Circulating sphingolipid biomarkers in models of type 1 diabetes
Bibliographic record
Abstract
Alterations in lipid metabolism may contribute to diabetic complications. Sphingolipids are essential components of cell membranes and have essential roles in homeostasis and in the initiation and progression of disease. However, the role of sphingolipids in type 1 diabetes remains largely unexplored. Therefore, we sought to quantify sphingolipid metabolites by LC-MS/MS from two animal models of type 1 diabetes (streptozotocin-induced diabetic rats and Ins2(Akita) diabetic mice) to identify putative therapeutic targets and biomarkers. The results reveal that sphingosine-1-phosphate (So1P) is elevated in both diabetic models in comparison to respective control animals. In addition, diabetic animals demonstrated reductions in plasma levels of omega-9 24:1 (nervonic acid)-containing ceramide, sphingomyelin, and cerebrosides. Reduction of 24:1-esterfied sphingolipids was also observed in liver and heart. Nutritional stress via a high-fat diet also reduced 24:1 content in the plasma and liver of mice, exacerbating the decrease in some cases where diabetes was also present. Subcutaneous insulin corrected both circulating So1P and 24:1 levels in the murine diabetic model. Thus, changes in circulating sphingolipids, as evidenced by an increase in bioactive So1P and a reduction in cardio- and neuro-protective omega-9 esterified sphingolipids, may serve as biomarkers for type 1 diabetes and represent novel therapeutic targets.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".