De novo lipogenesis in Type 1 diabetes pre‐ and post‐islet transplant
Bibliographic record
Abstract
Type 1 diabetes (T1D) patients have elevated cardiovascular disease (CVD) risk yet normal blood lipids, suggesting altered lipid metabolism. Islet transplant (ITx) may cure T1D in some people, but immunosuppressive drugs may be hyperlipidemic. The objective was to investigate lipogenesis in T1D and ITx patients using stable isotopes. People with T1D (T1D; 4F/3M), post‐ITx patients (ITx; 3F/5M) and healthy subjects (Control; 4F/2M) participated. Fasting blood samples (0h and 24h) were analyzed for triglyceride (TG). Deuterium‐labeled water was given and stable isotope analysis performed on VLDL TG estimated hepatic synthesis of total and individual fatty acids (FA). Plasma TG was not different between Control (0.84±0.24), T1D (0.95±0.22), or ITx (0.75±0.31). Compared to Control, T1D showed a trend for greater total FA synthesis, of particularly 14:0, 16:0, and 18:0. ITx had reduced total FA synthesis (p<0.01), particularly 14:0, 16:0, 18:0, and 18:1 (p<0.01 for all) compared to T1D. Total FA synthesis was significantly related to plasma TG in ITx only (p<0.05). Synthesis of 16:0 was correlated with plasma TG level in Control and T1D (p<0.05) but not ITx. T1D may have greater hepatic FA synthesis, contributing to CVD risk. In this ITx group, immunosuppression did not raise plasma TG levels and did not have deleterious effects on hepatic FA synthesis. Grant Funding Source : Canadian Institutes of Health Research
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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".