Current Alzheimer research · 2021
Risk Assessment During Longitudinal Progression of Cognition in Older Adults: A Community-based Bayesian Networks Model.
СтатьяПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Han Hongjuan, Qin Yao, Ge Xiaoyan, Cui Jing, Liu Long, Luo Yanhong, Yang Bei, Yu Hongmei
Аннотация
BACKGROUND: Cognitive dysfunction, particularly in Alzheimer's disease (AD), seriously affects the health and quality of life of older adults. Early detection can prevent and slow cognitive decline.
OBJECTIVE: This study aimed at evaluating the role of socio-demographic variables, lifestyle, and physical characteristics in cognitive decline during AD progression and analyzing the probable causes and predicting stages of the disease.
METHODS: By analyzing data of 301 subjects comprising normal elderly and patients with mild cognitive impairment (MCI) or AD from six communities in Taiyuan, China, we identified the influencing factors during AD progression by a Logistic Regression model (LR) and then assessed the associations between variables and cognition using a Bayesian Networks (BNs) model.
RESULTS: The LR revealed that age, sex, family status, education, income, character, depression, hypertension, disease history, physical exercise, reading, drinking, and job status were significantly associated with cognitive decline. The BNs model revealed that hypertension, education, job status, and depression affected cognitive status directly, while character, exercise, sex, reading, income, and family status had intermediate effects. Furthermore, we predicted probable cognitive stages of AD and analyzed probable causes of these stages using a model of causal and diagnostic reasoning.
CONCLUSION: The BNs model lays the foundation for causal analysis and causal inference of cognitive dysfunction, and the prediction model of cognition in older adults may help the development of strategies to control modifiable risk factors for early intervention in AD.
OBJECTIVE: This study aimed at evaluating the role of socio-demographic variables, lifestyle, and physical characteristics in cognitive decline during AD progression and analyzing the probable causes and predicting stages of the disease.
METHODS: By analyzing data of 301 subjects comprising normal elderly and patients with mild cognitive impairment (MCI) or AD from six communities in Taiyuan, China, we identified the influencing factors during AD progression by a Logistic Regression model (LR) and then assessed the associations between variables and cognition using a Bayesian Networks (BNs) model.
RESULTS: The LR revealed that age, sex, family status, education, income, character, depression, hypertension, disease history, physical exercise, reading, drinking, and job status were significantly associated with cognitive decline. The BNs model revealed that hypertension, education, job status, and depression affected cognitive status directly, while character, exercise, sex, reading, income, and family status had intermediate effects. Furthermore, we predicted probable cognitive stages of AD and analyzed probable causes of these stages using a model of causal and diagnostic reasoning.
CONCLUSION: The BNs model lays the foundation for causal analysis and causal inference of cognitive dysfunction, and the prediction model of cognition in older adults may help the development of strategies to control modifiable risk factors for early intervention in AD.