Effect of different immunosuppressive regimens on the evolution of distinct metabolic parameters: evidence from the Symphony study
Bibliographic record
Abstract
BACKGROUND: The metabolic syndrome (MS) is an important risk factor for graft dysfunction and patient death after renal transplantation. The aim of this sub-analysis of the Symphony study was to assess the progression of the laboratory parameters associated with MS in the first year after transplantation. METHODS: Data collected from the Symphony study were used; 1645 patients were randomized to receive standard-dose cyclosporine (Stand-CsA), low-dose cyclosporine (Low-CsA), tacrolimus (Low-Tac) or sirolimus (Low-SRL), in addition to mycophenolate mofetil (MMF) and corticosteroids. Data were collected for levels and progression over the first year post-transplantation of systolic and diastolic blood pressure, uric acid, triglycerides, low-density lipoprotein (LDL) cholesterol, high-density lipoprotein (HDL) cholesterol and fasting glucose levels by treatment arm. RESULTS: The low-SRL group had significantly higher levels of triglycerides and LDL. The two CsA arms were associated with the highest uric acid levels at each time point. There were no significant differences in overall levels or changes in glucose or HDL. Patients in the standard-CsA arm had significantly higher diastolic blood pressure than those in the Low-SRL and Low-Tac arms. Systolic blood pressure was higher in the Low-CsA arm than in the Low-Tac arm. The use of antihypertensive and antidiabetic agents was similar between the treatment arms. In the Low-SRL arm, more patients were treated with lipid-lowering therapy. Mean daily steroid doses were the highest in the Low-SRL arm. CONCLUSIONS: This sub-analysis demonstrates that there is a difference in metabolic parameters between immunosuppressive groups. CsA therapy was associated with the highest values of uric acid and systolic and diastolic blood pressure. Patients on SRL therapy had the worst lipaemic control. A possible effect of Tac on new-onset diabetes could not be excluded.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".