Tacrolimus and Rapamycin do not elevate cholesterol synthesis in post‐transplant patients, but may impair response to multi‐nutrient dietary intervention
Bibliographic record
Abstract
Patients frequently experience hypercholesterolemia after transplant due to immunosuppressive drugs. Mechanisms responsible for this rise in plasma cholesterol are not clear. Dietary therapy is usually not sufficient to reduce plasma cholesterol post‐transplant, however these interventions may be limited in scope. The purpose of this study was to investigate cholesterol synthesis as a possible mechanism for hypercholesterolemia in post‐transplant patients, and test the efficacy of a multi‐faceted dietary intervention. Islet (ITx; n=8) and liver (LTx; n=7) post‐transplant patients on Tacrolimus (Tac) or Rapamycin (Rapa) were recruited with normal control subjects (Ctl; n=9) to measure 24h cholesterol fractional synthesis (FSR‐C) using deuterium. A subset of patients underwent a 4‐week dietary intervention using fish oil, phytosterols, soy, and fibers. FSR‐C was not different between Ctl, ITx and LTx groups. Diet intervention significantly lowered plasma cholesterol and triglyceride in Ctl, however was not effective in ITx or LTx. Diet produced mixed results in changes in FSR‐C across groups. In conclusion, hyperlipidemia caused by Tac or Rapa may not be due to elevated cholesterol synthesis. However, these drugs may interfere with normal responses to hypolipidemic dietary therapy effective in non‐transplant populations. 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) | 0.002 | 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".