Relationship of Selected Adipokines with Markers of Vascular Damage in Patients with Type 2 Diabetes
Bibliographic record
Abstract
BACKGROUND: In this study we compared levels of selected adipokines between patients with type 2 diabetes (T2D) and healthy individuals and we determined their relationship with early vascular damage markers. METHODS: Seventy-seven subjects: 56 patients with T2D (34 men and 22 women) and 21 healthy controls (8 men and 13 women) were examined in this cross-sectional study. Selected adipokines [adiponectin, adipocyte fatty acid-binding protein (A-FABP), fibroblast growth factor 21 (FGF-21), C1q/TNF-related protein 9 (CTRP-9), and allograft inflammatory factor-1 (AIF-1)] with possible cardiovascular impact were measured in all participants. To identify markers of vascular damage von Willebrand factor (vWF), plasminogen activator inhibitor-1 (PAI-1) and arterial stiffness parameters were examined in all the subjects. RESULTS: When compared with healthy controls, T2D had significantly higher levels of A-FABP [50.0 (38.1-68.6) vs. 28.6 (23.6-32.9) ng/mL, P < 0.0001] and lower levels of adiponectin [5.9 (4.3-9.0) vs. 11.3 (8.7-14.8) μg/mL, P < 0.0001]. Differences in other adipokines were not statistically significant. Adiponectin level correlated negatively with vWF levels (ρ = -0.29, P < 0.05) and PAI-1 (ρ = -0.36, P < 0.05) and A-FABP positively with vWF (ρ = 0.61, P < 0.05) and PAI-1 (ρ = 0.47, P < 0.05) and augmentation index (ρ = 0.26, P < 0.05). Multivariate regression analysis showed independent association between A-FABP and vWF (b = 0.24, P < 0.05). CONCLUSIONS: Patients with T2D have significantly higher levels of A-FABP and lower levels of adiponectin. These adipokines correlate with indicators of vascular damage and could contribute to cardiovascular risk in patients with T2D. A-FABP may participate in direct endothelium damage.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".