Correlation between biomarkers associated with Alzheimer's disease and the cognitive practice effect Open Access
Ruan, Anyue (Spring 2024)
Abstract
Alzheimer's disease (AD) is an enormous challenge to society and there is a need to improve the understanding of its pathogenesis and to develop effective interventions. Mild cognitive impairment (MCI) is an important prognostic indicator for the development of Alzheimer's disease and provides a window for early intervention. Cognitive tests play a key role in assessing cognitive decline, and biomarkers provide additional information to understand disease progression. This study utilized data from the Emory Healthy Brain Study (EHBS) to explore the correlation between biomarkers and cognitive test scores, focusing on practice effects. Considering demographic factors and short-term training effects, the study used a standardized regression (SRB)-based approach to predict cognitive scores at follow-up visits. Results showed negative correlations between most cognitive tests, cerebrospinal fluid (CSF) protein levels, and cognitive test scores, indicating significant cognitive decline. Participants with poorer initial cognitive performance showed greater changes in scores between visits, suggesting a more pronounced effect on cognition over time. However, the analysis encountered limitations due to the upper limit of scores on the cognitive tests, which may not adequately reflect the cognitive abilities of high performers. Despite these limitations, the findings of this study emphasize the importance of early detection and intervention for cognitive decline, especially for individuals exhibiting MCI biomarkers. Future work includes continually refining the selection of cognitive tests through factor analysis to improve the methodological robustness. In conclusion, this study provides valuable insights into the correlation between AD biomarkers and cognitive functioning, emphasizing the need for comprehensive assessment and individualized interventions in countering AD progression.
Table of Contents
Introduction 1
Background 2
Method 3
Result 5
Discussion 10
Reference 13
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