Exceptional Aging: Cognitive and Brain Health in Super Movers
Bibliographic record
Abstract
Abstract Super movers, individuals aged 80 and older with gait speeds ≥1.5 SD above age- and sex-adjusted means, represent an exceptional aging phenotype and may offer insights into resilience against cognitive decline. We examined their risk of incident cognitive impairment, cognitive trajectories and brain health using data from 1) five Health and Retirement Study International Network of Studies (HRS-INS) 2) the LonGenity study and 3) the RUSH Memory Aging Project (RUSH MAP). In HRS-INS we assessed incident cognitive impairment (>1.5 SD below age-adjusted cognitive test means plus impaired Instrumental Activities of Daily Living) of super movers and conduct a meta-analysis using age- and sex-adjusted hazard ratios (HR) from Cox models of individual studies. LonGenity study data were used to model longitudinal cognitive decline using linear mixed-effects models (adjusted for age, sex, education, and parental longevity) and to compare cortical thickness and hippocampal subfield volumes between super versus non-super movers. RUSH MAP data assessed dementia-related pathology in super movers. In pooled HRS-INS data (n = 358/3,989 super movers; baseline age 83.6–84.4 years; follow-up 3.8–6.1 years), super movers had a lower risk of cognitive impairment (HR 0.50, 95% CI 0.29–0.71). In LonGenity (n = 197; mean age 84.6, SD 3.3), super movers exhibited slower decline in memory and non-memory domains and preserved hippocampal subfield volumes. In RUSH MAP, they had better late-life cognition, despite no differences in dementia-related pathology. Understanding the behavioral and biological traits of super movers may reveal protective mechanisms against cognitive decline and dementia to inform future interventions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".