Deletion of Protein Kinase D1 in Pancreatic β-Cells Impairs Insulin Secretion in High-Fat Diet–Fed Mice
Bibliographic record
Abstract
Ββ-Cell adaptation to insulin resistance is necessary to maintain glucose homeostasis in obesity. Failure of this mechanism is a hallmark of type 2 diabetes (T2D). Hence, factors controlling functional β-cell compensation are potentially important targets for the treatment of T2D. Protein kinase D1 (PKD1) integrates diverse signals in the β-cell and plays a critical role in the control of insulin secretion. However, the role of β-cell PKD1 in glucose homeostasis in vivo is essentially unknown. Using β-cell-specific, inducible PKD1 knockout mice (βPKD1KO), we examined the role of β-cell PKD1 under basal conditions and during high-fat feeding. βPKD1KO mice under a chow diet presented no significant difference in glucose tolerance or insulin secretion compared with mice expressing the Cre transgene alone; however, when compared with wild-type mice, both groups developed glucose intolerance. Under a high-fat diet, deletion of PKD1 in β-cells worsened hyperglycemia, hyperinsulinemia, and glucose intolerance. This was accompanied by impaired glucose-induced insulin secretion both in vivo in hyperglycemic clamps and ex vivo in isolated islets from high-fat diet-fed βPKD1KO mice without changes in islet mass. This study demonstrates an essential role for PKD1 in the β-cell adaptive secretory response to high-fat feeding in mice.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".