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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".