Recipient APOL1 risk alleles associate with death-censored renal allograft survival and rejection episodes
Bibliographic record
Abstract
ABSTRACT Apolipoprotein L1 (APOL1) risk alleles in donor kidneys associate with graft loss but whether recipient risk allele expression impacts kidney transplant outcomes is unclear. To test whether recipient APOL1 allelic variants independently correlate with transplant outcomes, we analyzed genome-wide SNP genotyping data of donors and recipients from two kidney transplant cohorts, Genomics of Chronic Allograft Rejection (GOCAR) and Clinical Trials in Organ Transplantation 1/17 (CTOT1/17). We estimated genetic ancestry (quantified as proportion of African ancestry or pAFR) by ADMIXTURE and correlated APOL1 genotypes and pAFR with outcomes. In the GOCAR discovery set, we observed that the number of recipient APOL1 G1/G2 alleles (R-nAPOL1) associated with increased risk of death-censored allograft loss (DCAL), independent of genetic ancestry (HR = 2.14; P = 0.006), and within the subgroup of African American and Hispanic (AA/H) recipients (HR = 2.36; P = 0.003). R-nAPOL1 also associated with increased risk of any T cell-mediated rejection (TCMR) event. Analysis of the CTOT cohort validated these associations. Ex vivo studies of peripheral blood mononuclear cells revealed unanticipated high APOL1 expression in activated CD4 + /CD8 + T cells and natural killer cells. We detected enriched immune response gene pathways in G1/G2 allele carriers vs. non-carriers among patients on the kidney waitlist and healthy controls. Together our findings highlight a previously unrecognized contribution of recipient APOL1 risk alleles to renal allograft outcomes. This immunomodulatory role has broader implications for immune mediated injury to native kidneys.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".