The influence of baroreflex sensitivity on ambulatory arterial stiffness index in individuals with cardiovascular risk
Bibliographic record
Abstract
OBJECTIVES: Although the predictor role of ambulatory arterial stiffness index (AASI) for cardiovascular risk has been shown, the question of whether AASI is a measure of arterial stiffness or, in fact, reflects influence of other origins on the arterial tree remains to be determined. We sought to elucidate whether AASI is associated with baroreflex sensitivity (BRS) or blood pressure (BP) reactivity to stress in individuals with cardiovascular risk. METHODS: A total of 120 participants were included in this study. Ambulatory BP measurements were performed over a 24-h period. AASI was calculated as 1 minus the regression slope of diastolic on systolic BP obtained from 24-h ambulatory BP monitoring data. BRS was estimated from the spontaneous changes in systolic BP and corresponding interbeat heart rate intervals. BP reactivity was induced by three stressors including the Stroop Color and Word Test, anger recall interview, and handgrip exercise. Arterial stiffness was determined by the pulse wave velocity. RESULTS: AASI significantly and inversely correlated to resting BRS (r=-0.24, P=0.01) and BRS under stress (r=-0.33, P=0.02) but not with systolic blood pressure reactivity (P=0.92) or pulse wave velocity (P=0.53). Adjusting for possible confounders, BRS independently predicted AASI (P=0.01). CONCLUSION: Increased AASI is associated with reduced BRS. Decreased BRS may at least in part explain the prognostic role of AASI in predicting cardiovascular risk. Our findings add support to the clinical significance of AASI. In particular, AASI may aid in the detection of cardiovascular risk.
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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 | 0.000 |
| 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".