Affiliations: [a] Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| [b] Section of Geriatrics, Department of Medicine, Boston University School of Medicine, Boston, MA, USA
| [c] The Danish Aging Research Center and The Danish Twin Registry, Epidemiology, Biostatistics and Biodemography, Department of Public Health, University of Southern Denmark, Odense, Denmark
| [d] Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, MA, USA
Correspondence:
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Correspondence to: Qingyan Xiang, PhD candidate, Department of Biostatistics, Boston University, 801 Massachusetts Avenue, Boston, MA 02118, USA. E-mail: [email protected].
Abstract: Background:Discovering patterns of cognitive domains and characterizing how these patterns associate with other risk factors and biomarkers can improve our understanding of the determinants of cognitive aging. Objective:To discover patterns of cognitive domains using neuropsychological test results in Long Life Family Study (LLFS) and characterize how these patterns associate with aging markers. Methods:5,086 LLFS participants were administered neuropsychological tests at enrollment. We performed a cluster analysis of six baseline neuropsychological test scores and tested the association between the identified clusters and various clinical variables, biomarkers, and polygenic risk scores using generalized estimating equations and the Chi-square test. We used Cox regression to correlate the clusters with the hazard of various medical events. We investigated whether the cluster information could enhance the prediction of cognitive decline using Bayesian beta regression. Results:We identified 12 clusters with different cognitive signatures that represent profiles of performance across multiple neuropsychological tests. These signatures significantly correlated with 26 variables including polygenic risk scores, physical and pulmonary functions, and blood biomarkers and were associated with the hazard of mortality (p < 0.01), cardiovascular disease (p = 0.03), dementia (p = 0.01), and skin cancer (p = 0.03). Conclusion:The identified cognitive signatures capture multiple domains simultaneously and provide a holistic vision of cognitive function, showing that different patterns of cognitive function can coexist in aging individuals. Such patterns can be used for clinical intervention and primary care.
