Usefulness of Serum Testosterone Concentration and Skin Autofluorescence as Coronary Risk Markers in Male Patients With Type 2 Diabetes Mellitus
Bibliographic record
Abstract
Background: No studies have reported simultaneous evaluation of the two coronary risk markers of testosterone and skin autofluorescence (SAF) as a marker of advanced glycation end products in patients with type 2 diabetes mellitus (T2DM) at present. This study aimed to clarify the clinical significance of both indicators as risk markers of coronary artery disease (CAD), including the association and background factors between testosterone and SAF in male patients with T2DM. Methods: This study enrolled 162 male patients with T2DM (CAD: n = 35). Testosterone was evaluated by serum total testosterone concentration (T-T). Various analyses related to T-T and SAF as coronary risk markers were performed. Results: T-T was significantly lower, and SAF was significantly higher in patients with CAD than in patients with non-CAD. A significant negative correlation was found between T-T and SAF (r = -0.45, P < 0.001), and the correlation was stronger in patients with CAD than in patients with non-CAD (non-CAD, r = -0.27, P = 0.003; CAD, r = -0.51, P < 0.001). However, both T-T and SAF had significant associations with triglyceride-glucose index as an insulin resistance marker and cardio-ankle vascular index as an arterial function marker. Multiple regression analysis revealed that both T-T and SAF were selected as independent variables to the presence of CAD as a dependent variable. However, the odds ratio increased due to the merger of two coronary risk markers, low T-T and high SAF (odds ratio: one risk marker: 3.24, 95% confidence interval: 1.01 - 10.50, P = 0.045; two risk markers: 13.22, 95% confidence interval: 3.41 - 39.92, P < 0.001). Conclusions: The results of this cross-sectional study indicate that T-T and SAF are closely related in CAD patients with T2DM. It also shows that insulin resistance and arterial dysfunction are in the background of both indicators. Additionally, not only are both indicators independent coronary risk markers, but the overlap of both indicators increases their weight as coronary risk markers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".