0.004). Third, associations of WMHV progression with HbA1c were particularly apparent for deep WMHV change (β = 7.17 × 10-4; P < 0.01) compared with periventricular WMHV change and, for frontal (β = 5.00 × 10-4; P < 0.001) and parietal (β = 1.53 × 10-4; P < 0.05) lobes, WMHV change compared with occipital and temporal WMHV change. In conclusion, higher HbA1c levels were associated with greater 2-year WMHV progression, especially in non-APOE ε4 participants or those with diabetic levels of HbA1c. These findings demonstrate that diabetes may potentially exacerbate cerebrovascular and white matter disease.

ARTICLE HIGHLIGHTS

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 (β=-0.968, 95% confidence interval [CI]=-1.197 to -0.739, p<0.001 for GSEP jobs, β=-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.

Comparative estimation of the effects of antihypertensive medications on schizophrenia occurrence: a multinational observational cohort study

BACKGROUND

The association between antihypertensive medication and schizophrenia has received increasing attention; however, evidence of the impact of antihypertensive medication on subsequent schizophrenia based on large-scale observational studies is limited. We aimed to compare the schizophrenia risk in large claims-based US and Korea cohort of patients with hypertension using angiotensin-converting enzyme (ACE) inhibitors versus those using angiotensin receptor blockers (ARBs) or thiazide diuretics.

METHODS

Adults aged 18 years who were newly diagnosed with hypertension and received ACE inhibitors, ARBs, or thiazide diuretics as first-line antihypertensive medications were included. The study population was sub-grouped based on age (> 45 years). The comparison groups were matched using a large-scale propensity score (PS)-matching algorithm. The primary endpoint was incidence of schizophrenia.

RESULTS

5,907,522; 2,923,423; and 1,971,549 patients used ACE inhibitors, ARBs, and thiazide diuretics, respectively. After PS matching, the risk of schizophrenia was not significantly different among the groups (ACE inhibitor vs. ARB: summary hazard ratio [HR] 1.15 [95% confidence interval, CI, 0.99-1.33]; ACE inhibitor vs. thiazide diuretics: summary HR 0.91 [95% CI, 0.78-1.07]). In the older subgroup, there was no significant difference between ACE inhibitors and thiazide diuretics (summary HR, 0.91 [95% CI, 0.71-1.16]). The risk for schizophrenia was significantly higher in the ACE inhibitor group than in the ARB group (summary HR, 1.23 [95% CI, 1.05-1.43]).

CONCLUSIONS

The risk of schizophrenia was not significantly different between the ACE inhibitor vs. ARB and ACE inhibitor vs. thiazide diuretic groups. Further investigations are needed to determine the risk of schizophrenia associated with antihypertensive drugs, especially in people aged > 45 years.

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.

Impact of symptomatic menopausal transition on the occurrence of depression, anxiety, and sleep disorders: A real-world multi-site study

BACKGROUND

The menopause transition is a vulnerable period that can be associated with changes in mood and cognition. The present study aimed to investigate whether a symptomatic menopausal transition increases the risks of depression, anxiety, and sleep disorders.

METHODS

This population-based, retrospective cohort study analysed data from five electronic health record databases in South Korea. Women aged 45-64 years with and without symptomatic menopausal transition were matched 1:1 using propensity-score matching. Subgroup analyses were conducted according to age and use of hormone replacement therapy (HRT). A primary analysis of 5-year follow-up data was conducted, and an intention-to-treat analysis was performed to identify different risk windows over 5 or 10 years. The primary outcome was first-time diagnosis of depression, anxiety, and sleep disorder. We used Cox proportional hazard models and a meta-analysis to calculate the summary hazard ratio (HR) estimates across the databases.

RESULTS

Propensity-score matching resulted in a sample of 17,098 women. Summary HRs for depression (2.10; 95% confidence interval [CI] 1.63-2.71), anxiety (1.64; 95% CI 1.01-2.66), and sleep disorders (1.47; 95% CI 1.16-1.88) were higher in the symptomatic menopausal transition group. In the subgroup analysis, the use of HRT was associated with an increased risk of depression (2.21; 95% CI 1.07-4.55) and sleep disorders (2.51; 95% CI 1.25-5.04) when compared with non-use of HRT.

CONCLUSIONS

Our findings suggest that women with symptomatic menopausal transition exhibit an increased risk of developing depression, anxiety, and sleep disorders. Therefore, women experiencing a symptomatic menopausal transition should be monitored closely so that interventions can be applied early.

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.

Privacy-Preserving Federated Model Predicting Bipolar Transition in Patients With Depression: Prediction Model Development Study

BACKGROUND

Mood disorder has emerged as a serious concern for public health; in particular, bipolar disorder has a less favorable prognosis than depression. Although prompt recognition of depression conversion to bipolar disorder is needed, early prediction is challenging due to overlapping symptoms. Recently, there have been attempts to develop a prediction model by using federated learning. Federated learning in medical fields is a method for training multi-institutional machine learning models without patient-level data sharing.

OBJECTIVE

This study aims to develop and validate a federated, differentially private multi-institutional bipolar transition prediction model.

METHODS

This retrospective study enrolled patients diagnosed with the first depressive episode at 5 tertiary hospitals in South Korea. We developed models for predicting bipolar transition by using data from 17,631 patients in 4 institutions. Further, we used data from 4541 patients for external validation from 1 institution. We created standardized pipelines to extract large-scale clinical features from the 4 institutions without any code modification. Moreover, we performed feature selection in a federated environment for computational efficiency and applied differential privacy to gradient updates. Finally, we compared the federated and the 4 local models developed with each hospital's data on internal and external validation data sets.

