Introduction: It is valuable to identify common latent cognitive constructs for dementia prevalence estimation across Chinese aging cohorts.
Methods: Based on cognitive measures of 12015 Chinese Longitudinal Healthy Longevity Survey (CLHLS; 13 items) and 6623 China Health and Retirement Longitudinal Study (CHARLS; 9 items) participants aged 65 to 99 in 2018, confirmatory factor analysis was applied to identify latent cognitive constructs, and to estimate dementia prevalence compared to Mini-Mental State Examination (MMSE) and nationwide estimates of the literature.
Results: A common three-factor cognitive construct of orientation, memory, and executive function and language was found for both cohorts with adequate model fits. Crude dementia prevalence estimated by factor scores was similar to MMSE in CLHLS, and was more reliable in CHARLS. Age-standardized dementia estimates of CLHLS were lower than CHARLS among those aged 70+, which were close to the nationwide prevalence reported by the COAST study and Global Burden of Disease.
Discussion: We verified common three-factor cognitive constructs for both cohorts, providing an approach to estimate dementia prevalence at the national level.
Highlights: Common three-factor cognitive constructs were identified in Chinese Longitudinal Healthy Longevity Survey (CLHLS) and China Health and Retirement Longitudinal Study (CHARLS).Crude dementia estimates using factor scores were reliable in both cohorts.Estimates of CHARLS were close to current evidence, but higher than that of CLHLS.
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http://dx.doi.org/10.1002/dad2.12356 | DOI Listing |
Alzheimers Dement
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Xuanwu Hospital, Capital Medical University, Beijing, Beijing, China.
Background: Effective early intervention of mild cognitive impairment (MCI) is the key for preventing dementia. However, there is currently no drug for MCI. As a multi-targeted neuroprotective agent, butylphthalide has been demonstrated to repair cognition in patients with vascular cognitive impairment, and has the potential to treat MCI due to Alzheimer's disease (AD).
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December 2024
Relecura, Bangalore, karnataka, India.
Background: Clinical Dementia Rating (CDR) and its evaluation have been important nowadays as its prevalence in older ages after 60 years. Early identification of dementia can help the world to take preventive measures as most of them are treatable. The cellular Automata (CA) framework is a powerful tool in analyzing brain dynamics and modeling the prognosis of Alzheimer's disease.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA.
Background: The prohibitive costs of drug development for Alzheimer's Disease (AD) emphasize the need for alternative in silico drug repositioning strategies. Graph learning algorithms, capable of learning intrinsic features from complex network structures, can leverage existing databases of biological interactions to improve predictions in drug efficacy. We developed a novel machine learning framework, the PreSiBOGNN, that integrates muti-modal information to predict cognitive improvement at the subject level for precision medicine in AD.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Camden and Islington NHS Foundation Trust, London, United Kingdom; University College London, London, United Kingdom.
Background: Long-term care (LTC) home residents may be isolated or lonely. Social connection is important for their physical, mental and cognitive health, quality of life and care. However, measuring social connection in LTC residents is challenging and there are no existing measures with adequately established psychometric properties.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Central South University, Changsha, Hunan, China.
Background: This prediction model quantifies the risk of cognitive impairment. This aim of this study was to develop and validate a prediction model to calculate the 6-year risk of cognitive impairment.
Methods: Participants from the Chinese Longitudinal Healthy Longevity Survey (CLHLS) 2008-2014 and 2011-2018 surveys were included for developing the cognitive impairment prediction model.
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