Determinants of Processing Speed Trajectories among Middle Aged or Older Adults, and Their Association with Chronic Illnesses: The English Longitudinal Study of Aging.
Viktor GkotzamanisGiorgos KoliopanosAlbert Sanchez-NiuboBeatriz OlayaFrancisco Félix CaballeroJosé Luis Ayuso-MateosSomnath ChatterjiJosep Maria HaroDemosthenes B PanagiotakosPublished in: Life (Basel, Switzerland) (2021)
The aim of this study was to identify latent groups of similar trajectories in processing speed through aging, as well as factors that are associated with these trajectories. In the context of the Ageing Trajectories of Health: Longitudinal Opportunities and Synergies (ATHLOS) project, data from the English Longitudinal Study of Aging (ELSA) (n = 12099) were analyzed. Latent groups of similar trajectories in the processing scores as well as their predictors and covariates were investigated, using group-based trajectory models (GBTM). The coefficient estimates for potential group predictors correspond to parameters of multinomial logit functions that are integrated in the model. Potential predictors included sex, level of education, marital status, level of household wealth, level of physical activity, and history of smoking, while time-varying covariates included incidence of cardiovascular disease (CVD), diabetes mellitus, depressive symptoms, and sleep disturbances. Four trajectories were identified and named after their baseline scores and shapes: High (4.4%), Middle/Stable (31.5%), Low/Stable (44.5%), and Low Decline (19.6%). Female sex, higher levels of education, mild level of physical activity, having been married, and higher level of wealth were associated with a higher probability of belonging to any of the higher groups compared to the Low/Decline that was set as reference, while presence of CVD, diabetes mellitus, and depressive symptoms were associated with lower processing speed scores within most trajectories. All the aforementioned factors might be valid targets for interventions to reduce the burden of age-related cognitive impairment.
Keyphrases
- depressive symptoms
- physical activity
- sleep quality
- social support
- healthcare
- cardiovascular disease
- quality improvement
- cognitive impairment
- public health
- body mass index
- middle aged
- magnetic resonance imaging
- mass spectrometry
- computed tomography
- adipose tissue
- social media
- machine learning
- health information
- cross sectional
- magnetic resonance
- drug induced
- electronic health record