Association between the blood pressure variability and cognitive decline in Parkinson's disease
Bibliographic record
Abstract
OBJECTIVES: High visit-to-visit blood pressure variability (BPV) was found to be associated with cognitive decline in the elderly. This study aimed to investigate the impact of visit-to-visit BPV on cognition in patients with early-stage Parkinson's disease (PD). DESIGN: This is a retrospective analysis of a prospective cohort. SETTING AND PARTICIPANTS: A total of 297 patients with early-stage PD (103 mild cognitive impairments [PD-MCI] and 194 normal cognitions [PD-NC] at baseline) were included from the Parkinson's Progression Markers Initiative study. METHODS: Variation independent of mean (VIM) of the first year was used as the indicator of BPV. The Montreal Cognitive Assessment (MoCA) was used to assess global cognition. Patients were divided into PD-MCI and PD-NC according to the MoCA score at baseline. Longitudinal cerebrospinal fluid (Aβ-42, Aβ, α-synuclein, neurofilament light protein, tau phosphorylated at the threonine 181 position, total tau, glial fibrillary acidic protein) and serum (neurofilament light protein) biomarkers were assessed. The Bayesian linear growth model was used to evaluate the relationship between baseline BPV and the rate of change in cognition and biomarkers. RESULTS: Higher systolic VIM of the first year was related to a greater rate of decline in MoCA score in the following years in PD-MCI (β = -.15 [95% CI -.29, -.01]). No association was found between BPV and biomarkers. CONCLUSION AND IMPLICATIONS: Higher systolic VIM predicted a steeper decline in cognitive tests in PD-MCI independently from the mean value of blood pressure, orthostatic hypotension, and supine hypertension.
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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 | 0.003 |
| 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 | 0.001 |
| 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".