Calcineurin inhibitor (CNI) use in pediatric liver and kidney transplant (Tx) patients is associated with increased risk of obesity and impaired T-regulatory (Treg) cell function. (126.19)
Bibliographic record
Abstract
Abstract CNI use can cause hyperlipidemia and new onset diabetes, and obesity may have adverse effects on allograft and patient survival, but the mechanisms are unclear. Design: We assessed if CNI or rapamycin (Rapa) use correlated with the metabolic status of children (n = 62) with stable, long-term liver (26 M, 27 F) or kidney (6 M, 3 F) allografts, and if being overweight or obese (Ow/Ob) was associated with impaired Treg phenotype or function. Results: Most patients had CNI (39) or Rapa (9) monotherapy, though 12 received CNI (8) or Rapa (4) plus MMF, Aza or steroids. 45 children had normal weight, but 7 were overweight and 10 were obese. The CNI group had more Ow/Ob patients (p=0.045) and higher BMI-for age percentile (BMI) than the Rapa group (61.7 vs. 42.9, p=0.047). CNI dose correlated with BMI (0.521, p=0.013), while Rapa use negatively correlated with BMI (-0.888, p=0.044). Also, Ow/Ob patients had reduced Treg suppressive function compared to patients with normal weight (153 vs. 100 units for autologous CD4 responders, 166 vs. 92 for standard CD4 responders, 114 vs. 43 for CD8 responders, p=0.031), and BMI was negatively correlated with CTLA-4 expression in Tregs (-0.555, p=0.011). Conclusions: CNI use is associated with being overweight or obese post-Tx, and the latter are associated with impaired Treg function and phenotype. The metabolic consequences of being overweight or obese post-Tx may include disruption of immune regulation and favor a pro-inflammatory phenotype.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".