Cognitive Reserve and Gait Decline Across the Parkinson’s Spectrum: Evidence from PPMI Longitudinal Cohorts
Bibliographic record
Abstract
Abstract Background Cognitive reserve has been linked to slower decline in mobility and daily function in Parkinson’s disease (PD), but its role across the prodromal-to-diagnosed spectrum, and it shifts over time, remains unclear. Methods We analyzed longitudinal data from the Parkinson’s Progression Markers Initiative (PPMI; baseline N = 3,971; Mage = 62.4 years, 42% female) across four annual visits. Participants included healthy controls (HC; n = 331), prodromal subgroups (hyposmia, RBD, LRRK2 carriers; n = 2,190), and early PD (≤ 2 years from diagnosis; n = 1,450). Mixed-effects models tested between-person (average Montreal Cognitive Assessment [MoCA]) and within-person (time-specific deviations) effects on gait impairment (PIGD), controlling for age, sex, and education. Results All groups showed significant gait decline over four years. Higher between-person MoCA predicted slower decline, whereas within-person deviations did not. Subgroup analyses revealed the strongest protective effect in LRRK2 carriers, with weaker effects in hyposmia and RBD. In PD, the reserve effect was attenuated but remained present. Growth models showed significant slope-slope coupling between cognition and gait decline (r = -.24, p < .01), evident when four visits were modeled. Conclusions Cognitive reserve helps preserve mobility even before PD diagnosis, but once PD manifests, cognitive and motor decline become closely intertwined This stage-specific shift marks the point where resilience gives way to vulnerability, aligning with biopsychosocial and mind-body frameworks that guide intervention in aging and neurodegeneration. Clinically, findings underscore stratified prevention that harnesses reserve before diagnosis and multimodal interventions that address cognitive–motor interdependence once PD manifests.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".