Reduced Heart Rate Variability Is Associated With Worse Cognitive Performance in Elderly Mexican Americans
Bibliographic record
Abstract
Reduced heart rate variability is a strong predictor of cardiovascular risk factors, cardiovascular events, and mortality and thus may be associated with cognitive neurodegeneration. Yet, this has been relatively unexplored, particularly in minority populations with high cardiovascular burden. We used data from the Sacramento Area Latino Study on Aging to examine the cross-sectional association of reduced heart rate variability with cognitive function among elderly Mexican Americans. A total of 869 participants (mean age, 75 years; 59% women) had their 6-minute heart rate variability measured using an ECG monitor and respiration pacer in response to deep breathing. We used the mean circular resultant, known as R bar, as a measure of heart rate variability and categorized it into quartiles (Q1 to Q4 of R bar: reduced to high heart rate variability). Cognitive function was assessed using the modified Mini-Mental State Examination, a 100-point test of global cognitive function, and the Spanish and English verbal learning test, a 15-point test of verbal memory recall. In fully adjusted linear regression models, participants in quartile 1 had a 4-point lower modified Mini-Mental State Examination score (P<0.01), those in quartile 2 had a 2-point lower score (P=0.04), and those in quartile 3 had a 1-point lower score (P=0.35) compared with those in the highest quartile of R bar. Reduced R bar was not associated with verbal memory. Our results suggest that reduced heart rate variability is associated with worse performance on the test of global cognitive function, above and beyond traditional cardiovascular risk factors.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".