RESULTS

In the internal data set, 279 out of 17,631 patients showed bipolar disorder transition. In the external data set, 39 out of 4541 patients showed bipolar disorder transition. The average performance of the federated model in the internal test (area under the curve [AUC] 0.726) and external validation (AUC 0.719) data sets was higher than that of the other locally developed models (AUC 0.642-0.707 and AUC 0.642-0.699, respectively). In the federated model, classifications were driven by several predictors such as the Charlson index (low scores were associated with bipolar transition, which may be due to younger age), severe depression, anxiolytics, young age, and visiting months (the bipolar transition was associated with seasonality, especially during the spring and summer months).

CONCLUSIONS

We developed and validated a differentially private federated model by using distributed multi-institutional psychiatric data with standardized pipelines in a real-world environment. The federated model performed better than models using local data only.

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.

Association between impaired glucose metabolism and long-term prognosis at the time of diagnosis of depression: Impaired glucose metabolism as a promising biomarker proposed through a machine-learning approach

BACKGROUND

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.

METHODS

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.

RESULTS

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.

CONCLUSIONS

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.

Transferability of Alzheimer Disease Polygenic Risk Score Across Populations and Its Association With Alzheimer Disease-Related Phenotypes

IMPORTANCE

Polygenic risk scores (PRSs), which aggregate the genetic effects of single-nucleotide variants identified in genome-wide association studies (GWASs), can help distinguish individuals at a high genetic risk for Alzheimer disease (AD). However, genetic studies have predominantly focused on populations of European ancestry.

OBJECTIVE

To evaluate the transferability of a PRS for AD in the Korean population using summary statistics from a prior GWAS of European populations.

DESIGN, SETTING, AND PARTICIPANTS

This cohort study developed a PRS based on the summary statistics of a large-scale GWAS of a European population (the International Genomics of Alzheimer Project; 21 982 AD cases and 41 944 controls). This PRS was tested for an association with AD dementia and its related phenotypes in 1634 Korean individuals, who were recruited from 2013 to 2019. The association of a PRS based on a GWAS of a Japanese population (the National Center for Geriatrics and Gerontology; 3962 AD cases and 4074 controls) and a transancestry meta-analysis of European and Japanese GWASs was also evaluated. Data were analyzed from December 2020 to June 2021.

MAIN OUTCOMES AND MEASURES

Risk of AD dementia, amnestic mild cognitive impairment (aMCI), earlier symptom onset, and amyloid β deposition (Aβ).

RESULTS

A total of 1634 Korean patients (969 women [59.3%]), including 716 individuals (43.6%) with AD dementia, 222 (13.6%) with aMCI, and 699 (42.8%) cognitively unimpaired controls, were analyzed in this study. The mean (SD) age of the participants was 71.6 (9.0) years. Higher PRS was associated with a higher risk of AD dementia independent of APOE ɛ4 status in the Korean population (OR, 1.95; 95% CI, 1.40-2.72; P < .001). Furthermore, PRS was associated with aMCI, earlier symptom onset, and Aβ deposition independent of APOE ɛ4 status. The PRS based on a transancestry meta-analysis of data sets comprising 2 distinct ancestries showed a slightly improved accuracy.

CONCLUSIONS AND RELEVANCE

In this cohort study, a PRS derived from a European GWAS identified individuals at a high risk for AD dementia in the Korean population. These findings emphasize the transancestry transferability and clinical value of PRSs and suggest the importance of enriching diversity in genetic studies of AD.

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.

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 effect of sex and physical frailty on incident disability after 2 years among community-dwelling older adults: KFACS study

Background

This study investigated the impact of physical frailty on the development of disabilities in mobility, activities of daily living (ADL), and instrumental activities of daily living (IADL) according to sex among community-dwelling Korean older adults.

Methods

We used data of 2,905 older adults aged 70-84 years from the Korean Frailty and Aging Cohort Study (KFACS) at baseline (2016-2017) and Wave 2 (2018-2019). Fried’s physical frailty phenotype was used to identify frailty.

Results

After adjustment, frailty showed a higher impact for women than men on developing mobility disability (odds ratio [OR]=14.00, 95% confidence interval [CI]=4.8–40.78 vs. OR=9.89, 95% CI=4.28–22.86) and IADL disability after two years (OR=7.22, 95% CI=2.67–19.56 vs. OR=3.19, 95% CI=1.17–8.70). Pre-frailty led to mobility disability for women and men (OR=2.77, 95% CI=1.93–3.98 vs. OR=2.49, 95% CI=1.66–3.72, respectively), and IADL disability only for women (OR=3.01, 95% CI=1.28–7.09). Among the IADL components, both men and women who were prefrail or frail showed increased disability in ‘using transportation’. Among men, pre-frailty was significantly associated with disability in “going out” and “shopping”. In women, frailty was significantly associated with disability in “doing laundry,” “performing household chores,” “shopping,” and “managing money”.

Conclusions

Physical frailty increased disability over 2 years for women more than men. Physical frailty increased disability in outdoor activity-related IADL components in men and household work-related IADL components in women. This study highlights the need for gender-specific policies and preventative programs for frailty, particularly restorative interventions that focus on women who are physically frail.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12877-022-03263-5.

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.

