Associations of Alterations in Pulsatile Arterial Load With Left Ventricular Longitudinal Strain
Bibliographic record
Abstract
BACKGROUND: Increased arterial stiffness leads to increased pulsatile load on the heart. We investigated associations of components of pulsatile load with a measure of left ventricular (LV) systolic function-global longitudinal strain (GLS), in a community-based cohort ascertained based on family history of hypertension. METHODS: Arterial tonometry and echocardiography with speckle tracking were performed in 520 adults with normal LV ejection fraction (EF) (age 67±9 years, 70% hypertensive) to quantify measures of pulsatile load (characteristic aortic impedance (Zc), total arterial compliance (TAC), and augmentation index (AI)) and GLS. The associations of log-Zc, log-TAC, and AI with GLS were assessed using sex-specific z-scores for each measure of arterial load. RESULTS: In univariable analyses, higher Zc was associated with worse GLS (less negative) and higher TAC and AI were associated with better GLS (all P < 0.001). In a multivariable model including age, sex, heart rate (HR), LVEF, mean arterial load (systemic vascular resistance), and measures of pulsatile load, Zc remained associated with GLS (β = 0.28, P < 0.001), while the associations of TAC and AI were no longer significant (P > 0.5). Additional adjustment for cardiovascular risk factors and history of coronary heart disease and stroke did not attenuate the association of Zc with GLS; Zc, sex, HR, LVEF remained associated with GLS after stepwise elimination (all P < 0.001). CONCLUSIONS: Greater proximal aortic stiffness, as manifested by a higher Zc, is independently associated with worse LV longitudinal function.
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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 | 0.001 |
| 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) | 0.001 | 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".