Youthful Processing Speed in Older Adults: Genetic, Biological, and Behavioral Predictors of Cognitive Processing Speed Trajectories in Aging - Bott, Nicholas T.; Bettcher, Brianne M.; Yokoyama, Jennifer S.; Frazier, Darvis T.; Wynn, Matthew; Karydas, Anna; Yaffe, Kristine; Kramer, Joel H. (Frontiers in Aging Neuroscience, 2017)
Objective: To examine the impact of genetic, inflammatory, cardiovascular, lifestyle, and neuroanatomical factors on cognitive processing speed (CPS) change over time in functionally intact older adults. Methods: This was an observational study conducted over two time points, included 120 community dwelling cognitively normal older adults between the ages of 60 and 80 from the University of California San Francisco Memory and Aging Center. Participants were followed with composite measures of CPS, calculated based on norms for 20-30 year-olds. Variables of interest were AD risk genes (APOE, CR1), markers of inflammation (interleukin 6) and cardiovascular health (BMI, LDL, HDL, mean arterial pressure, fasting insulin), self-reported physical activity, and corpus callosum (CC) volumes. The sample was divided into three groups: 17 "resilient-agers" with fast and stable processing speed; 56 "average-agers" with average and stable processing speed; and 47 "sub-agers" with average baseline speed who were slower at follow-up. Results: Resilient-agers had larger baseline CC volumes than sub-agers (p < .05). Resilient-agers displayed lower levels of interleukin-6 (IL-6) and insulin (ps < .05) than sub-agers, and reported more physical activity than both average- and sub-agers (ps < .01). In a multinomial logistic regression, physical activity and IL-6 predicted average- and sub-ager groups. Resilient-agers displayed a higher frequency of APOE e4 and CR1 AA/AG alleles. Conclusions: Robust and stable CPS is associated with larger baseline CC volumes, lower levels of inflammation and insulin, and greater self-reported physical activity. These findings highlight the relevance of neuroanatomical, biological, and lifestyle factors in the identification and prediction of heterogeneous cognitive aging change over time.