Background: Although the presence of both obesity and reduced muscle mass presents a dual metabolic burden and additively has a negative effect on a variety of cardiometabolic parameters, data regarding the associations between their combined effects and left ventricular diastolic function are limited. This study investigated the association between the ratio of skeletal muscle mass to visceral fat area (SVR) and left ventricular diastolic dysfunction (LVDD) in patients with preserved ejection fraction using random forest machine learning. Methods: In total, 1,070 participants with preserved left ventricular ejection fraction who underwent comprehensive health examinations, including transthoracic echocardiography and bioimpedance body composition analysis, were enrolled. SVR was calculated as an index of sarcopenic obesity by dividing the appendicular skeletal muscle mass by the visceral fat area. Results: In the random forest model, age and SVR were the most powerful predictors of LVDD. Multivariate logistic regression analysis demonstrated that older age (adjusted odds ratio [OR], 1.11; 95% confidence interval [CI], 1.07 to 1.15) and lower SVR (adjusted OR, 0.08; 95% CI, 0.01 to 0.57) were independent risk factors for LVDD.SVR showed a significant improvement in predictive performance and fair predictability for LVDD, with the highest area under the curve noted in both men and women, with statistical significance. In non-obese and metabolically healthy individuals, the lowest SVR tertile was associated with a greater risk of LVDD compared to the highest SVR tertile. Conclusion Decreased muscle mass and increased visceral fat were significantly associated with LVDD compared to obesity, body fat composition, and body muscle composition indices.

Background: Although the presence of both obesity and reduced muscle mass presents a dual metabolic burden and additively has a negative effect on a variety of cardiometabolic parameters, data regarding the associations between their combined effects and left ventricular diastolic function are limited. This study investigated the association between the ratio of skeletal muscle mass to visceral fat area (SVR) and left ventricular diastolic dysfunction (LVDD) in patients with preserved ejection fraction using random forest machine learning. Methods: In total, 1,070 participants with preserved left ventricular ejection fraction who underwent comprehensive health examinations, including transthoracic echocardiography and bioimpedance body composition analysis, were enrolled. SVR was calculated as an index of sarcopenic obesity by dividing the appendicular skeletal muscle mass by the visceral fat area. Results: In the random forest model, age and SVR were the most powerful predictors of LVDD. Multivariate logistic regression analysis demonstrated that older age (adjusted odds ratio [OR], 1.11; 95% confidence interval [CI], 1.07 to 1.15) and lower SVR (adjusted OR, 0.08; 95% CI, 0.01 to 0.57) were independent risk factors for LVDD.SVR showed a significant improvement in predictive performance and fair predictability for LVDD, with the highest area under the curve noted in both men and women, with statistical significance. In non-obese and metabolically healthy individuals, the lowest SVR tertile was associated with a greater risk of LVDD compared to the highest SVR tertile. Conclusion Decreased muscle mass and increased visceral fat were significantly associated with LVDD compared to obesity, body fat composition, and body muscle composition indices.

Background: Although the presence of both obesity and reduced muscle mass presents a dual metabolic burden and additively has a negative effect on a variety of cardiometabolic parameters, data regarding the associations between their combined effects and left ventricular diastolic function are limited. This study investigated the association between the ratio of skeletal muscle mass to visceral fat area (SVR) and left ventricular diastolic dysfunction (LVDD) in patients with preserved ejection fraction using random forest machine learning. Methods: In total, 1,070 participants with preserved left ventricular ejection fraction who underwent comprehensive health examinations, including transthoracic echocardiography and bioimpedance body composition analysis, were enrolled. SVR was calculated as an index of sarcopenic obesity by dividing the appendicular skeletal muscle mass by the visceral fat area. Results: In the random forest model, age and SVR were the most powerful predictors of LVDD. Multivariate logistic regression analysis demonstrated that older age (adjusted odds ratio [OR], 1.11; 95% confidence interval [CI], 1.07 to 1.15) and lower SVR (adjusted OR, 0.08; 95% CI, 0.01 to 0.57) were independent risk factors for LVDD.SVR showed a significant improvement in predictive performance and fair predictability for LVDD, with the highest area under the curve noted in both men and women, with statistical significance. In non-obese and metabolically healthy individuals, the lowest SVR tertile was associated with a greater risk of LVDD compared to the highest SVR tertile. Conclusion Decreased muscle mass and increased visceral fat were significantly associated with LVDD compared to obesity, body fat composition, and body muscle composition indices.

Objective: This study aimed to develop a machine learning model to predict cognitive decline in community-dwelling older adults. By integrating multimodal data, including demographic, psychosocial, and neuroimaging information, we sought to en-hance early detection of cognitive decline. Methods: Data were obtained from 159 participants in the Biobank Innovations for Chronic Cerebrovascular Disease with Alzheimer’s Disease Study. Participants underwent clinical assessments, neuropsychological testing, and magnetic resonance im-aging scans. Cognitive decline was defined as an increase in the Clinical Dementia Rating-Sum of Boxes of greater than 2.05 points per year at follow-up. Models were developed using the logistic classification, combining demographic, psychosocial as-sessments, and neuroimaging data. Model performance was evaluated using area under the curve (AUC), accuracy, and F1 score, while Shapley additive explanation values were used to assess feature importance. Results: The model that incorporated all data types achieved the highest performance, with an AUC of 0.834. The top predictor of cognitive decline was years of education, underscoring the importance of non-invasive, easily accessible data for prediction. Conclusion This machine learning model demonstrates significant potential for early cognitive decline prediction, offering a scalable tool for improving dementia screening and timely intervention, especially in resource-limited settings.

