MK2 Deletion in Mice Prevents Diabetes-Induced Perturbations in Lipid Metabolism and Cardiac Dysfunction
Bibliographic record
Abstract
Heart disease remains a major complication of diabetes, and the identification of new therapeutic targets is essential. This study investigates the role of the protein kinase MK2, a p38 mitogen-activated protein kinase downstream target, in the development of diabetes-induced cardiomyopathy. Diabetes was induced in control (MK2(+/+)) and MK2-null (MK2(-/-)) mice using repeated injections of a low dose of streptozotocin (STZ). This protocol generated in MK2(+/+) mice a model of diabetes characterized by a 50% decrease in plasma insulin, hyperglycemia, and insulin resistance (IR), as well as major contractile dysfunction, which was associated with alterations in proteins involved in calcium handling. While MK2(-/-)-STZ mice remained hyperglycemic, they showed improved IR and none of the cardiac functional or molecular alterations. Further analyses highlighted marked lipid perturbations in MK2(+/+)-STZ mice, which encompass increased 1) circulating levels of free fatty acid, ketone bodies, and long-chain acylcarnitines and 2) cardiac triglyceride accumulation and ex vivo palmitate β-oxidation. MK2(-/-)-STZ mice were also protected against all these diabetes-induced lipid alterations. Our results demonstrate the benefits of MK2 deletion on diabetes-induced cardiac molecular and lipid metabolic changes, as well as contractile dysfunction. As a result, MK2 represents a new potential therapeutic target to prevent diabetes-induced cardiac dysfunction.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".