Comparison risk factors of cognitive decline between aged living alone and with a spouse

2021 ◽  
Vol 22 (3) ◽  
pp. 83-96
Author(s):  
Hyuna Park ◽  
◽  
Hyunjong Song
Keyword(s):  
Risk Factors ◽  
Cognitive Decline ◽  
Living Alone

scholarly journals Risk factors for subjective cognitive decline: the CABLE study

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Chen Wen ◽  
Hao Hu ◽  
Ya-Nan Ou ◽  
Yan-Lin Bi ◽  
Ya-Hui Ma ◽  
...  
Keyword(s):  
Risk Factors ◽  
Cognitive Decline ◽  
Living Alone ◽  
Subjective Cognitive Decline ◽  
Trend Test ◽  
Linear Regression Models ◽  
Early Prevention ◽  
Factors Associated ◽  
Physical Exercises ◽  
Armitage Trend Test

AbstractIncreasing evidences supported that subjective cognitive decline (SCD) might be a potential first symptomatic manifestation of Alzheimer’s disease (AD). The rapidly growing number of SCD individuals who seek medical help and advice also makes it urgent to develop more precise strategy for SCD. Therefore, this study aimed to explore the risk factors for SCD. Logistics and linear regression models were performed to investigate 41 factors for SCD in 1165 participants without objective cognitive impairment. Cochran-Armitage trend test was used to confirm the constant trend toward higher prevalence of SCD with an increasing number of risk factors. A high overall prevalence of SCD was found in total participants (42%). Eight factors were eventually identified as risk factors for SCD, including four stable factors associated with both SCD statues and severity (older age, thyroid diseases, minimal anxiety symptoms, and day time dysfunction; odds ratio (OR) ranging from 1.74 to 2.29) as well as four suggestive factors associated with either SCD statues or severity (female sex, anemia, lack of physical exercises, and living alone; OR ranging from 1.30 to 2.29). The prevalence of SCD gradually increased with the number of risk factors clustering increased in individuals (p for trend <0.001). Five of these eight factors were further proved among individuals with SCD-plus features. These findings revealed several risk factors for SCD, providing some new clues for formulating priority strategies for early prevention of SCD.

The Spectrum of Cerebrovascular Disease and Associated Cognitive Decline

2019 ◽  
pp. 574-599
Author(s):  
Victoria J. Williams ◽  
Steven E. Arnold ◽  
David H. Salat
Keyword(s):  
Risk Factors ◽  
Cognitive Impairment ◽  
Cerebrovascular Disease ◽  
Cognitive Decline ◽  
Vascular Dementia ◽  
Structure And Function ◽  
Vascular Risk Factors ◽  
Vascular Health ◽  
Vascular Risk ◽  
And Function

Throughout the lifespan, common variations in systemic health and illness contribute to alterations in vasculature structure and function throughout the body, significantly increasing risk for cardiovascular and cerebrovascular disease (CVD). CVD is a prevalent cause of mortality in late life; it also promotes brain alterations, contributing to cognitive decline and, when severe, vascular dementia. Even prior to diseased states, individual variation in CVD risk is associated with structural and functional brain alterations. Yet, how cumulative asymptomatic alterations in vessel structure and function contribute to more subtle changes in brain tissue integrity and function that emerge in late life is unclear. Finally, vascular risk factors are associated with the clinical progression of neurodegenerative diseases such as Alzheimer’s disease (AD); however, recent theory posits that vascular degeneration may serve a contributory role in these conditions. This chapter reviews how lifespan changes in vascular health contribute to degenerative changes in neural tissue and the subsequent development of cognitive impairment and/or vascular dementia. It first discusses associations between vascular risk factors and cognition and also how declining vascular health may lead to cognitive impairment and dementia. Next, it identifies basic aspects of cerebrovascular anatomy and physiology sustaining tissue health and discusses how vulnerabilities of this system contribute to neurodegenerative changes. Finally, it reviews evidence of vascular contributions to AD and presents ideas for future research to better understand the full spectrum of cerebrovascular contributions to brain aging, cognitive decline, and dementia.

