Association of four DNA polymorphisms with acute rejection after kidney transplantation
Bibliographic record
Abstract
Renal transplant outcomes exhibit large inter-individual variability, possibly on account of genetic variation in immune-response mediators and genes influencing the pharmacodynamics/pharmacokinetics of immunosuppressants. We examined 21 polymorphisms from 10 genes in 237 de novo renal transplant recipients participating in an open-label, multicenter study [Cyclosporine Avoidance Eliminates Serious Adverse Renal-toxicity (CAESAR)] investigating renal function and biopsy-proven acute rejection (BPAR) with different cyclosporine A regimens and mycophenolate mofetil. Genes were selected for their immune response and pharmacodynamic/pharmacokinetic relevance and were tested for association with BPAR. Four polymorphisms were significantly associated with BPAR. The ABCB1 2677T allele tripled the odds of developing BPAR (OR: 3.16, 95% CI [1.50-6.67]; P=0.003), as did the presence of at least one IMPDH2 3757C allele (OR: 3.39, 95% CI [1.42-8.09]; P=0.006). BPAR was almost fivefold more likely in patients homozygous for IL-10 -592A (OR: 4.71, 95% CI [1.52-14.55]; P=0.007) and twice as likely in patients with at least one A allele of TNF-alpha G-308A (OR: 2.18, 95% CI [1.08-4.41]; P=0.029). There were no statistically significant interactions between polymorphisms, or the different treatment regimens. Variation in genes of immune response and pharmacodynamic/pharmacokinetic relevance may be important in understanding acute rejection after renal transplant.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".