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. 2021 Sep 13;76(8):1512-1522.
doi: 10.1093/geronb/gbaa191.

Using Mixture Modeling to Construct Subgroups of Cognitive Aging in the Wisconsin Longitudinal Study

Sara M Moorman  1 , Emily A Greenfield  2 , Kyle Carr  1
Affiliations

Using Mixture Modeling to Construct Subgroups of Cognitive Aging in the Wisconsin Longitudinal Study

Sara M Moorman et al. J Gerontol B Psychol Sci Soc Sci. .

Abstract

Objectives: Longitudinal surveys of older adults increasingly incorporate assessments of cognitive performance. However, very few studies have used mixture modeling techniques to describe cognitive aging, identifying subgroups of people who display similar patterns of performance across discrete cognitive functions. We employ this approach to advance empirical evidence concerning interindividual variability and intraindividual change in patterns of cognitive aging.

Method: We drew upon data from 3,713 participants in the Wisconsin Longitudinal Study (WLS). We used latent class analysis to generate subgroups of cognitive aging based on assessments of verbal fluency and episodic memory at ages 65 and 72. We also employed latent transition analysis to identify how individual participants moved between subgroups over the 7-year period.

Results: There were 4 subgroups at each point in time. Approximately 3 quarters of the sample demonstrated continuity in the qualitative type of profile between ages 65 and 72, with 17.9% of the sample in a profile with sustained overall low performance at both ages 65 and 72. An additional 18.7% of participants made subgroup transitions indicating marked decline in episodic memory.

Discussion: Results demonstrate the utility of using mixture modeling to identify qualitatively and quantitatively distinct subgroups of cognitive aging among older adults. We discuss the implications of these results for the continued use of population health data to advance research on cognitive aging.

Keywords: Alzheimer’s disease and related dementias; Episodic memory; Life course; Mixture models; Verbal fluency.

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Figures

Figure 1.
Figure 1.
Probability of top-quartile cognitive test scores conditional on latent class membership (Wisconsin Longitudinal Study, N = 3,713).Note: Absent bars indicate a 0% probability of scoring in the top quartile of the respective test.
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