Objective: This study aimed to develop a machine learning model to predict cognitive decline in community-dwelling older adults. By integrating multimodal data, including demographic, psychosocial, and neuroimaging information, we sought to en-hance early detection of cognitive decline. Methods: Data were obtained from 159 participants in the Biobank Innovations for Chronic Cerebrovascular Disease with Alzheimer’s Disease Study. Participants underwent clinical assessments, neuropsychological testing, and magnetic resonance im-aging scans. Cognitive decline was defined as an increase in the Clinical Dementia Rating-Sum of Boxes of greater than 2.05 points per year at follow-up. Models were developed using the logistic classification, combining demographic, psychosocial as-sessments, and neuroimaging data. Model performance was evaluated using area under the curve (AUC), accuracy, and F1 score, while Shapley additive explanation values were used to assess feature importance. Results: The model that incorporated all data types achieved the highest performance, with an AUC of 0.834. The top predictor of cognitive decline was years of education, underscoring the importance of non-invasive, easily accessible data for prediction. Conclusion This machine learning model demonstrates significant potential for early cognitive decline prediction, offering a scalable tool for improving dementia screening and timely intervention, especially in resource-limited settings.

Objective: This study aimed to develop a machine learning model to predict cognitive decline in community-dwelling older adults. By integrating multimodal data, including demographic, psychosocial, and neuroimaging information, we sought to en-hance early detection of cognitive decline. Methods: Data were obtained from 159 participants in the Biobank Innovations for Chronic Cerebrovascular Disease with Alzheimer’s Disease Study. Participants underwent clinical assessments, neuropsychological testing, and magnetic resonance im-aging scans. Cognitive decline was defined as an increase in the Clinical Dementia Rating-Sum of Boxes of greater than 2.05 points per year at follow-up. Models were developed using the logistic classification, combining demographic, psychosocial as-sessments, and neuroimaging data. Model performance was evaluated using area under the curve (AUC), accuracy, and F1 score, while Shapley additive explanation values were used to assess feature importance. Results: The model that incorporated all data types achieved the highest performance, with an AUC of 0.834. The top predictor of cognitive decline was years of education, underscoring the importance of non-invasive, easily accessible data for prediction. Conclusion This machine learning model demonstrates significant potential for early cognitive decline prediction, offering a scalable tool for improving dementia screening and timely intervention, especially in resource-limited settings.

Objective: This study aimed to develop a machine learning model to predict cognitive decline in community-dwelling older adults. By integrating multimodal data, including demographic, psychosocial, and neuroimaging information, we sought to en-hance early detection of cognitive decline. Methods: Data were obtained from 159 participants in the Biobank Innovations for Chronic Cerebrovascular Disease with Alzheimer’s Disease Study. Participants underwent clinical assessments, neuropsychological testing, and magnetic resonance im-aging scans. Cognitive decline was defined as an increase in the Clinical Dementia Rating-Sum of Boxes of greater than 2.05 points per year at follow-up. Models were developed using the logistic classification, combining demographic, psychosocial as-sessments, and neuroimaging data. Model performance was evaluated using area under the curve (AUC), accuracy, and F1 score, while Shapley additive explanation values were used to assess feature importance. Results: The model that incorporated all data types achieved the highest performance, with an AUC of 0.834. The top predictor of cognitive decline was years of education, underscoring the importance of non-invasive, easily accessible data for prediction. Conclusion This machine learning model demonstrates significant potential for early cognitive decline prediction, offering a scalable tool for improving dementia screening and timely intervention, especially in resource-limited settings.

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.

Background/Aims: Achieving rapid reduction of low-density lipoprotein cholesterol (LDL-C) levels below 55 mg/dL in patients with acute myocardial infarction (AMI) can be challenging with statins alone. This single-center, retrospective study aimed to assess the impact of single-dose injection of evolocumab 140 mg on LDL-C levels during the peri-percutaneous coronary intervention (PCI) period in patients with AMI. Methods: A total of 95 patients with AMI who underwent PCI were divided into the evolocumab (n = 50) and non-evolocumab (n = 45) groups. Results: The percentage change of LDL-C level at 1–3 weeks from baseline was 78.4 ± 13.4% reduction in the evolocumab group versus 45.6 ± 22.6% in the non-evolocumab group, with a mean difference of -33.5% between the groups (95% CI: -42.6 to -24.5%; p < 0.001). The achievement rate of LDL-C levels below 55 mg/dL at 1–3 weeks was significantly higher in the evolocumab group than in the non-evolocumab group (97.7% vs. 60.0%, p < 0.001). Conclusions Patients with AMI who received single-dose injection of evolocumab 140 mg during the peri-PCI period had a significantly greater LDL-C reduction and higher proportion of patients achieved the target LDL-C level in the early phase AMI than those who did not receive evolocumab.

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.