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Identification and phenotypic profiling of subgroups with distinct cognitive aging trajectories
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DOI：10.1007/s11357-026-02455-w.png)
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Abstract
En 中文
Cognitive aging is shaped by genetic variation, environmental factors, and health-related conditions. Until now, it is largely unclear why some individuals maintain their cognitive function, and others show progressive cognitive decline. This study uncovered determinants of distinct cognitive aging trajectories in the older population. To approach the inter-individual variability in cognitive aging, we clustered n = 696 dementia-free individuals from the prospective TREND study based on their longitudinal changes in comprehensive cognitive testing every 2 years over 13 years on average. Identified subgroups of cognitive aging were tested for differences in general physical health, motor function, mental and neuropsychological health, personality, lifestyle, diet, and genetic and biofluid markers as observed at the phenotypes’ initial records. Comparing the best- and lowest-performing subgroups of cognitive aging, we found that individuals who maintain high cognitive function compared to those with low baseline and progressive cognitive decline showed significant faster gait speed, higher health-related quality of life, did sports and cognitive stimulating activities more frequently, and reported higher plant-based foods intake. Although there are fewer phenotypic differences involving the intermediate subgroups of cognitive aging, the best-performing subgroup compared to all other subgroups showed higher plant-based foods intake. Overall, this study identifies distinct, data-driven subgroups of long-term cognitive aging trajectories and reveals factors associated with these divergent paths using deeply phenotyped data. The findings highlight the substantial heterogeneity of cognitive aging and suggest that favorable trajectories are linked to modifiable behavioral and health-related characteristics, providing a foundation for future multidisciplinary strategies to promote healthy cognitive aging.
Keywords:
Healthy aging
Aging heterogeneity
Cognitive aging
Cognitive maintenance
Trajectory modeling
Longitudinal data analysis
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