Abstract P048: Segment-Specific Pulse Wave Velocity and Subclinical Cardiac Overload and Damage in Older Adults: The Atherosclerosis Risk in Communities (ARIC) Study
Bibliographic record
Abstract
Background: Pulse wave velocity (PWV) independently predicts cardiovascular disease. However, few studies simultaneously explored the associations of segment-specific PWV measures with markers of both cardiac overload (natriuretic peptide [NT-proBNP]) and damage (high-sensitivity cardiac troponin T [hs-cTnT]) among adults without cardiac disease. Methods: We examined 2,845 whites and blacks (67-90 years) without clinical history of cardiac disease during ARIC visit 5 (2011-13). The association of PWV quartiles (cf [carotid-femoral], hc [heart-carotid], hf [heart-femoral], ha [heart-ankle], ba [brachial-ankle], and fa [femoral-ankle]) with log-transformed NT-proBNP and hs-cTnT was evaluated using linear and logistic regression models to adjust for potential confounders. Results: Most PWV measures demonstrated J- or U-shaped associations with NTpro-BNP and hs-cTnT [Figure]. The highest vs. the second lowest quartile of central PWV measures (cfPWV, hfPWV, and hcPWV) was associated with higher levels of NT-proBNP independently of demographic characteristics. The associations were weaker for hs-cTnT. These associations were attenuated after further adjustment, but hcPWV and NT-proBNP remained borderline significant (p=0.069). haPWV, baPWV, and faPWV including peripheral elements had less evident positive associations after adjusting for traditional risk. Conclusion: The positive associations between PWV and cardiac biomarkers were stronger for central vs. peripheral arterial stiffness and for NT-proBNP vs. hs-cTnT among older adults without prevalent cardiac disease. Our findings indicate the relative importance of central arterial stiffness behind subclinical cardiac overload.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".