Impaired dynamic cerebral autoregulation: A potential mechanism of orthostatic hypotension and dementia in Parkinson’s disease
Bibliographic record
Abstract
Background Orthostatic hypotension (OH) and cognitive impairment are common non-motor symptoms of Parkinson’s disease (PD). This study aimed to investigate whether impaired dynamic cerebral autoregulation (dCA) is associated with OH and Parkinson’s disease dementia (PDD), and analyze the related risk factors in patients with PDD. Materials and methods We enrolled 89 patients with PD and 20 age- and sex-matched healthy controls (HCs). Cognition and different cognitive domains were assessed by the Montreal Cognitive Assessment scale. Non-invasive continuous beat-to-beat blood pressure and cerebral blood flow velocity were assessed using a servo-controlled finger plethysmograph and transcranial Doppler, respectively. dCA was examined using supine and orthostatic changes with transfer function analysis to derive the autoregulatory parameters of phase, gain, and coherence. Logistic regression analysis was performed to determine the risk factors for PDD. Results We found that 21 (23.6%) patients with PD had OH. These patients showed worse cognitive performance in specific cognitive tasks, such as language and orientation. The patients with OH also had poorer dCA; the very low frequency (VLF) phase in two different postures was lower than that in patients without OH as well as HCs (both P < 0.05). And the normalized gain in the VLF and low frequency (LF) in standing position was higher in PD patients with and without OH than in HCs. PDD patients also had significantly higher LF normalized gain when standing than patients without dementia (P = 0.015), indicating impaired dCA. LF normalized gain in standing (odds ratio: 3.756, 95% confidence interval: 1.241–11.367) and education were significantly associated with PDD. Conclusion Diminished dCA may represent a potential mechanism for OH and cognitive impairment and low educational level might be a significant factor contributing to the increased risk of PDD.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".