Advanced Glycation End Products Predict Loss of Renal Function and High-Risk Chronic Kidney Disease in Type 2 Diabetes
Bibliographic record
Abstract
OBJECTIVE: To evaluate the association of a multicomponent advanced glycation end product (AGE) panel with decline in kidney function and its utility in predicting renal function loss (RFL) when added to routine clinical measures in type 2 diabetes. RESEARCH DESIGN AND METHODS: Carboxymethyl and carboxyethyl lysine and methylglyoxal, 3-deoxyglucosone, and glyoxal hydroimidazolones were measured in baseline serum and plasma samples, respectively, from Action to Control Cardiovascular Risk in Diabetes (ACCORD) (n = 1,150) and Veterans Affairs Diabetes Trial (VADT) (n = 447) participants. A composite AGE score was calculated from individual AGE z scores. The primary outcome was a sustained 30% decline in estimated glomerular filtration rate (eGFR) (30% RFL in both cohorts). Secondary outcomes (in ACCORD) were 40% RFL, macroalbuminuria, and high-risk chronic kidney disease (hrCKD). RESULTS: After adjustment for baseline and follow-up HbA1c and other risk factors in ACCORD, the AGE score was associated with reduction in eGFR (β-estimate -0.66 mL/min ⋅ 1.73 m2 per year; P = 0.001), 30% RFL (hazard ratio 1.42 [95% CI 1.13-1.78]; P = 0.003), 40% RFL (1.40 [1.13-1.74]; P = 0.003), macroalbuminuria (1.53 [1.13-2.06]; P = 0.006), and hrCKD (1.88 [1.37-2.57]; P < 0.0001). AGE score improved net reclassification (NRI) and relative integrated discrimination (IDI) for 30% RFL (NRI 23%; P = 0.02) (relative IDI 7%; P = 0.009). In VADT, the AGE score calculated by the ACCORD-derived coefficients was associated with 30% RFL (1.37 [1.03-1.82); P = 0.03) and improved NRI (24%; P = 0.03) but not IDI (P = 0.18). CONCLUSIONS: These data provide further support for a causal role of AGEs in diabetic nephropathy independently of glycemic control and suggest utility of the composite AGE panel in predicting long-term decline in renal function.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".