Untangling the heart–brain connection in Parkinson’s disease: emerging mechanisms and models
Bibliographic record
Abstract
Cardiovascular autonomic dysfunction (CVAD) is a prevalent yet underrecognized nonmotor manifestation of Parkinson's disease (PD) that adversely affects morbidity, prognosis, and quality of life. Framed by the heart-brain axis, this review examines bidirectional interactions between neurodegeneration and cardiovascular control, synthesizing clinical and preclinical evidence from 2015 to 2025. We searched PubMed for English-language studies addressing autonomic involvement in PD and cardiovascular outcomes; of 1035 records identified, more than 240 met inclusion criteria following removal of duplicates, commentaries, and off-topic articles. Consistent clinical observations include orthostatic hypotension, diminished heart rate variability, impaired baroreflex sensitivity, and blood pressure lability, though heterogeneity in acquisition protocols and analytics limits comparability. Experimental models reveal mechanistic leads but often lack integration of central and peripheral endpoints, sex-inclusive cohorts, aging variables, and longitudinal designs. We highlight methodological constraints, particularly the interpretive limits of HRV, anesthesia effects in preclinical work, and inconsistent preprocessing, and outline priorities for standardized, multimodal approaches that couple neural markers with cardiovascular readouts. Advancing an integrative, translational framework may enable earlier diagnosis, robust autonomic biomarkers, risk stratification across "body-first" and "brain-first" trajectories, and targeted interventions, including neuromodulation and metabolic strategies, aimed at mitigating CVAD and potentially modifying PD progression.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".