Carnosine scavenging of glucolipotoxic free radicals enhances insulin secretion and glucose uptake
Bibliographic record
Abstract
The worldwide prevalence of diabetes has risen to 8.5% among adults, which represents a staggering rise in prevalence from 4.7% in 1980. Whilst some treatments work by increasing insulin secretion, over time their effectiveness decreases. We aim to increase insulin secretion by developing strategies that work through mechanisms independent of current treatment options. Isolated CD1 mouse islets, INS-1 pancreatic β-cells, or C2C12 mouse myotubes were incubated in standard tissue culture media, or media supplemented with 28 mM glucose, 200 μM palmitic acid, and 200 μM oleic acid as a cellular model of diabetic glucolipotoxicity. Intracellular reactive species content was assayed using 2',7'-dichlorofluorescein diacetate dye, inducible nitric oxide synthase levels determined by Western blot, 3-nitrotyrosine and 4-hydrpxnonenal both assayed by ELISA, insulin secretion quantified using ELISA or radioimmunoassay, and glucose uptake determined through 2-deoxy glucose 6 phosphate luminescence. Our data indicate that carnosine, a histidine containing dipeptide available through the diet, is an effective scavenger of each of the aforementioned reactive species. This results in doubling of insulin secretion from isolated mouse islets or INS-1 β-cells. Crucially, carnosine also reverses glucolipotoxic inhibition of insulin secretion and enhances glucose uptake into skeletal muscle cells. Thus, carnosine, or non-hydrolysable carnosine analogs, may represent a new class of therapeutic agent to fight type 2 diabetes.
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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) | 0.000 | 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) | 0.001 | 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".