NRF2 Deficiency Attenuates Diabetic Kidney Disease in Db/Db Mice via Down-Regulation of Angiotensinogen, SGLT2, CD36, and FABP4 Expression and Lipid Accumulation in Renal Proximal Tubular Cells
Bibliographic record
Abstract
The role(s) of nuclear factor erythroid 2-related factor 2 (NRF2) in diabetic kidney disease (DKD) is/are controversial. We hypothesized that Nrf2 deficiency in type 2 diabetes (T2D) db/db mice (db/dbNrf2 knockout (KO)) attenuates DKD progression through the down-regulation of angiotensinogen (AGT), sodium-glucose cotransporter-2 (SGLT2), scavenger receptor CD36, and fatty -acid-binding protein 4 (FABP4), and lipid accumulation in renal proximal tubular cells (RPTCs). Db/dbNrf2 KO mice were studied at 16 weeks of age. Human RPTCs (HK2) with NRF2 KO via CRISPR-Cas9 genome editing and kidneys from patients with or without T2D were examined. Compared with db/db mice, db/dbNrf2 KO mice had lower systolic blood pressure, fasting blood glucose, kidney hypertrophy, glomerular filtration rate, urinary albumin/creatinine ratio, tubular lipid droplet accumulation, and decreased expression of AGT, SGLT2, CD36, and FABP4 in RPTCs. Male and female mice had similar results. NRF2 KO attenuated the stimulatory effect of the Nrf2 activator, oltipraz, on AGT, SGLT2, and CD36 expression and high-glucose/free fatty acid (FFA)-stimulated lipid accumulation in HK2. Kidneys from T2D patients exhibited markedly higher levels of CD36 and FABP4 in RPTCs than kidneys from non-diabetic patients. These data suggest that NRF2 exacerbates DKD through the stimulation of AGT, SGLT2, CD36, and FABP4 expression and lipid accumulation in RPTCs of T2D.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".