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Plasma protein biomarkers have been considered promising tools for diagnosing dementia subtypes due to their low variability, cost-effectiveness, and minimal invasiveness in diagnostic procedures. Machine learning (ML) methods have been applied to enhance accuracy of the biomarker discovery. However, previous ML-based studies often overlook interactions between proteins, which are crucial in complex disorders like dementia. While protein-protein interactions (PPIs) have been used in network models, these models often fail to fully capture the diverse properties of PPIs due to their local awareness. This drawback increases the chance of neglecting critical components and magnifying the impact of noisy interactions. In this study, we propose a novel graph-based ML model for dementia subtype diagnosis, the graph propagational network (GPN). By propagating the independent effect of plasma proteins on PPI network, the GPN extracts the globally interactive effects between proteins. Experimental results showed that the interactive effect between proteins yielded to further clarify the differences between dementia subtype groups and contributed to the performance improvement where the GPN outperformed existing methods by 10.4% on average., (© The Author(s) 2024. Published by Oxford University Press.)

Introduction: This study aimed to explore the potential of whole brain white matter patterns as novel neuroimaging biomarkers for assessing cognitive impairment and disability in older adults., Methods: We conducted an in-depth analysis of magnetic resonance imaging (MRI) and amyloid positron emission tomography (PET) scans in 454 participants, focusing on white matter patterns and white matter inter-subject variability (WM-ISV)., Results: The white matter pattern ensemble model, combining MRI and amyloid PET, demonstrated a significantly higher classification performance for cognitive impairment and disability. Participants with Alzheimer's disease (AD) exhibited higher WM-ISV than participants with subjective cognitive decline, mild cognitive impairment, and vascular dementia. Furthermore, WM-ISV correlated significantly with blood-based biomarkers (such as glial fibrillary acidic protein and phosphorylated tau-217 [p-tau217]), and cognitive function and disability scores., Discussion: Our results suggest that white matter pattern analysis has significant potential as an adjunct neuroimaging biomarker for clinical decision-making and determining cognitive impairment and disability., Highlights: The ensemble model combined both magnetic resonance imaging (MRI) and amyloid positron emission tomography (PET) and demonstrated a significantly higher classification performance for cognitive impairment and disability. Alzheimer's disease (AD) revealed a notably higher heterogeneity compared to that in subjective cognitive decline, mild cognitive impairment, or vascular dementia. White matter inter-subject variability (WM-ISV) was significantly correlated with blood-based biomarkers (glial fibrillary acidic protein and phosphorylated tau-217 [p-tau217]) and with the polygenic risk score for AD. White matter pattern analysis has significant potential as an adjunct neuroimaging biomarker for clinical decision-making processes and determining cognitive impairment and disability., (© 2024 The Author(s). Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association.)

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., Competing Interests: Competing interests: Thurston: Astellas, Bayer, Hello Therapeutics (Advisory Board). All other authors declare no competing interests.

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.