Impact of non-alcoholic fatty liver disease on the risk of sarcopenia: a nationwide multicenter prospective study

BACKGROUND AND AIMS

Despite the association between sarcopenia and non-alcoholic fatty liver disease (NAFLD), no study has evaluated the predictive role of NAFLD in sarcopenia. We investigated impact of NAFLD on the risk of low muscle mass (LMM) and low muscle strength (LMS) in a nationwide multicenter study.

METHODS

A total of 1595 community-dwelling people aged 70-84 years were followed for 2 years in the Korean Frailty and Aging Cohort Study. Muscle mass was estimated by dividing appendicular skeletal muscle mass (ASM) by body mass index (BMI). Muscle strength was measured as handgrip strength (HGS) divided by BMI. The sex-specific lowest quintiles of ASM/BMI and HGS/BMI of the study population were used as cutoffs for LMM and LMS, respectively. The risk of LMM and LMS were assessed according to hepatic steatosis index (HSI) and fatty liver index (FLI) quartiles.

RESULTS

As HSI quartiles increased, the LMM risk increased gradually, after adjusting for age, sex, lifestyle factors, comorbidities, and several causative factors (insulin resistance, inflammation, and vitamin D) (Q4 vs. Q1 OR [95% CI] 3.46 [2.23-5.35]). The increased risk of LMS was even higher according to HSI quartiles (Q4 vs. Q1 5.81 [3.67-9.21]). Multivariate analyses based on FLI showed similar results. People with NAFLD (HSI > 36) were at higher risk of developing LMM and LMS compared to those without (1.65 [1.19-2.31] and 2.29 [1.61-3.26], respectively).

CONCLUSIONS

The presence of NAFLD may predict future risk of LMM and LMS, with greater impact on LMS than on LMM.

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.

Network modeling of anxiety and psychological characteristics on suicidal behavior: Cross-sectional study

BACKGROUND

Suicide is influenced by complex interactions among various psychopathological features. We aimed to examine the relationship between suicide risk and psychological risk factors such as defense mechanisms, personality, and anxiety.

METHODS

We established a psychiatric database by utilizing the Observational Medical Outcomes Partnership Common Data Model. We conducted a 1:1 propensity score matching with age, sex, and depression severity, and identified a sample (n = 258) with two groups: those with suicidal behavior and those with non-suicidal behavior. Using principal component analysis, we extracted nine psychological scales and applied network analysis to compare relationships among psychological factors between the two groups.

RESULTS

Patients with non-suicidal behaviors showed associations between trait anxiety and defense mechanisms, while those with suicidal behaviors did not. For patients with suicidal ideation there was an association between somatization and trait anxiety. Patients with suicide attempts showed associations between paranoia and dissociation connected to trait anxiety.

LIMITATIONS

Longitudinal research is required to fully observe transitions from suicidal ideation to attempts and recurrent suicidal events. In addition, these networks may not generalize suicidal behaviors because the group participants are not homogeneous. Lastly, data from self-report questionnaires limits the reliability of responses.

CONCLUSIONS

We presented important new insights on suicidal behavior by estimating psychological networks. Patients with non-suicidal behavior may exhibit discrete relationships between defense mechanisms and anxiety, compared to those with suicidal behavior. Patients with suicidal ideation and suicide attempts may show distinct associations between anxiety and psychopathological features.

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.

Shared genetic effects of emotion and subcortical volumes in healthy adults

Ample studies have reported a strong association between emotion and subcortical volumes; still, the underlying mechanism regarding this relation remains unclear. Using a twin design, the current study aimed to explore the intrinsic association between emotion and subcortical volumes by examining their phenotypic, genetic, and environmental correlations. We used a group dataset of 960 individuals from the Human Connectome Project (234 monozygotic twins, 145 dizygotic twins, 581 not twins, males = 454, age = 22-37 years). We found that both emotion and subcortical volumes were heritable. Of the 17 emotional traits, 13 were significantly phenotypically correlated with the volumes of multiple subcortical regions. There was no environmental correlation between emotion and subcortical volumes; however, we found a genetic overlap between overall emotional traits and caudate volume. Taken together, our results showed that emotion and subcortical volumes were heritable and closely related. Although the caudate has been often studied with execution of movement, given that the caudate volume is genetically associated with diverse emotional domains, such as negative affect, psychological well-being, and social relationships, it may suggest that the caudate volume might also be an important factor when studying the brain basis of emotion.

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.

Psychosis Relapse Prediction Leveraging Electronic Health Records Data and Natural Language Processing Enrichment Methods

BACKGROUND

Identifying patients at a high risk of psychosis relapse is crucial for early interventions. A relevant psychiatric clinical context is often recorded in clinical notes; however, the utilization of unstructured data remains limited. This study aimed to develop psychosis-relapse prediction models using various types of clinical notes and structured data.

METHODS

Clinical data were extracted from the electronic health records of the Ajou University Medical Center in South Korea. The study population included patients with psychotic disorders, and outcome was psychosis relapse within 1 year. Using only structured data, we developed an initial prediction model, then three natural language processing (NLP)-enriched models using three types of clinical notes (psychological tests, admission notes, and initial nursing assessment) and one complete model. Latent Dirichlet Allocation was used to cluster the clinical context into similar topics. All models applied the least absolute shrinkage and selection operator logistic regression algorithm. We also performed an external validation using another hospital database.

RESULTS

A total of 330 patients were included, and 62 (18.8%) experienced psychosis relapse. Six predictors were used in the initial model and 10 additional topics from Latent Dirichlet Allocation processing were added in the enriched models. The model derived from all notes showed the highest value of the area under the receiver operating characteristic (AUROC = 0.946) in the internal validation, followed by models based on the psychological test notes, admission notes, initial nursing assessments, and structured data only (0.902, 0.855, 0.798, and 0.784, respectively). The external validation was performed using only the initial nursing assessment note, and the AUROC was 0.616.

