Reduced Complexity of Pulse Rate Is Associated With Faster Cognitive Decline in Older Adults
Bibliographic record
Abstract
BACKGROUND: Cardiovascular diseases are closely linked to cognitive health. Subclinical cardiovascular functional changes, such as cardiac autonomic dysfunction, precede cardiovascular diseases and improve risk stratification. Continuous monitoring of heart rate or pulse rate is a commonly used approach to evaluate cardiac cycle and autonomic regulation. We investigated whether the complexity of pulse rate is associated with longitudinal cognitive decline in older adults. METHODS: Overnight pulse oximetry data were collected from 503 participants (mean age=82±7 [SD] years, 76% female). We used a previously established distribution entropy algorithm to extract the complexity of pulse rate as a proxy for subclinical cardiovascular function. Participants completed a standardized cognitive test battery during the same visit of pulse oximetry and at least 1 follow-up visit. Linear mixed-effects models were conducted to test whether distribution entropy is associated with longitudinal changes in global cognition and separately, in 5 cognitive domains. RESULTS: Greater distribution entropy (ie, better complexity) was associated with a slower decline in global cognition; the effect of 1-SD increase in distribution entropy was equivalent to being approximately 3 years younger. No associations were observed between conventional time- or frequency-domain pulse rate variability measures and cognitive changes. CONCLUSIONS: Higher complexity of pulse rate is linked with slower cognitive decline in older adults. Future studies should test whether complexity is also associated with future risks of neurodegenerative disorders, such as dementia, and further elucidate the causal directions.
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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 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".