American Journal of Geriatric Psychiatry最新文献

Objective: The current evidence regarding how different predictor domains contributes to predicting incident dementia remains unclear. This study aims to assess the incremental value of five predictor domains when added to a simple dementia risk prediction model (DRPM) for predicting incident dementia in older adults.

Design: Population-based, prospective cohort study.

Setting: UK Biobank study.

Participants: Individuals aged 60 or older without dementia.

Measurements: Fifty-five dementia-related predictors were gathered and categorized into clinical and medical history, questionnaire, cognition, polygenetic risk, and neuroimaging domains. Incident dementia (all-cause) and the subtypes, Alzheimer's disease (AD) and vascular dementia (VaD), were determined through hospital and death registries. Ensemble machine learning (ML) DRPMs were employed for prediction. The incremental values of risk predictors were assessed using the percent change in Area Under the Curve (∆AUC%) and the net reclassification index (NRI).

Results: The simple DRPM which included age, body mass index, sex, education, diabetes, hyperlipidaemia, hypertension, depression, smoking, and alcohol consumption yielded an AUC of 0.711 (± 0.008 SD). The five predictor domains exhibited varying levels of incremental value over the basic model when predicting all-cause dementia and the two subtypes. Neuroimaging markers provided the highest incremental value in predicting all-cause dementia (∆AUC% +9.6%) and AD (∆AUC% +16.5%) while clinical and medical history data performed the best at predicting VaD (∆AUC% +12.2%). Combining clinical and medical history, and questionnaire data synergistically enhanced ML DRPM performance.

Conclusion: Combining predictors from different domains generally results in better predictive performance. Selecting predictors involves trade-offs, and while neuroimaging markers can significantly enhance predictive accuracy, they may pose challenges in terms of cost or accessibility.

Objectives: To determine the prevalence and correlates of successful aging in US veterans who screened positive for current major depressive disorder (MDD), generalized anxiety disorder (GAD), and/or posttraumatic stress disorder (PTSD).

Methods: In a nationally representative sample of 475 US military veterans (mean age=58.3, SD=14.7; range 24-92) who screened positive for MDD, GAD, and/or PTSD, multivariable logistic regression and relative importance analyses were conducted to identify independent correlates of successful aging.

Results: One-in-five (20.6%) veterans rated themselves as aging successfully. Resilience and gratitude were the strongest positive correlates of successful aging, accounting for 38.1% and 32.4% of the explained variance, respectively. Greater somatic symptoms were the strongest negative correlate, accounting for 11.2% of the explained variance. Higher gratitude moderated the negative association between somatic symptoms and successful aging.

Conclusions: Positive psychiatry interventions targeting psychosocial factors such as resilience and gratitude may help promote successful aging among US veterans with mental disorders.

Objectives: To examine prevalence and risk correlates for post-traumatic stress disorder (PTSD) occurring during or after admission to a Veterans Administration (VA) skilled nursing facility.

Design: Retrospective cohort analysis of electronic health record information extracted from the VA Corporate Data Warehouse.

Setting: United States VA skilled nursing facility.

Participants: 57,414 Veterans age 60+ with an admission during five fiscal years, 2018-2022, excluding those who died within six months of admission or were still admitted.

Measurements: The dependent variable was PTSD diagnosis during or six-months following the admission. Risk correlates examined were: age, gender, race, rurality, clinical complexity, prior dementia diagnosis, length of stay, and facility size; odds ratios (OR) and confidence intervals (CI) are provided for each correlate.

Results: 19.1% of Veterans had a diagnosis of PTSD, associated with younger age (age 60-69 compared to age 80+; OR: 2.92, 95% CI: 2.70-3.14; age 70-79 compared to age 80+ OR: 4.51, 95% CI: 4.20-4.84); female gender (OR: 1.65, 95% CI: 1.50-1.82); minoritized race (OR: 1.17, 95% CI: 1.12-1.23); higher clinical complexity (OR:1.22, 95% CI: 1.17-1.28). As compared to Veterans who had a prior PTSD diagnosis, Veterans with newly diagnosed PTSD were more likely to be older (age 60 group OR= 0.59, 95% CI:0.51-0.70; age 70 group OR= 0.54, 95% CI:0.46-0.62,), rural (OR=1.14, 95% CI:1.04-1.24) and admitted to a larger facility (OR=1.22, 95% CI:1.12-1.33).