CONCLUSIONS

We developed prediction models for psychosis relapse using the NLP-enrichment method. Models using clinical notes were more effective than models using only structured data, suggesting the importance of unstructured data in psychosis prediction.

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.

Predictors of diagnostic transition from major depressive disorder to bipolar disorder: a retrospective observational network study

Many patients with bipolar disorder (BD) are initially misdiagnosed with major depressive disorder (MDD) and are treated with antidepressants, whose potential iatrogenic effects are widely discussed. It is unknown whether MDD is a comorbidity of BD or its earlier stage, and no consensus exists on individual conversion predictors, delaying BD's timely recognition and treatment. We aimed to build a predictive model of MDD to BD conversion and to validate it across a multi-national network of patient databases using the standardization afforded by the Observational Medical Outcomes Partnership (OMOP) common data model. Five "training" US databases were retrospectively analyzed: IBM MarketScan CCAE, MDCR, MDCD, Optum EHR, and Optum Claims. Cyclops regularized logistic regression models were developed on one-year MDD-BD conversion with all standard covariates from the HADES PatientLevelPrediction package. Time-to-conversion Kaplan-Meier analysis was performed up to a decade after MDD, stratified by model-estimated risk. External validation of the final prediction model was performed across 9 patient record databases within the Observational Health Data Sciences and Informatics (OHDSI) network internationally. The model's area under the curve (AUC) varied 0.633-0.745 (µ = 0.689) across the five US training databases. Nine variables predicted one-year MDD-BD transition. Factors that increased risk were: younger age, severe depression, psychosis, anxiety, substance misuse, self-harm thoughts/actions, and prior mental disorder. AUCs of the validation datasets ranged 0.570-0.785 (µ = 0.664). An assessment algorithm was built for MDD to BD conversion that allows distinguishing as much as 100-fold risk differences among patients and validates well across multiple international data sources.

Development of Guidelines on the Primary Prevention of Frailty in Community-Dwelling Older Adults

Background

Despite the increasing number of older adults as the population ages, there is a lack of frailty prevention guidelines for community-dwelling older adults. The Korean Frailty and Aging Cohort Study conducted systematic review on contributors to frailty and developed guidelines on the primary prevention of frailty in community-dwelling older adults.

Methods

This study updated a previous systematic review of contributors to frailty by adding the most recent articles. Based on this updated systematic review, experts in geriatrics and gerontology developed guidelines for preventing frailty using the Delphi method.

Results

These guidelines categorized the recommendations into physical activity, resilience, oral health, management of non-communicable diseases, involvement in society, smoking cessation, and eating various kinds of food.

Conclusion

Unlike previous frailty-related guidelines, this study developed evidence-based frailty prevention guidelines based on a systematic review. The guidelines are expected to contribute to the healthy aging of community-dwelling older adults by the primary prevention of frailty.

A 9-Year Comparison of Dementia Prevalence in Korea: Results of NaSDEK 2008 and 2017

BACKGROUND

In many high-income Western countries, the prevalence of dementia had been reduced over the past decades.

OBJECTIVE

We investigated whether the prevalence of all-cause dementia, Alzheimer's disease, vascular dementia, and mild cognitive impairment (MCI) had changed in Korea from 2008 to 2017.

METHODS

Nationwide Survey on Dementia Epidemiology of Korea (NaSDEK) in 2008 and 2017 was conducted on representative elderly populations that were randomly sampled across South Korea. Both surveys employed a two-stage design (screening and diagnostic phases) and diagnosed dementia and MCI according to the fourth edition of the Diagnostic and Statistical Manual of Mental Disorders and the consensus criteria from the International Working Group, respectively. The numbers of participants aged 65 years or older in the screening and diagnostic phases were 6,141 and 1,673 in the NaSDEK 2008 and 2,972 and 474 in the NaSDEK 2017, respectively.

RESULTS

The age- and sex-standardized prevalence of all-cause dementia and Alzheimer's disease showed nonsignificant decrease (12.3% to 9.8%, odds ratio [OR] = 0.89, 95% confidence interval [CI] = 0.54-1.48 for all-cause dementia; 7.6% to 6.8%, OR [95% CI] = 0.91 [0.58-1.42] for Alzheimer's disease). Vascular dementia decreased in the young-old population aged less than 75 years (2.7% to 0.001%, OR [95% CI] = 0.04 [0.01-0.15]) and in women (1.9% to 0.5%, OR [95% CI] = 0.27 [0.10-0.72]) while MCI remained stable (25.3% to 26.2%, OR [95% CI] = 1.08 [0.67-1.73]).

CONCLUSION

We found that the prevalence of dementia in Korea showed a nonsignificant decrease between 2008 and 2017.

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.

Rapid Symptom Improvement in Major Depressive Disorder Using Accelerated Repetitive Transcranial Magnetic Stimulation

Objective

Repetitive transcranial magnetic stimulation (rTMS) has contributed to increase in the remission rate for patients with major depressive disorder (MDD). However, current rTMS treatment is practically inconvenient because it requires daily treatment sessions for several weeks. Accelerated rTMS treatment is as efficient and safe for MDD patients as conventional rTMS.

