Relationships Between Arterial Pressure-Volume Index and Cardiovascular Disease Biomarkers in Patients With Hypertension
Bibliographic record
Abstract
Background: The arterial pressure-volume index (API), which is obtained by conventional blood pressure measurement, is a new marker for arterial stiffness. The aim of this study was to clarify the relationships between the API and various clinical parameters, including cardiovascular disease (CVD) biomarkers, in patients with hypertension for the prevention of CVD. Methods: This cross-sectional study enrolled 288 patients with hypertension receiving pharmacological treatment, without a history of CVD (males/females: 115/173; age: 63 ± 11 years (mean ± standard deviation)). The API was automatically calculated using a commercial device. Results: The API was significantly correlated with important CVD biomarkers, such as the concentration of urinary albumin (r = 0.42, P < 0.001), high-sensitivity troponin T (r = 0.39, P < 0.001), and skin autofluorescence (marker of advanced glycation end products in tissues) (r = 0.41, P < 0.001). Multiple regression analyses demonstrated that when the API was used as a subordinate factor, these biomarkers were independent variables. According to the receiver operating characteristic curve analysis, an API of > 26 is the optimal cut-off point for determining albuminuria as ≥ 30 mg/g Cr, high high-sensitivity cardiac troponin T concentration as ≥ 0.014 ng/mL, or high skin autofluorescence as ≥ 3.0 arbitrary unit (area under the curve = 0.703, 0.702, and 0.704; and P < 0.001, respectively). Conclusion: This investigation demonstrates that API had an independent relationship with relevant CVD biomarkers, such as urinary albumin, high-sensitivity troponin T, and skin autofluorescence. Additionally, the outcomes of receiver operating characteristic curve analysis are presented as values that an API > 26 defines for these biomarkers linked with the formation of CVD.
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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 | 0.001 | 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) | 0.001 | 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".