Arterial Stiffness and Markers of Atrial Myopathy
Bibliographic record
Abstract
BACKGROUND: Arterial stiffness, measured using carotid-femoral pulse wave velocity (c-f PWV) and heart rate-corrected augmentation index (Aix75), is associated with cardiovascular disease, and in some studies incident atrial fibrillation (AF). In this cross-sectional study, we aimed to investigate whether arterial stiffness is associated with markers of atrial myopathy, which refers to structural and electrical changes in the atria that indicate increased AF risk. METHODS: We included 1050 participants (age 57 ± 4.3 years, 47% males) from the population-based Swedish CArdioPulmonary bioImage Study with c-f PWV and Aix75 data. A random subsample (n = 331) underwent echocardiography. The association between arterial stiffness and atrial myopathy markers was studied using multivariable-adjusted negative binomial regression models for premature atrial complexes (PACs) on 24 h ECG, linear regression for P-wave duration and left atrial volume index (LAVi), and logistic regression models for abnormal P-wave terminal force in V1 (PWTFV1) and P-wave axis. RESULTS: Arterial stiffness was associated with fewer PACs: incidence rate ratio (IRR) 0.45 (95% CI: 0.31 to 0.65, p < 0.001) per 1 m/s increase in c-f PWV and IRR 0.66 (95% CI: 0.49 to 0.89, p = 0.01) per % increase in Aix75. There was no association between arterial stiffness and P-wave indices, OR 1.09 (95% CI: 0.85 to 1.40), p = 0.50 for abnormal PWTFV1, and β -0.003 (-0.10 to 0.09), p = 0.95 for P-wave duration, both per 1 m/s increase in c-f PWV. CONCLUSIONS: Arterial stiffness, measured as either c-f PWV or Aix75, was associated with fewer PACs, whereas no association was found with P-wave indices. The association between arterial stiffness and atrial myopathy is complex and merits further study.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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) | 0.002 | 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".