Methods

Fifty-one patients with MDD participated in this study; they were randomized into accelerated rTMS (n = 21), conventional rTMS (n = 22), and sham-treatment (n = 8) groups. The accelerated and conventional rTMS groups received 15 sessions for 3 days and 3 weeks, respectively. The sham-treatment group received 15 sham rTMS sessions for 3 days. Primary outcome was assessed using self-report and clinician-rated Korean Quick Inventory of Depressive Symptomatology (KQIDS-SR and KQIDS-C, respectively). Adverse effects were monitored using the Frequency, Intensity, and Burden of Side Effects Rating scale. Changes in depressive symptoms were compared among the three groups using mixed model analyses.

Results

For the KQIDS-SR score, there was a significant main effect of "time" (F_3,47 = 11.05, p < 0.001), but no effect of "group" (F_2,47 = 2.04, p = 0.142), and a trend-level interaction effect of "group × time" (F_6,47 = 2.26, p = 0.053). Improvement in depressive symptoms, based on the KQIDS-SR score 3 weeks after treatment, was more prominent in the accelerated rTMS group than in the sham-treatment group (p = 0.011). Tolerability was comparable among the three groups.

Conclusion

The accelerated rTMS treatment group showed rapid improvement of depressive symptoms compared with the sham-treatment and conventional rTMS treatment groups. Therefore, accelerated rTMS treatment could be a viable option for MDD, with improved accessibility.

Dementia key gene identification with multi-layered SNP-gene-disease network

MOTIVATION

Recently, various approaches for diagnosing and treating dementia have received significant attention, especially in identifying key genes that are crucial for dementia. If the mutations of such key genes could be tracked, it would be possible to predict the time of onset of dementia and significantly aid in developing drugs to treat dementia. However, gene finding involves tremendous cost, time and effort. To alleviate these problems, research on utilizing computational biology to decrease the search space of candidate genes is actively conducted.

In this study, we propose a framework in which diseases, genes and single-nucleotide polymorphisms are represented by a layered network, and key genes are predicted by a machine learning algorithm. The algorithm utilizes a network-based semi-supervised learning model that can be applied to layered data structures.

RESULTS

The proposed method was applied to a dataset extracted from public databases related to diseases and genes with data collected from 186 patients. A portion of key genes obtained using the proposed method was verified in silico through PubMed literature, and the remaining genes were left as possible candidate genes.

AVAILABILITY AND IMPLEMENTATION

The code for the framework will be available at http://www.alphaminers.net/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Risk of Mortality in Elderly Coronavirus Disease 2019 Patients With Mental Health Disorders: A Nationwide Retrospective Study in South Korea

OBJECTIVE

This study aimed to investigate the different clinical characteristics among elderly coronavirus disease 2019 (COVID-19) patients with and without mental disorders in South Korea and determine if these characteristics have an association with underlying mental disorders causing mortality.

METHOD

A population-based comparative cohort study was conducted using the national claims database. Individuals aged ≥65 years with confirmed COVID-19 between January 1, 2020 and April 10, 2020 were assessed. The endpoints for evaluating mortality for all participants were death, 21 days after diagnosis, or April 10, 2020. The risk of mortality associated with mental disorders was estimated using Cox hazards regression.

RESULTS

We identified 814 elderly COVID-19 patients (255 [31.3%] with mental disorder and 559 [68.7%] with nonmental disorder). Individuals with mental disorders were found more likely to be older, taking antithrombotic agents, and had diabetes, hypertension, chronic obstructive lung disease, and urinary tract infections than those without mental disorders. After propensity score stratification, our study included 781 patients in each group (236 [30.2%] with mental disorder and 545 [69.8%] with nonmental disorder). The mental disorder group showed higher mortality rates than the nonmental disorder group (12.7% [30/236] versus 6.8% [37/545]). However, compared to patients without mental disorders, the hazard ratio (HR) for mortality in elderly COVID-19 patients with mental disorders was not statistically significant (HR: 1.57, 95%CI: 0.95-2.56).

CONCLUSION

Although the association between mental disorders in elderly individuals and mortality in COVID-19 is unclear, this study suggests that elderly patients with comorbid conditions and those taking psychiatric medications might be at a higher risk of COVID-19.

Effects of transcranial direct current stimulation using miniaturized devices vs sertraline for depression in Korea: A 6 week, multicenter, randomized, double blind, active-controlled study

We compared the efficacy and safety of transcranial direct current stimulation (tDCS) vs. Sertraline in the treatment of Major Depressive Disorder (MDD) in South Korean participants. This was a multi-center, double blind, active controlled study with non-inferiority testing. Patients were randomly assigned to receive tDCS (n = 45) or Sertraline (n = 47). tDCS was administered in 30-min, 2 mA prefrontal stimulation sessions for 10 consecutive weekdays, followed by 2 treatments at 4 and 6 weeks. Sertraline was administered at a dose of 50 mg per day for 6 weeks. The primary outcome measure was a change in the Montgomery-Asberg Depression Rating Scale (MADRS) score at six weeks. Mean MADRS scores decreased by 14.58 ± 8.51 points in the tDCS group and 12.32 ± 8.56 points in the Sertraline group. There was no significant main effect of group (p = 0.5877) or time by group interaction across weeks 0, 3, and 6 (p = 0.1539). Noninferiority of tDCS compared with Sertraline was not demonstrated. The mean difference between the Sertraline and tDCS group was -2.258 (95% confidence interval [CI], -5.795 to 1.27811), and the lower boundary of the CI was lower than the prespecified noninferiority margin of -3.56. There were no significant group differences in the rate of adverse events. In the present study, the noninferiority of tDCS to Sertraline for the treatment of depression was not found in this Korean population.

