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.

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.

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.

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.

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.

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.

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.

A Randomized Comparison of Medication and Cognitive Behavioral Therapy for Treating Depression in Low-Income Young Minority Women.

BACKGROUND Longitudinal data arise frequently in biomedical science and health studies where each subject is repeatedly measured over time. We compared the effectiveness of medication and cognitive behavioral therapy on depression in predominantly low-income young minority women. MATERIAL AND METHODS The treatment effects on patients with low-level depression may differ from the treatment effects on patients with high-level depression. We used a quantile regression model for longitudinal data analysis to determine which treatment is most beneficial for patients at different stress levels over time. RESULTS The results confirm that both treatments are effective in reducing the depression score over time, regardless of the depression level. CONCLUSIONS Compared to cognitive behavioral therapy, treatment with medication more often effective, although the size of the effect differs. Thus, no matter how severe a patient's depression symptoms are, antidepressant medication is effective in decreasing depression symptoms.

Large intracranial volume accelerates conversion to dementia in males and APOE4 non-carriers with mild cognitive impairment.

BACKGROUND: It is unclear how brain reserve interacts with gender and apolipoprotein E4 (APOE4) genotype, and how this influences the progression of Alzheimer's disease (AD). The association between intracranial volume (ICV) and progression to AD in subjects with mild cognitive impairment (MCI), and differences according to gender and APOE4 genotype, was investigated. METHODS: Data from subjects initially diagnosed with MCI and at least two visits were downloaded from the ADNI database. Those who progressed to AD were defined as converters. The longitudinal influence of ICV was determined by survival analysis. The time of conversion from MCI to AD was set as a fiducial point, as all converters would be at a similar disease stage then, and longitudinal trajectories of brain atrophy and cognitive decline around that point were compared using linear mixed models. RESULTS: Large ICV increased the risk of conversion to AD in males (HR: 4.24, 95% confidence interval (CI): 1.17-15.40) and APOE4 non-carriers (HR: 10.00, 95% CI: 1.34-74.53), but not in females or APOE4 carriers. Cognitive decline and brain atrophy progressed at a faster rate in males with large ICV than in those with small ICV during the two years before and after the time of conversion. CONCLUSIONS: Large ICV increased the risk of conversion to AD in males and APOE4 non-carriers with MCI. This may be due to its influence on disease trajectory, which shortens the duration of the MCI stage. A longitudinal model of progression trajectory is proposed.

Association of alcohol drinking with verbal and visuospatial memory impairment in older adults: Clinical Research Center for Dementia of South Korea (CREDOS) study.

BACKGROUND: The study's aim was to examine the association of alcohol consumption with verbal and visuospatial memory impairment in older people. METHODS: Participants were 1,572, aged ≥60 years, in the hospital-based registry of the Clinical Research Center for Dementia of South Korea (CREDOS). Moderate drinking was defined as no more than seven drinks per week and three drinks per day. Memory impairment was defined as performance with more than 1 standard deviation below the mean value on the Seoul Verbal Learning Test and Rey Complex Figure Test. RESULTS: Those who consumed alcohol moderately, compared with abstainers, had a lower odds of verbal memory impairment (Odds Ratio [OR] = 0.64; 95% Confidence Interval [CI]: 0.46-0.87), adjusting for covariates. Visuospatial memory, however, was not significantly associated with alcohol consumption. CONCLUSIONS: Moderate alcohol drinking is associated with a reduced likelihood of verbal memory impairment among older people attending memory clinics.

Effects of a multidomain lifestyle modification on cognitive function in older adults: an eighteen-month community-based cluster randomized controlled trial.

BACKGROUND: A healthy lifestyle may protect against cognitive decline. We examined outcomes in elderly individuals after 18 months of a five-group intervention program consisting of various modalities to prevent cognitive decline. METHODS: We conducted a cluster randomized controlled trial assessing 460 community-dwelling individuals aged 60 years and older in a geriatric community mental health center in Suwon, Republic of Korea, between 2008 and 2010. We developed an intervention program based on the principles of contingency management, which could be delivered by ordinary primary health workers. Group A (n = 81) received standard care services. Group B (n = 80) received bimonthly (once every 2 months) telephonic care management. Group C (n = 111) received monthly telephonic care management and educational materials similar to those in group B. Group D (n = 93) received bimonthly health worker-initiated visits and counseling. Group E (n = 94) received bimonthly health worker-initiated visits, counseling, and rewards for adherence to the program. RESULTS: The primary outcome was the change in Mini-Mental State Examination (MMSE) scores from baseline to the final follow-up visit at 18 months. Group E showed superior cognitive function to group A (adjusted coefficient ß = 0.99, p = 0.044), with participation in cognitive activities being the most important determining factor among several health behaviors (adjusted coefficient ß = 1.04, p < 0.01). CONCLUSIONS: Engaging in cognitive activities, in combination with positive health behaviors, may be most beneficial in preserving cognitive abilities in community-dwelling older adults.