Conclusions: PTSD is a significant concern for older Veterans admitted to VA skilled nursing facilities, supporting the need for trauma-informed care, particularly for those most at risk.

Objectives: Family caregivers of older adults with severe dementia often experience anticipatory grief. We aimed to investigate the temporal association of caregiving stressors (older adults' behavioral symptoms, and caregiver-older adult co-residence and emotional closeness) and caregivers' anticipatory grief, and its mediation by positive and negative caregiving experiences.

Design: Prospective cohort.

Setting: Singapore.

Participants: About 169 family caregivers of older adults with severe dementia were surveyed every 4 months for 4 years (up to 13 surveys).

Measurements: We measured anticipatory grief using the Marwit Meuser Caregiver Grief Inventory-Short Form, negative caregiving experiences using the Caregiver Reaction Assessment scale, positive caregiving experiences using Gain in Alzheimer Care Instrument and behavioral symptoms using the Cohen-Mansfield Agitation Inventory. We implemented a cross-lagged panel model to test mediation, a form of longitudinal path analysis.

Results: About 35% of the caregivers reported high anticipatory grief at least once during the study period. Older adults' behavioral symptoms had a significant direct effect (Standardized coefficient [95% confidence interval]: 0.12 [0.04, 0.21]) on caregivers' anticipatory grief. Negative experiences mediated the significant indirect effect of older adults' behavioral symptoms (0.16 [0.06, 0.25]) and coresidence (0.16 [0.07, 0.25]) on caregivers' anticipatory grief. Positive caregiving experiences did not mediate any path.

Conclusions: Findings indicate a temporal association between caregiving stressors and anticipatory grief, mediated by negative caregiving experiences. Routine screening for anticipatory grief, and interventions to address caregiver stressors and negative caregiving experiences may alleviate caregivers' grief.

Background: Aging, frontotemporal dementia (FTD), and Alzheimer's dementia (AD) manifest electroencephalography (EEG) alterations, particularly in the beta-to-theta power ratio derived from linear power spectral density (PSD). Given the brain's nonlinear nature, the EEG nonlinear features could provide valuable physiological indicators of aging and cognitive impairment. Multiscale dispersion entropy (MDE) serves as a sensitive nonlinear metric for assessing the information content in EEGs across biologically relevant time scales.

Objective: To compare the MDE-derived beta-to-theta entropy ratio with its PSD-based counterpart to detect differences between healthy young and elderly subjects and between different dementia subtypes.

Methods: Scalp EEG recordings were obtained from two datasets: 1) Aging dataset: 133 healthy young and 65 healthy older adult individuals; and 2) Dementia dataset: 29 age-matched healthy controls (HC), 23 FTD, and 36 AD participants. The beta-to-theta ratios based on MDE vs. PSD were analyzed for both datasets. Finally, the relationships between cognitive performance and the beta-to-theta ratios were explored in HC, FTD, and AD.

Results: In the Aging dataset, older adults had significantly higher beta-to-theta entropy ratios than young adults. In the Dementia dataset, this ratio outperformed the beta-to-theta PSD approach in distinguishing between HC, FTD, and AD. The AD participants had a significantly lower beta-to-theta entropy ratio than FTD, especially in the temporal region, unlike its corresponding PSD-based ratio. The beta-to-theta entropy ratio correlated significantly with cognitive performance.

Conclusion: Our study introduces the beta-to-theta entropy ratio using nonlinear MDE for EEG analysis, highlighting its potential as a sensitive biomarker for aging and cognitive impairment.