Associations of rest-activity patterns with amyloid burden, medial temporal lobe atrophy, and cognitive impairment

BACKGROUND

We sought to investigate the possible associations of rest-activity patterns with cortical amyloid burden, medial temporal lobe (MTL) neurodegeneration, and cognitive function in patients in the early stage of cognitive impairment.

METHODS

Rest-activity patterns were assessed in 100 participants (70 with mild cognitive impairment and 30 with mild dementia) using wrist actigraphy. All participants underwent 18F-flutemetamol positron emission tomography (PET) imaging to quantify cortical amyloid burden, structural brain magnetic resonance imaging (MRI) to quantify MTL grey matter volume, neuropsychological testing, and clinical diagnosis. We used multiple linear regression models adjusted for covariates, including demographics, diabetes, hypertension, depressive symptom, psychotropic medication, sleep medication, weekend effect, and apolipoprotein-ε allele status.

FINDINGS

After adjusting for possible confounders, we found that the midline estimation of statistic of rhythm (MESOR) associated positively with frontal/executive function (estimate = 1.17, standard error [SE] = 0.37, p = 0.002). The least active 5-h (L5) onset time associated positively with MTL grey matter volume and memory function (estimate = 1.24, SE = 0.33, p = 0.001, and estimate = 3.77, SE = 1.22, p = 0.003, respectively), particularly in amyloid-negative participants. Additional path analysis revealed that MTL grey matter volume partially mediated the association between L5 onset time and memory function in amyloid-negative participants.

INTERPRETATION

Decreased MESOR and advanced L5 onset time may be useful as early signs of cognitive decline or MTL neurodegeneration. Furthermore, amyloid pathology may act as a moderator of the relationships between rest-activity patterns, neurodegeneration, and cognitive function.

FUNDING

Korea Centres for Disease Control and Prevention (#4845-303); National Research Foundation of Korea (2019M3C7A1031905, 2019R1A5A2026045).

Deep Learning Approach for Imputation of Missing Values in Actigraphy Data: Algorithm Development Study

Background

Data collected by an actigraphy device worn on the wrist or waist can provide objective measurements for studies related to physical activity; however, some data may contain intervals where values are missing. In previous studies, statistical methods have been applied to impute missing values on the basis of statistical assumptions. Deep learning algorithms, however, can learn features from the data without any such assumptions and may outperform previous approaches in imputation tasks.

Objective

The aim of this study was to impute missing values in data using a deep learning approach.

Methods

To develop an imputation model for missing values in accelerometer-based actigraphy data, a denoising convolutional autoencoder was adopted. We trained and tested our deep learning–based imputation model with the National Health and Nutrition Examination Survey data set and validated it with the external Korea National Health and Nutrition Examination Survey and the Korean Chronic Cerebrovascular Disease Oriented Biobank data sets which consist of daily records measuring activity counts. The partial root mean square error and partial mean absolute error of the imputed intervals (partial RMSE and partial MAE, respectively) were calculated using our deep learning–based imputation model (zero-inflated denoising convolutional autoencoder) as well as using other approaches (mean imputation, zero-inflated Poisson regression, and Bayesian regression).

Results

The zero-inflated denoising convolutional autoencoder exhibited a partial RMSE of 839.3 counts and partial MAE of 431.1 counts, whereas mean imputation achieved a partial RMSE of 1053.2 counts and partial MAE of 545.4 counts, the zero-inflated Poisson regression model achieved a partial RMSE of 1255.6 counts and partial MAE of 508.6 counts, and Bayesian regression achieved a partial RMSE of 924.5 counts and partial MAE of 605.8 counts.

Conclusions

Our deep learning–based imputation model performed better than the other methods when imputing missing values in actigraphy data.

Downregulated Platelet miR-1233-5p in Patients with Alzheimer's Pathologic Change with Mild Cognitive Impairment is Associated with Aβ-Induced Platelet Activation via P-Selectin

MicroRNAs (miRNAs) have been proposed as a promising biomarker for various diseases including Alzheimer’s disease (AD). More attention has recently been focused on the diagnosis and treatment at earlier stage of mild cognitive impairment (MCI) for preventing its progression to AD. To identify potential pathologic markers for Aβ(+)MCI (Alzheimer’s pathologic change with MCI), we investigated miRNA expression profiles in the platelets from patients with Aβ(+)MCI, in comparison with those from Aβ(−)MCI (Non-Alzheimer’s pathologic change with MCI) and CNI (cognitively normal individuals). We found that let-7i-5p, miR-125a, miR-1233-5p, and miR-6787-5p were significantly downregulated, while miR-6880-5p expression was upregulated. Of these, only miR-1233-5p was significantly downregulated by Aβ treatment in both human platelets and their precursor megakaryocytes (MEG-01 cells). We explored the role of miRNAs by using miRNA mimics or inhibitors, and found that the diminished level of miR-1233-5p was associated with Aβ-induced increase in the expression of P-selectin and cell adhesion to fibronectin. Our results further indicated that Aβ-induced increase in platelet/MEG adhesion to fibronectin is likely mediated via P-selectin. In conclusion, this study suggests the downregulation of platelet-derived miR-1233-5p as a pathologic marker for Aβ(+)MCI.

Brief Screening for Four Mental Illnesses of the Elderly in Community Mental Health Services: the BS4MI-Elderly

OBJECTIVE

Early detection and proper management of mental illness can help to prevent severe deterioration. However, with limited financial and human resources of community mental health services, it is not practical to carry out all conventional screening tools simultaneously. In this study, we aimed to develop and validate a brief but comprehensive screening questionnaire for four common mental illnesses of the elderly.

