Saturated fatty acids induce insulin resistance in podocytes through inhibition of IRS1 via activation of both IKKβ and mTORC1
Bibliographic record
Abstract
Diabetic nephropathy (DN), a microvascular complication of diabetes, is the leading cause of end-stage renal disease worldwide. Multiple studies have shown that podocyte dysfunction is a central event in the progression of the disease. Beside chronic hyperglycemia, dyslipidemia can induce insulin resistance and dysfunction in podocytes. However, the exact mechanisms of free fatty acid (FFA)-induced podocyte insulin unresponsiveness are poorly understood. We used a type 2 diabetic mouse model (db/db) and mouse podocytes exposed to palmitic acid for 24 h followed by an insulin stimulation. Renal function and pathology were evaluated at 25 weeks of age to confirm the DN development. Our results demonstrate that saturated FFA activated the serine/threonine kinases IκB kinase (IKK)β/IκBα and mTORC1/S6K1, but not protein kinase C and c-jun N-terminal kinase, in podocytes and glomeruli of db/db mice. Activation of both kinases promoted serine 307 phosphorylation of IRS1, a residue known to provoke IRS1 inhibition. Using IKK, mTORC1 and ceramide production inhibitors, we were able to blunt IRS1 serine 307 phosphorylation and restore insulin stimulation of Akt. In conclusion, our results indicate that FFA and diabetes contribute to insulin resistance through the activation of IKKβ and S6K1 leading to podocyte dysfunction and DN.
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 | 0.000 | 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) | 0.002 | 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".