Reduced beat‐to‐beat blood pressure variability is associated with cognitive decline over six years in the Malaysian Elders Longitudinal Research (MELoR) study
Bibliographic record
Abstract
Abstract Background Blood pressure variability (BPV) is defined as fluctuations in blood pressure (BP) from visit‐to‐visit, day‐to‐day, hour‐to‐hour and beat‐to‐beat. While published evidence on visit‐to‐visit BPV have suggested an association between BPV and cognitive decline, but published research on beat‐to‐beat BPV and cognitive decline remains limited. This prospective study, therefore, aimed to determine the relationship between beat‐to‐beat BPV and changes in cognitive performance over a six‐year period. Method The first and fourth wave data from Malaysian Elders Longitudinal Research (MELoR) study, which included community dwellers aged 55 years and over at recruitment, were utilized. Non‐invasive continuous beat‐to‐beat BP was recorded at baseline using the vascular unloading method (TaskforceTM,CNSystems). Systolic (SBPV) and diastolic (DBPV) BPV were calculated using standard deviation (SD), coefficient of variation (CV) and average real variability (ARV), which were then dichotomized using median cut‐offs. The Montreal Cognitive Assessment (MoCA) Blind instrument was utilized to allow for telephone‐based assessments at Wave 4 which occurred during the COVID‐19 pandemic. Cognitive decline was defined as 2 points reduction in MoCA. Result Data were available from492 participants, mean (SD) age = 67.8 (6.8) years. Mean MoCA‐blinds scores had increased from 17.1 (3.0) to 18.6 (2.7) from baseline to follow‐up. Cognitive decline was observed in 65 (13%). Individuals with cognitive decline had lower depression scores at baseline . Lower SD‐DBPV (mean difference, MD (95%CI) = 0.56 (0.31,0.98) and CV‐DBPV (MD (95%CI) = 0.51 (0.28,0.91) were significantly increased cognitive change after adjustment for potential co‐founders. Conclusion Lower BPV was associated with increased risk of cognitive decline in our population‐based study, which conflicted with available studies using primarily visit‐to‐visit and ambulatory BP measurements and involving mainly hospital‐based populations. Beat‐to‐beat BPV may be a measure of arterial stiffness which may is associated with increased risk of vasculopathy and development cognitive impairment. Future studies should determine whether interventions which lead to increased beat‐to‐beat BPV could reduce cognitive decline.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".