METHODS

The brief screening for four mental illnesses of elderly (BS4MI-elderly) is a 14-item binary response questionnaire that covers dementia, depressive disorder, sleep disorder, and hwa-byung. To test validity, we compared conventional scale scores for three groups of participants classified using the BS4MI-elderly. The sensitivity, specificity, predictive value of positive test, likelihood ratio of positive test and internal consistency of the BS4MI-elderly were assessed. Finally, a correlation analysis between the BS4MI-elderly and general mental health scales was conducted.

RESULTS

A total of 254 participants aged over 65 years were recruited. The BS4MI-elderly showed moderate to high sensitivity for the test that distinguishes the normal group from the risk and disorder groups (dementia: 0.61, depressive disorder: 0.88, sleep disorder: 0.85, hwa-byung: 0.94) and high specificity for the test that distinguishes the disorder group from the normal and risk groups (dementia: 0.91, depressive disorder: 0.93, hwa-byung: 0.84, sleep disorder: 0.84). The BS4MI-elderly also exhibited good internal consistency and significant correlations with general mental health scales.

CONCLUSION

The BS4MI-elderly, a brief but comprehensive screening tool, could be a useful instrument for screening the elderly in community mental health services.

Korean frailty and aging cohort study (KFACS): cohort profile

PURPOSE

The purpose of the Korean Frailty and Aging Cohort Study (KFACS) is to initiate a nationwide, population-based prospective cohort study of older adults living in the community to assess their frailty status and explore transitions between frailty states over time in Korea.

PARTICIPANTS

The KFACS is a multicentre longitudinal study with the baseline survey conducted from May 2016 to November 2017. Each centre recruited participants using quota sampling stratified by age and sex. The number of participants recruited through 2 years of baseline study from 10 centres was 3014, with each site accounting for approximately 300 participants. The inclusion criteria were: having an age of 70-84 years, currently living in the community, having no plans to move out in the next 2 years, having no problems with communication and no prior dementia diagnosis.

FINDINGS TO DATE

To define physical frailty, the KFACS used a modified version of the Fried Frailty Phenotype (FFP) consisting of five components of frailty: unintended weight loss, weakness, self-reported exhaustion, slowness and low physical activity. In the baseline study of 2016-2017, 2907 of 3014 individuals fulfilled all five components of FFP. The results indicated that 7.8% of the participants (n=228) were frail, 47.0% (n=1366) were prefrail and 45.2% (n=1313) were robust. The prevalence of frailty increased with age in both sexes; in the group aged 70-74 years, 1.8% of men and 3.7% of women were frail, whereas in the 80-84 years age group, 14.9% of men and 16.7% of women were frail. Women tended to exhibit a higher prevalence of frailty than men in all age groups.

FUTURE PLANS

The KFACS plans to identify outcomes and risk factors associated with frailty by conducting a 10-year cohort study, with a follow-up every 2 years, using 3014 baseline participants.

A 12-week multidomain intervention for late-life depression: a community-based randomized controlled trial

BACKGROUND

Given that current unimodal strategies for treating late-life depression are insufficient, the awareness of the necessity and importance of multidomain intervention has increased. We assessed the efficacy of multidomain intervention in reducing symptoms of late-life depression.

METHODS

This was a 12-week community-based randomized controlled trial in 78 older adults diagnosed with major depressive disorder. Participants were randomly assigned to the multidomain intervention or supportive therapy group. We provided four home visits and 12 telephone calls over 12 weeks. Four therapeutic approaches (physical activity, healthy diet, social activity, and brief cognitive restructuring) were incorporated into the multidomain intervention. The primary outcome was the change in depressive symptoms, as measured by the Montgomery-Asberg Depression Rating Scale (MADRS). Secondarily, we investigated changes in resting-state functional connectivity.

RESULTS

The MADRS total score was reduced more in the multidomain intervention group than in the supportive therapy group during the 12 weeks (intervention × time interaction, P = =0.007). After correction for multiple comparisons, the multidomain intervention group exhibited a lower MADRS total score at week 12 (score difference 5.117; P = =0.029). At follow-up, the multidomain intervention group also exhibited less functional connectivity between the posterior cingulate cortex and left inferior parietal lobule within the default mode network (FDR < 0.1).

LIMITATIONS

Caution is needed in the interpretation of the results, considering the small sample size and high percentage of female participants.

CONCLUSIONS

A 12-week multidomain intervention resulted in a greater reduction of depressive symptoms among the elderly with major depressive disorder than their counterparts who received supportive therapy.

Association between use of benzodiazepines and occurrence of acute angle-closure glaucoma in the elderly: A population-based study

OBJECTIVE

Because benzodiazepines (BZDs) can affect pupillae muscles, their use could be a risk factor for acute angle-closure glaucoma (AACG), which is an ophthalmic emergency. However, there is no research evidence for the association between BZDs and AACG, except two case reports. We aimed to investigate whether BZDs increase the risk of AACG in a geriatric population.

METHODS

We performed a case-control study using a geriatric cohort from the National Health Insurance database (2002-2013) in Korea. Case subjects (n = 1117) were patients diagnosed with AACG. Controls, people who have not been diagnosed with AACG, were randomly matched with the case according to age, sex, and index year (n = 4468). To examine the risk of BZD use for AACG, we performed conditional logistic regression analyses with potential confounders including comorbidities and concomitant medication.

