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入学时的潜在认知类别可预测老年社区样本未来认知衰退的轨迹。

Latent Cognitive Class at Enrollment Predicts Future Cognitive Trajectories of Decline in a Community Sample of Older Adults.

机构信息

Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA.

Department of Psychiatry and Behavioral Sciences, Rush University Medical Center, Chicago, IL, USA.

出版信息

J Alzheimers Dis. 2021;83(2):641-652. doi: 10.3233/JAD-210484.

Abstract

BACKGROUND

Methods that can identify subgroups with different trajectories of cognitive decline are crucial for isolating the biologic mechanisms which underlie these groupings.

OBJECTIVE

This study grouped older adults based on their baseline cognitive profiles using a latent variable approach and tested the hypothesis that these groups would differ in their subsequent trajectories of cognitive change.

METHODS

In this study we applied time-varying effects models (TVEMs) to examine the longitudinal trajectories of cognitive decline across different subgroups of older adults in the Rush Memory and Aging Project.

RESULTS

A total of 1,662 individuals (mean age = 79.6 years, SD = 7.4, 75.4%female) participated in the study; these were categorized into five previously identified classes of older adults differing in their baseline cognitive profiles: Superior Cognition (n = 328, 19.7%), Average Cognition (n = 767, 46.1%), Mixed-Domains Impairment (n = 71, 4.3%), Memory-Specific Impairment (n = 274, 16.5%), and Frontal Impairment (n = 222, 13.4%). Differences in the trajectories of cognition for these five classes persisted during 8 years of follow-up. Compared with the Average Cognition class, The Mixed-Domains and Memory-Specific Impairment classes showed steeper rates of decline, while other classes showed moderate declines.

CONCLUSION

Baseline cognitive classes of older adults derived through the use of latent variable methods were associated with distinct longitudinal trajectories of cognitive decline that did not converge during an average of 8 years of follow-up.

摘要

背景

能够识别认知能力下降不同轨迹的方法对于分离这些分组所依据的生物学机制至关重要。

目的

本研究使用潜在变量方法根据老年人的基线认知特征对其进行分组,并检验这些组在随后的认知变化轨迹上存在差异的假设。

方法

在这项研究中,我们应用时变效应模型(TVEM)来检查 Rush 记忆与衰老项目中不同老年亚组的认知衰退的纵向轨迹。

结果

共有 1662 人(平均年龄 79.6 岁,标准差 7.4,75.4%为女性)参与了研究;这些人分为五个不同的基线认知特征的老年人类别:卓越认知(n=328,19.7%)、平均认知(n=767,46.1%)、混合域损伤(n=71,4.3%)、记忆特异性损伤(n=274,16.5%)和额叶损伤(n=222,13.4%)。在 8 年的随访期间,这五个类别的认知轨迹差异仍然存在。与平均认知类别相比,混合域和记忆特异性损伤类别表现出更陡峭的下降速度,而其他类别则表现出中度下降。

结论

通过使用潜在变量方法得出的老年人基线认知类别与不同的认知衰退纵向轨迹相关,在平均 8 年的随访期间并未趋同。

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