Cardiac autonomic function during exercise and incident Parkinson’s disease
Bibliographic record
Abstract
Abstract Objective To determine whether established parameters of cardiac autonomic function are associated with incident Parkinson’s disease, independent of clinical characteristics, and established autonomic prodromal features. Methods Population-based cohort study of UK Biobank participants who performed a standardized bicycle exercise test (2009-2013), followed until November 2022, and analyzed in January 2024. Heart rate increase from rest to exercise, and the decrease in heart rate from peak exercise to recovery were extracted and associated with incident Parkinson’s disease. Associations were adjusted using multivariable models consisting of clinical characteristics only and combined with prodromal autonomic features. Results 69,288 eligible participants (male 48%, mean age 56.8 [SD 8.2]) were followed for 12.5 years (median; IQR 0.3): 319 (0.5%) developed Parkinson’s disease. Median lag time to diagnosis was 9.3 years (IQR 4.4). Both heart rate increase (37.5 [SD 11.5] vs 40.8 [SD 12.4] beats/min, p < 0.001) and recovery (23.4 [SD 8.8] vs. 27.8 [SD 10.3] beats/min, p < 0.001) were significantly lower in incident cases compared to controls. After adjusting for prodromal clinical and autonomic features, heart rate recovery was independently associated with incident Parkinson’s disease, while heart rate increase was not. Specifically, a blunted heart rate lowering during recovery was associated with a 30% higher risk of incident Parkinson’s disease (HR: 1.3; 95% CI 1.1-1.4; p < 0.001 per 10 beats less recovery) Interpretation These findings suggest that cardiac autonomic dysfunction precedes clinically manifest Parkinson’s disease, and that heart rate recovery might serve as a quantitative prodromal marker.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".