Renal tubule-specific Atgl deletion links kidney lipid metabolism to glucagon-like peptide 1 and insulin secretion independent of renal inflammation or lipotoxicity
Bibliographic record
Abstract
OBJECTIVE: Lipotoxic injury from renal lipid accumulation in obesity and type 2 diabetes (T2D) is implicated in associated kidney damage. However, models examining effects of renal ectopic lipid accumulation independent of obesity or T2D are lacking. We generated renal tubule-specific adipose triglyceride lipase knockout (RT-SAKO) mice to determine if this targeted triacylglycerol (TAG) over-storage affects glycemic control and kidney health. METHODS: Male and female RT-SAKO mice and their control littermates were tested for changes in glycemic control at 10-12 and 16-18 weeks of age. Markers of kidney health and blood lipid and hormone concentrations were analyzed. Kidney and blood lysophosphatidic acid (LPA) levels were measured, and a role for LPA in mediating impaired glycemic control was evaluated using the LPA receptor 1/3 inhibitor Ki-16425. RESULTS: All groups remained insulin sensitive, but 16- to 18-week-old male RT-SAKO mice became glucose intolerant, without developing kidney inflammation or fibrosis. Rather, these mice displayed lower circulating insulin and glucagon-like peptide 1 (GLP-1) levels. Impaired first-phase glucose-stimulated insulin secretion was detected and restored by Exendin-4. Kidney and blood LPA levels were elevated in older male but not female RT-SAKO mice, associated with increased kidney diacylglycerol kinase epsilon. Inhibition of LPA-mediated signaling restored serum GLP-1 levels, first-phase insulin secretion, and glucose tolerance. CONCLUSIONS: TAG over-storage alone is insufficient to cause renal tubule lipotoxicity. This work is the first to show that endogenously derived LPA modulates GLP-1 levels in vivo, demonstrating a new mechanism of kidney-gut-pancreas crosstalk to regulate insulin secretion and glucose homeostasis.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".