ArticleThe journals of gerontology. Series B, Psychological sciences and social sciences2021
Using Mixture Modeling to Construct Subgroups of Cognitive Aging in the Wisconsin Longitudinal Study.
Article in The journals of gerontology. Series B, Psychological sciences and social sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed, 4 citations in OpenAlex.
- Joint Trajectories of Frailty and Cognitive Function in Older Koreans Based on 14-Year Period Data From Korean Longitudinal Study of Aging (KLoSA).Geriatrics & gerontology international · 2026Article
- The impact of lifestyle factors on trajectories of cognitive subtypes in the older adult population.Scientific reports · 2025Article
- Social and Leisure Activities Predict Transitions in Cognitive Functioning in Older Mexican Adults: A Latent Transition Analysis of the Mexican Health and Aging Study.The journals of gerontology. Series B, Psychological sciences and social sciences · 2023Article
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Authors and funding
3 authors at 2 institutions in 1 country.
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Abstract
objectivesLongitudinal 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.
methodWe 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.
resultsThere 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.
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