Analyses of Visuospatial and Visuoperceptual Errors as Predictors of Dementia in Parkinson’s Disease Patients with Subjective Cognitive Decline and Mild Cognitive Impairment

2020 ◽  
pp. 1-11
Author(s):  
Iván Galtier ◽  
Antonieta Nieto ◽  
María Mata ◽  
Jesús N. Lorenzo ◽  
José Barroso
Keyword(s):  
Risk Factors ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Cognitive Decline ◽  
Poor Performance ◽  
Subjective Cognitive Decline ◽  
Independent Risk Factors

ABSTRACT Objective: Subjective cognitive decline (SCD) and mild cognitive impairment (MCI) in Parkinson’s disease (PD) are considered as the risk factors for dementia (PDD). Posterior cortically based functions, such as visuospatial and visuoperceptual (VS-VP) processing, have been described as predictors of PDD. However, no investigations have focused on the qualitative analysis of the Judgment of Line Orientation Test (JLOT) and the Facial Recognition Test (FRT) in PD-SCD and PD-MCI. The aim of this work was to study the VS-VP errors in JLOT and FRT. Moreover, these variables are considered as predictors of PDD. Method: Forty-two PD patients and 19 controls were evaluated with a neuropsychological protocol. Patients were classified as PD-SCD and PD-MCI. Analyses of errors were conducted following the procedure described by Ska, Poissant, and Joanette (1990). Follow-up assessment was conducted to a mean of 7.5 years after the baseline. Results: PD-MCI patients showed a poor performance in JLOT and FRT total score and made a greater proportion of severe intraquadrant (QO2) and interquadrant errors (IQO). PD-SCD showed a poor performance in FRT and made mild errors in JLOT. PD-MCI and QO2/IQO errors were independent risk factors for PDD during the follow-up. Moreover, the combination of both PD-MCI diagnosis and QO2/IQO errors was associated with a greater risk. Conclusions: PD-MCI patients presented a greater alteration in VS-VP processing observable by the presence of severe misjudgments. PD-SCD patients also showed mild difficulties in VS-SP functions. Finally, QO2/IQO errors in PD-MCI are a useful predictor of PDD, more than PD-MCI diagnosis alone.

ALZHEIMER’S ASSOCIATION: INITIATIVES & PUBLIC HEALTH PERSPECTIVE

2016 ◽  
pp. 1-3
Author(s):  
H.M. Snyder ◽  
M.C. Carrillo
Keyword(s):  
Public Health ◽  
Physical Activity ◽  
Risk Factors ◽  
Cognitive Decline ◽  
Population Based ◽  
Current Evidence ◽  
Public Health Perspective ◽  
Reduce Risk ◽  
Number Of Individuals ◽  
Do So

An estimated 47 million people worldwide are living with dementia in 2015 and this number is expected to triple by 2050. There is a clear urgency for therapies and / or interventions to slow, stop or prevent dementia. Amounting evidence suggests strategies to reduce risk of development dementia may be of growing import for reducing the number of individuals affected. The Alzheimer’s Association believes, from a population based perspective that: (1) Regular physical activity and management of cardiovascular risk factors (e.g. diabetes, obesity, smoking and hypertension) have been shown to reduce the risk of cognitive decline and may reduce the risk of dementia; (2) A healthy diet and lifelong learning/cognitive training may also reduce the risk of cognitive decline. The current evidence underscores the need to communicate to the broader population what the science indicates and to do so with diverse stakeholders and consistent messaging. There has never been a better time to define and distribute global messaging on public health for dementia.

Long-Term Cognitive Decline After Stroke: An Individual Participant Data Meta-Analysis

Stroke ◽  
2021 ◽  
Author(s):  
Jessica W. Lo ◽  
John D. Crawford ◽  
David W. Desmond ◽  
Hee-Joon Bae ◽  
Jae-Sung Lim ◽  
...  
Keyword(s):  
Risk Factors ◽  
Cognitive Function ◽  
Cognitive Decline ◽  
Recurrent Stroke ◽  
Meta Analysis ◽  
Older Age ◽  
Individual Participant Data ◽  
Individual Participant ◽  
Global Cognition

