Increases in bioactive lipids accompany early metabolic changes associated with β-cell expansion in response to short-term high-fat diet
Bibliographic record
Abstract
Pancreatic β-cell expansion is a highly regulated metabolic adaptation to increased somatic demands, including obesity and pregnancy; adult β cells otherwise rarely proliferate. We previously showed that high-fat diet (HFD) feeding induces mouse β-cell proliferation in less than 1 wk in the absence of insulin resistance. Here we metabolically profiled tissues from a short-term HFD β-cell expansion mouse model to identify pathways and metabolite changes associated with β-cell proliferation. Mice fed HFD vs. chow diet (CD) showed a 14.3% increase in body weight after 7 days; β-cell proliferation increased 1.75-fold without insulin resistance. Plasma from 1-wk HFD-fed mice induced β-cell proliferation ex vivo. The plasma, as well as liver, skeletal muscle, and bone, were assessed by LC and GC mass-spectrometry for global metabolite changes. Of the 1,283 metabolites detected, 159 showed significant changes [false discovery rate (FDR) < 0.1]. The majority of changes were in liver and muscle. Pathway enrichment analysis revealed key metabolic changes in steroid synthesis and lipid metabolism, including free fatty acids and other bioactive lipids. Other important enrichments included changes in the citric acid cycle and 1-carbon metabolism pathways implicated in DNA methylation. Although the minority of changes were observed in bone and plasma (<20), increased p-cresol sulfate was increased >4 fold in plasma (the largest increase in all tissues), and pantothenate (vitamin B5) decreased >2-fold. The results suggest that HFD-mediated β-cell expansion is associated with complex, global metabolite changes. The finding could be a significant insight into Type 2 diabetes pathogenesis and potential novel drug targets.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".