A16521 Serum Advanced glycation end-products are associated with nutrition status but not aortic stiffness in end-stage renal disease
Bibliographic record
Abstract
Objectives: Advanced glycation end-products (AGEs) are a heterogeneous group of uremic toxins, which are acquired via endogenous pathways and through absorption of AGE-modified peptides found in food. Through modification of extracellular structural proteins of the arterial wall, AGEs can lead to vascular stiffness. In this study, we aimed to examine the association of serum fluorescent AGEs with aortic stiffness (AS), tissue AGEs and protein intake in a cohort of subjects undergoing chronic hemodialysis. Methods: In 166 subjects with a mean age of 65 ± 15, 55% male, 48% diabetic and 59% with established cardiovascular disease, serum AGEs was determined by total serum and molecular weight based serum autofluorescence (HPLC), tissue levels of AGEs was measured by skin autofluorescence (AF), AS was measured by determination of carotid-femoral pulse wave velocity (cfPWV). Results: Despite higher levels of HbA1C in diabetic patients (0.063 ± 0.011 vs 0.052 ± 0.004, p = 0.001) and higher cfPWV (14.9 ± 3.7 vs 12.4 ± 3.8, p < 0.001), total serum AGEs was lower in diabetic subjects (3.47 ± 0.77 vs 2.97 ± 0.76, p < 0.001). There was a positive association between total serum AGEs and normalized protein nitrogen appearance rate in both diabetics (r = 0.337, p = 0.004) and non-diabetics (r = 0.362, p < 0.001). There were no correlations between skin AF and total serum AGEs or any of the fractions of molecular weight using various cut-offs (2, 5, 10 and 30 kd). In non-diabetics, there was a consistent negative association between serum AGEs and AS (even after adjustments for confounding factors), but there was not any associations between serum AGEs and AS in diabetics. Conclusion: Serum measurements of AGEs based on serum auto-fluorescence is better correlated to protein intake and is not associated with tissue levels of AGEs or AS.
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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) | 0.000 | 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) | 0.003 | 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".