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. 2015 Aug;30(8):824-32.
doi: 10.1002/gps.4221. Epub 2014 Nov 3.

Latent classes of course in Alzheimer's disease and predictors: the Cache County Dementia Progression Study

Jeannie-Marie S Leoutsakos  1 , Sarah N Forrester  1 , Christopher D Corcoran  2   3 , Maria C Norton  2   4   5 , Peter V Rabins  1 , Martin I Steinberg  1 , Joann T Tschanz  2   4 , Constantine G Lyketsos  1
Affiliations

Latent classes of course in Alzheimer's disease and predictors: the Cache County Dementia Progression Study

Jeannie-Marie S Leoutsakos et al. Int J Geriatr Psychiatry. 2015 Aug.

Abstract

Objective: Several longitudinal studies of Alzheimer's disease (AD) report heterogeneity in progression. We sought to identify groups (classes) of progression trajectories in the population-based Cache County Dementia Progression Study (N = 328) and to identify baseline predictors of membership for each group.

Methods: We used parallel-process growth mixture models to identify latent classes of trajectories on the basis of Mini-Mental State Exam (MMSE) and Clinical Dementia Rating sum of boxes scores over time. We then used bias-corrected multinomial logistic regression to model baseline predictors of latent class membership. We constructed receiver operating characteristic curves to demonstrate relative predictive utility of successive sets of predictors.

Results: We fit four latent classes; class 1 was the largest (72%) and had the slowest progression. Classes 2 (8%), 3 (11%), and 4 (8%) had more rapid worsening. In univariate analyses, longer dementia duration, presence of psychosis, and worse baseline MMSE and Clinical Dementia Rating sum of boxes were associated with membership in class 2, relative to class 1. Lower education was associated with membership in class 3. In the multivariate model, only MMSE remained a statistically significant predictor of class membership. Receiver operating characteristic areas under the curve were 0.98, 0.88, and 0.67, for classes 2, 3, and 4 relative to class 1.

Conclusions: Heterogeneity in AD course can be usefully characterized using growth mixture models. The majority belonged to a class characterized by slower decline than is typically reported in clinical samples. Class membership could be predicted using baseline covariates. Further study may advance our prediction of AD course at the population level and in turn shed light on the pathophysiology of progression.

Keywords: Alzheimer; disease course; growth mixture model; trajectory.

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Figures

Figure 1
Figure 1
Fitted and Observed MMSE and CDR-sb1 Trajectories By Class 1CDR-sb has been reverse coded, such that higher scores represent less progression.
Figure 2
Figure 2
ROC Curves for Successive Sets of Predictors of Latent Class Membership
See this image and copyright information in PMC

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