Human Aldose Reductase Expression Prevents Atherosclerosis Regression in Diabetic Mice
Bibliographic record
Abstract
Guidelines to reduce cardiovascular risk in diabetes include aggressive LDL lowering, but benefits are attenuated compared with those in patients without diabetes. Consistent with this, we have reported in mice that hyperglycemia impaired atherosclerosis regression. Aldose reductase (AR) is thought to contribute to clinical complications of diabetes by directing glucose into pathways producing inflammatory metabolites. Mice have low levels of AR, thus raising them to human levels would be a more clinically relevant model to study changes in diabetes under atherosclerosis regression conditions. Donor aortae from Western diet–fed Ldlr−/− mice were transplanted into normolipidemic wild-type, Ins2Akita (Akita+/−, insulin deficient), human AR (hAR) transgenic, or Akita+/−/hAR mice. Akita+/− mice had impaired plaque regression as measured by changes in plaque size and the contents of CD68+ cells (macrophages), lipids, and collagen. Supporting synergy between hyperglycemia and hAR were the even more pronounced changes in these parameters in Akita+/−/hAR mice, which had atherosclerosis progression in spite of normolipidemia. Plaque CD68+ cells from the Akita+/−/hAR mice had increased oxidant stress and expression of inflammation-associated genes but decreased expression of anti-inflammatory genes. In summary, hAR expression amplifies impaired atherosclerosis regression in diabetic mice, likely by interfering with the expected reduction in plaque macrophage inflammation.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".