RESULTS

The use of BZD within 30 days was not significantly associated with AACG risk (adjusted odds ratio [aOR] = 1.14, 95% confidence interval [CI] = 0.94-1.37). Further analyses showed that, compared with non-use of BZD, new BZD use had a significantly increased risk for the development of AACG (aOR = 1.62, 95% CI = 1.09-2.37). The risk was higher in the new BZD users exposed within 7 days (aOR = 3.09, 95% CI = 1.58-5.88).

CONCLUSION

We found that BZDs increase the risk of AACG at the beginning of its use among the Korean elderly. Clinicians should monitor visual disturbance in the elderly during the early period after prescription of BZD.

Effect of Cilostazol on Incident Dementia in Elderly Men and Women with Ischemic Heart Disease

BACKGROUND

Ischemic heart disease (IHD) is associated with cognitive decline and may contribute to an increased risk of dementia.

OBJECTIVE

The goal of the present study was to investigate whether cilostazol use is associated with a lower risk of incident dementia in Asian patients with IHD, and whether these effects differed based on sex.

METHODS

This retrospective cohort study was performed using the Korean National Insurance Claim Data of the Health Insurance Review and Assessment Service; the duration of the study was from January 1, 2007 to December 31, 2015. The study group comprised 66,225 patients with IHD, aged >65 years, who had received cilostazol. Age- and sex-matched IHD patients without cilostazol exposure were selected as the control group. The risk of dementia was compared between the cilostazol and control groups.

RESULTS

Compared to the control group, total cilostazol users had a marginally significant lower risk of incident dementia. After stratification by sex, the reducing effect of cilostazol on incident dementia was significant in female participants, but not in male participants. Female patients who had cilostazol for over 2 years showed a clinically meaningful preventive effect (HR, 0.85; 95% CI, 0.82-0.88).

CONCLUSIONS

This study suggested that cilostazol treatment may reduce the risk of incident dementia in Korean patients with IHD. Its beneficial effect was remarkably significant in female patients who received cilostazol for over a 2-year period.

Moderate hearing loss is related with social frailty in a community-dwelling older adults: The Korean Frailty and Aging Cohort Study (KFACS)

OBJECTIVES

To determine whether hearing loss is associated with social frailty in older adults.

METHODS

Cross-sectional analysis of cohort study data. Hearing was measured using of Pure-tone audiometry. Hearing loss was determined based on the average of hearing thresholds at 0.5, 1, and 2 kHz in the ear that had better hearing. Social frailty was defined based on the summation of the following 5 social components (1. Neighborhood meeting attendance 2. Talking to friend(s) sometimes 3.Someone gives you love and affection 4. Living alone 5. Meeting someone every day). Participants who had no correspondence to the components were considered non-social frailty; those with 1-2 components were considered social prefrailty; and those having 3 or more components were considered social frailty.

RESULTS

The prevalence of non-social frailty, social prefrailty, social frailty was 27.6%, 60.7% and 11.7% respectively. Of the five questions, two components (Neighborhood meeting attendance and Presence of someone who shows love and affection to the participants) were associated with hearing loss (p < 0.001). Compared to non-social frailty, the odds ratio of social frailty for hearing loss was 2.24 (95% CI 1.48-3.38) after adjusting for age, residential area, economic status, smoking, depressive disorder and MMSE, and 2.17 (95% CI 1.43-3.30) after further adjustments with physical frailty.

CONCLUSION

Hearing loss was associated with social frailty even after controlling confounding factors even including physical frailty.

Risk of suicide and accidental deaths among elderly patients with cognitive impairment

Background

The leading causes of death among the elderly with cognitive impairment are unknown. This study aims to estimate the suicide and accidental death rates on the basis of a clinical case registry of patients diagnosed with cognitive impairment.

Methods

The target sample consisted of 10,169 patients diagnosed with dementia or mild cognitive impairment (MCI), who were evaluated at the Clinical Research Center for Dementia of Korea (CREDOS) from January 2005 to December 2013. Information about whether the patients had died from suicide or in any kind of accident by December 31, 2016, was obtained from the database of the National Statistical Office (NSO). The standardized mortality ratio (SMR) and Cox-regression analysis were performed for evaluating the risk of suicide and accidental death as identified by the ICD-10.

Results

The average of the Clinical Dementia Rating Scale (CDR) score (0.68 vs 0.93) was lower, and the age at the time of study registration (71.42 vs 75.68 years) was younger in the suicidal death group, as compared to the accidental death group. The overall SMR for accidental death in cognitively impaired patients (1.44, 95% CI 1.22–1.71) was significantly higher than the general population. Later onset (1.43, 95% CI 1.20–1.71) and older age (2.21, 95% CI 1.04–4.68) increased the risk of accidental death in cognitively impaired patients. According to the dementia subtypes, the SMR for accidental death was higher in both Alzheimer’s disease (1.72, 95% CI 1.36–2.14) and vascular dementia (2.14, 95% CI 1.27–3.38). Additionally, the SMR for accidental death showed an increasing tendency as the CDR score increased (mild 1.80, 95% CI 1.32–2.42, moderate 1.86, 95% CI 1.07–3.03, severe 3.32, 95% CI 1.08–7.76). Unemployment increased the risks of both suicide (3.71, 95% CI 1.54–8.95) and accidental death (2.09, 95% CI 1.20–3.63).

Conclusions

Among people with cognitive impairment, the risk of death by suicide did not increase, whereas that of accidental death increased significantly. Preventive strategies for premature mortality in those with cognitive impairment should be implemented from the early stages and should include careful evaluation of the individual risk factors for each type of death.