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Background: We investigated whether aspects of subjective cognitive aging, including awareness of age-related gains and losses in cognition (AARC-gains, AARC-losses) and subjective cognitive decline (SCD), predict change in objective cognitive function as measured by verbal reasoning (VR) and working memory (WM). Methods: We used longitudinal data for 3,299 cognitively healthy UK residents aged 65+. We used data on AARC and SCD assessed in 2019, and cognitive tasks assessed in 2019, 2020, and 2021. We used latent growth curve modeling, latent class growth analysis, and growth mixture modeling. Results: For VR, multiple growth trajectories were not evident. Mean VR at baseline was 37.45; this remained stable over time. Higher AARC-gains in cognition (mean intercept = -0.23; 95%CI: -0.31; -0.16), higher AARC-losses in cognition (mean intercept = -0.37; 95%CI: -0.46; -0.28), and lower SCD (mean intercept = 2.92; 95%CI: 2.58; 3.58) were associated with poorer VR at baseline. A three-class growth mixture model--class varying best represented trajectories of WM. In Class 1 (N = 182) mean WM at baseline was 31.20; this decreased by 2.48 points each year. In Class 2 (N = 119) mean WM at baseline was 23.12; this increased by 3.28 points each year. In Class 3 (N = 2,998) mean WM at baseline was 30.11; and it remained stable. Higher AARC-gains (Odds Ratio = 1.08; 95%CI: 1.03; 1.14) and AARC-losses (Odds Ratio = 1.10; 95%CI: 1.04; 1.16) in cognition predicted greater likelihood of being in Class 2 than Class 3. Conclusion: Although both higher AARC-gains and AARC-losses indicate poorer concurrent cognition, higher AARC-gains may be a resource that facilitates future cognitive improvement. [ABSTRACT FROM AUTHOR]

Objective: Amyotrophic lateral sclerosis (ALS) is a multi-system disorder characterized primarily by motor neuron degeneration, but may be accompanied by cognitive dysfunction. Statistically appropriate criteria for establishing cognitive impairment (CI) in ALS are lacking. We evaluate quantile regression (QR), that accounts for age and education, relative to a traditional two standard deviation (SD) cutoff for defining CI. Methods: QR of cross-sectional data from a multi-center North American Control (NAC) cohort of 269 healthy adults was used to model the 5th percentile of cognitive scores on the Edinburgh Cognitive and Behavioral ALS Screen (ECAS). The QR approach was compared to traditional two SD cutoff approach using the same NAC cohort (2SD-NAC) and to existing UK-based normative data derived using the 2SD approach (2SD-UK) to assess the impact of cohort selection and statistical model in identifying CI in 182 ALS patients. Results: QR-NAC models revealed that age and education impact cognitive performance on the ECAS. Based on QR-NAC normative cutoffs, the frequency of CI in the 182 PENN ALS patients was 15.9% for ALS specific, 12.6% for ALS nonspecific, and 15.4% for ECAS total. This frequency of CI is substantially more conservative in comparison to the 2SD-UK (20.3%–34.6%) and modestly more conservative to the 2SD-NAC (14.3%–16.5%) approaches for estimating CI. Conclusions: The choice of normative cohort has a substantial impact and choice of statistical method a modest impact on defining CI in ALS. This report establishes normative ECAS thresholds to identify whether ALS patients in the North American population have CI. [ABSTRACT FROM AUTHOR]