Association of advanced glycation end‐products levels with vascular events in postmenopausal women
Bibliographic record
Abstract
AIM: Advanced glycation end-products (AGEs) are a known factor that accelerates vascular complications. AGEs (e.g. pentosidine or N-ε-carboxy-methyl-lysine [CML]) have been particularly investigated in patients with diabetes or chronic kidney disease and have been associated not only with arteriosclerosis, but also with novel vascular events. On the contrary, the correlation of vascular events with AGEs has not been sufficiently investigated in groups excluding those with diabetes or chronic kidney disease. The present study aimed to evaluate the impact of AGEs on the history of vascular events in postmenopausal women excluding those with diabetes or renal insufficiency. METHODS: Japanese postmenopausal women were registered to the study after obtaining informed consent. Patients with critical illness, including diabetes mellitus and renal insufficiency, were excluded from the study. Participants were asked about their medical histories during the registration for the Nagano Cohort Study. Non-fasting serum and urine samples were collected to measure biochemical markers, including urinary pentosidine and serum CML levels. RESULTS: Among 357 postmenopausal women, 32 had a history of vascular events. After adjusting age and other variables known to be associated with the presence of vascular event history, positive correlations between AGEs and vascular event history were observed (standardized odds ratio of log[pentosidine] 1.38, 95% CI 0.96-2.00, P = 0.086; standardized odds ratio of log[CML] 1.73, 95% CI 1.10-2.74, P = 0.019). DISCUSSION: The present results showed a significant association between serum CML and the presence of vascular event history, suggesting that serum CML might play a role in vascular events. Geriatr Gerontol Int 2021; 21: 651-656.
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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 | 0.001 |
| 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".