Differential effects of glyoxalase 1 overexpression on diabetic atherosclerosis and renal dysfunction in streptozotocin-treated, apolipoprotein E-deficient mice
Bibliographic record
Abstract
The reactive dicarbonyls, glyoxal and methylglyoxal (MG), increase in diabetes and may participate in the development of diabetic complications. Glyoxal and MG are detoxified by the sequential activities of glyoxalase 1 (GLO1) and glyoxalase 2. To determine the contribution of these dicarbonyls to the etiology of complications, we have genetically manipulated GLO1 levels in apolipoprotein E-null (Apoe(-/-)) mice. Male Apoe(-/-) mice, hemizygous for a human GLO1 transgene (GLO1TGApoe(-/-) mice) or male nontransgenic Apoe(-/-) litter mates were injected with streptozotocin or vehicle and 6 or 20 weeks later, aortic atherosclerosis was quantified. The GLO1 transgene lessened streptozotocin (STZ)-induced increases in immunoreactive hydroimidazolone (MG-H1). Compared to nondiabetic mice, STZ-treated GLO1TGApoe(-/-) and Apoe(-/-) mice had increased serum cholesterol and triglycerides and increased atherosclerosis at both times after diabetes induction. While the increased GLO1 activity in the GLO1TGApoe(-/-) mice failed to protect against diabetic atherosclerosis, it lessened glomerular mesangial expansion, prevented albuminuria and lowered renal levels of dicarbonyls and protein glycation adducts. Aortic atherosclerosis was also quantified in 22-week-old, male normoglycemic Glo1 knockdown mice on an Apoe(-/-) background (Glo1KDApoe(-/-) mice), an age at which Glo1KD mice exhibit albuminuria and renal pathology similar to that of diabetic mice. In spite of ~75% decrease in GLO1 activity and increased aortic MG-H1, the Glo1KDApoe(-/-) mice did not show increased atherosclerosis compared to age-matched Apoe(-/-) mice. Thus, manipulation of GLO1 activity does not affect the development of early aortic atherosclerosis in Apoe(-/-) mice but can dictate the onset of kidney disease independently of blood glucose levels.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".