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.

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.

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 (ß = 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 (ß = -2.59 × 10-3; P = 0.004). Third, associations of WMHV progression with HbA1c were particularly apparent for deep WMHV change (ß = 7.17 × 10-4; P < 0.01) compared with periventricular WMHV change and, for frontal (ß = 5.00 × 10-4; P < 0.001) and parietal (ß = 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.

A multimodal machine learning model for predicting dementia conversion in Alzheimer's disease.

Alzheimer's disease (AD) accounts for 60-70% of the population with dementia. Mild cognitive impairment (MCI) is a diagnostic entity defined as an intermediate stage between subjective cognitive decline and dementia, and about 10-15% of people annually convert to AD. We aimed to investigate the most robust model and modality combination by combining multi-modality image features based on demographic characteristics in six machine learning models. A total of 196 subjects were enrolled from four hospitals and the Alzheimer's Disease Neuroimaging Initiative dataset. During the four-year follow-up period, 47 (24%) patients progressed from MCI to AD. Volumes of the regions of interest, white matter hyperintensity, and regional Standardized Uptake Value Ratio (SUVR) were analyzed using T1, T2-weighted-Fluid-Attenuated Inversion Recovery (T2-FLAIR) MRIs, and amyloid PET (αPET), along with automatically provided hippocampal occupancy scores (HOC) and Fazekas scales. As a result of testing the robustness of the model, the GBM model was the most stable, and in modality combination, model performance was further improved in the absence of T2-FLAIR image features. Our study predicts the probability of AD conversion in MCI patients, which is expected to be useful information for clinician's early diagnosis and treatment plan design.

Sex-Specific Risk Factors and Clinical Dementia Outcomes for White Matter Hyperintensities in a large South Korean Cohort.

Objective: White matter hyperintensities (WMH) on brain MRI images are the most common feature of cerebral small vessel disease (CSVD). Studies have yielded divergent findings on the modifiable risk factors for WMH and WMH's impact on cognitive decline. Mounting evidence suggests sex differences in WMH burden and subsequent effects on cognition. Thus, we aimed to identify sex-specific modifiable risk factors for WMH. We then explored whether there were sex-specific associations of WMH to longitudinal clinical dementia outcomes. Methods: Participants aged 49-89 years were recruited at memory clinics and underwent a T2-weighted fluid-attenuated inversion recovery (FLAIR) 3T MRI scan to measure WMH volume. Participants were then recruited for two additional follow-up visits, 1-2 years apart, where clinical dementia rating sum of boxes (CDR-SB) scores were measured. We first explored which known modifiable risk factors for WMH were significant when tested for a sex-interaction effect. We additionally tested which risk factors were significant when stratified by sex. We then tested to see whether WMH is longitudinally associated with clinical dementia that is sex-specific. Results: The study utilized data from 713 participants (241 males, 472 females) with a mean age of 72.3 years and 72.8 years for males and females, respectively. 57.3% and 59.5% of participants were diagnosed with mild cognitive impairment (MCI) for males and females, respectively. 40.7% and 39.4% were diagnosed with dementia for males and females, respectively. Of the 713 participants, 181 participants had CDR-SB scores available for three longitudinal time points. Compared to males, females showed stronger association of age to WMH volume. Type 2 Diabetes was associated with greater WMH burden in females but not males. Finally, baseline WMH burden was associated with worse clinical dementia outcomes longitudinally in females but not in males. Discussion: Elderly females have an accelerated increase in cerebrovascular burden as they age, and subsequently are more vulnerable to clinical dementia decline due to CSVD. Additionally, females are more susceptible to the cerebrovascular consequences of diabetes. These findings emphasize the importance of considering sex when examining the consequences of CSVD. Future research should explore the underlying mechanisms driving these sex differences and personalized prevention and treatment strategies. Clinical trial registration: The BICWALZS is registered in the Korean National Clinical Trial Registry (Clinical Research Information Service; identifier, KCT0003391). Registration Date 2018/12/14.

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.

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.

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.