Background and Purpose: Poststroke cognitive impairment is common, but the trajectory and magnitude of cognitive decline after stroke is unclear. We examined the course and determinants of cognitive change after stroke using individual participant data from the Stroke and Cognition Consortium. Methods: Nine longitudinal hospital-based cohorts from 7 countries were included. Neuropsychological test scores and normative data were used to calculate standardized scores for global cognition and 5 cognitive domains. One-step individual participant data meta-analysis was used to examine the rate of change in cognitive function and risk factors for cognitive decline after stroke. Stroke-free controls were included to examine rate differences. Based on the literature and our own data that showed short-term improvement in cognitive function after stroke, key analyses were restricted to the period beginning 1-year poststroke to focus on its long-term effects. Results: A total of 1488 patients (mean age, 66.3 years; SD, 11.1; 98% ischemic stroke) were followed for a median of 2.68 years (25th–75th percentile: 1.21–4.14 years). After an initial period of improvement through up to 1-year poststroke, decline was seen in global cognition and all domains except executive function after adjusting for age, sex, education, vascular risk factors, and stroke characteristics (−0.053 SD/year [95% CI, −0.073 to −0.033]; P <0.001 for global cognition). Recurrent stroke and older age were associated with faster decline. Decline was significantly faster in patients with stroke compared with controls (difference=−0.078 SD/year [95% CI, −0.11 to −0.045]; P <0.001 for global cognition in a subgroup analysis). Conclusions: Patients with stroke experience cognitive decline that is faster than that of stroke-free controls from 1 to 3 years after onset. An increased rate of decline is associated with older age and recurrent stroke.

scholarly journals Bereavement and behavioral changes as risk factors for cognitive decline in adults with Down syndrome

2014 ◽  
pp. 2209 ◽  
Author(s):  
Luciana Mascarenhas Fonseca ◽  
Melaine Cristina de Oliveira ◽  
Laura Maria de Figueiredo Ferreira Guilhoto ◽  
Esper Abrao Cavalheiro ◽  
Cassio Machado de Campos Bottino
Keyword(s):  
Risk Factors ◽  
Down Syndrome ◽  
Cognitive Decline ◽  
Behavioral Changes

scholarly journals O3-08-02: CARDIOVASCULAR RISK FACTORS ACROSS THE LIFECOURSE AND COGNITIVE DECLINE: A POOLED COHORT STUDY

2019 ◽  
Vol 15 ◽  
pp. P901-P901
Author(s):  
Kristine Yaffe ◽  
Eric Vittinghoff ◽  
Patrick Stuchlik ◽  
Leslie Grasset ◽  
Tina D. Hoang ◽  
...  
Keyword(s):  
Risk Factors ◽  
Cohort Study ◽  
Cardiovascular Risk ◽  
Cognitive Decline ◽  
Cardiovascular Risk Factors

scholarly journals Cognitive Decline in Adults Aged 65 and Older in Cumbayá, Quito, Ecuador: Prevalence and Risk Factors

Cureus ◽  
2018 ◽  
Author(s):  
Patricio H Espinosa del Pozo ◽  
Patricio S Espinosa ◽  
Eduardo A Donadi ◽  
Edson Z Martinez ◽  
JUAN C SALAZAR-URIBE ◽  
...  
Keyword(s):  
Risk Factors ◽  
Cognitive Decline

scholarly journals Developing a Predictive Model for Depressive Disorders Using Stacking Ensemble and Naive Bayesian Nomogram: Using Samples Representing South Korea

2022 ◽  
Vol 12 ◽  
Author(s):  
Haewon Byeon
Keyword(s):  
Risk Factors ◽  
Older Adults ◽  
Fatty Acid ◽  
Depressive Disorders ◽  
Subjective Health ◽  
Bayesian Regression ◽  
Living Alone ◽  
Study Objective ◽  
Naive Bayesian ◽  
Naïve Bayesian

This study provided baseline data for preventing depression in female older adults living alone by understanding the degree of their depressive disorders and factors affecting these depressive disorders by analyzing epidemiological survey data representing South Koreans. To achieve the study objective, this study explored the main risk factors of depressive disorders using the stacking ensemble machine technique. Moreover, this study developed a nomogram that could help primary physicians easily interpret high-risk groups of depressive disorders in primary care settings based on the major predictors derived from machine learning. This study analyzed 582 female older adults (≥60 years old) living alone. The depressive disorder, a target variable, was measured using the Korean version of Patient Health Questionnaire-9. This study developed five single predictive models (GBM, Random Forest, Adaboost, SVM, XGBoost) and six stacking ensemble models (GBM + Bayesian regression, RandomForest + Bayesian regression, Adaboost + Bayesian regression, SVM + Bayesian regression, XGBoost + Bayesian regression, GBM + RandomForest + Adaboost + SVM + XGBoost + Bayesian regression) to predict depressive disorders. The naive Bayesian nomogram confirmed that stress perception, subjective health, n-6 fatty acid, n-3 fatty acid, mean hours of sitting per day, and mean daily sleep hours were six major variables related to the depressive disorders of female older adults living alone. Based on the results of this study, it is required to evaluate the multiple risk factors for depression including various measurable factors such as social support.

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