Relationship Between Serum Total Testosterone Concentration and Augmentation Index at Radial Artery in Japanese Postmenopausal Patients
Bibliographic record
Abstract
Background: The significance of testosterone as a risk factor for cardiovascular disease (CVD) in females is controversial. This cross-sectional study aimed to elucidate the relationship between serum total testosterone concentration (T-T) and augmentation index at the radial artery (r-AIx) as a marker of arterial function in Japanese postmenopausal patients. Methods: A total of 447 postmenopausal patients with traditional cardiovascular risk factors and/or a history of CVD (age (mean ± standard deviation (SD)), 73 ± 10 years) were enrolled. r-AIx was measured using tonometry, and the association between r-AIx and various clinical parameters, including T-T, was determined. Results: r-AIx significantly increased (CVD vs. non-CVD: 99±11% vs. 91±11%, P < 0.001) and T-T significantly decreased (CVD vs. non-CVD: 0.31 ± 0.13 ng/mL vs. 0.49 ± 0.23 ng/mL, P < 0.001) in patients with CVD than in those without CVD. A significant negative correlation (r = -0.48; P < 0.001) between r-AIx and T-T was observed. Furthermore, multiple regression analysis indicated that T-T (t value = -7.7; P < 0.001), height (t value = -5.3; P < 0.001), d-ROMs test as a marker of oxidative stress in vivo (t value = 3.2; P < 0.001), CVD (t value = 2.9; P < 0.01), and pulse rate (t value = -2.7; P < 0.01) were independent variables for r-AIx as a subordinate factor. Conclusion: This study revealed that low T-T is an important determining factor for an increase in r-AIx in Japanese postmenopausal patients. A prospective multicenter study with a large sample size is required to confirm the results of this study. J Clin Med Res. 2017;9(10):872-878 doi: https://doi.org/10.14740/jocmr3164w
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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 | 0.000 | 0.001 |
| 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".