ArticleFrontiers in aging neuroscience2023
Classification and prediction of cognitive trajectories of cognitively unimpaired individuals.
Article in Frontiers in aging neuroscience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Biomarker-integrated prognostic stagings for Alzheimer's Disease.Nature communications · 2026Article
- Difference in trajectories according to early amyloid accumulation in cognitively unimpaired elderly.European journal of neurology · 2024Article
- A Subtype Perspective on Cognitive Trajectories in Healthy Aging.Brain sciences · 2024Article
- Characterizing the clinical heterogeneity of early symptomatic Alzheimer's disease: a data-driven machine learning approach.Frontiers in aging neuroscience · 2024Article
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9 authors.
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Abstract
Objectives: Efforts to prevent Alzheimer's disease (AD) would benefit from identifying cognitively unimpaired (CU) individuals who are liable to progress to cognitive impairment. Therefore, we aimed to develop a model to predict cognitive decline among CU individuals in two independent cohorts. Methods: A total of 407 CU individuals from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and 285 CU individuals from the Samsung Medical Center (SMC) were recruited in this study. We assessed cognitive outcomes by using neuropsychological composite scores in the ADNI and SMC cohorts. We performed latent growth mixture modeling and developed the predictive model. Results: Growth mixture modeling identified 13.8 and 13.0% of CU individuals in the ADNI and SMC cohorts, respectively, as the "declining group." In the ADNI cohort, multivariable logistic regression modeling showed that increased amyloid-β (Aβ) uptake (β [SE]: 4.852 [0.862], Conclusion: Our study provides novel insights into the cognitive trajectories of CU individuals. Furthermore, the predictive model can facilitate the classification of CU individuals in future primary prevention trials.
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