APOL1 Genotype and Renal Function of Black Living Donors
Bibliographic record
Abstract
Black living kidney donors are at higher risk of developing kidney disease than white donors. We examined the effect of the APOL1 high-risk genotype on postdonation renal function in black living kidney donors and evaluated whether this genotype alters the association between donation and donor outcome. We grouped 136 black living kidney donors as APOL1 high-risk (two risk alleles; n =19; 14%) or low-risk (one or zero risk alleles; n =117; 86%) genotype. Predonation characteristics were similar between groups, except for lower mean±SD baseline eGFR (CKD-EPI equation) in donors with the APOL1 high-risk genotype (98±17 versus 108±20 ml/min per 1.73 m 2 ; P =0.04). At a median of 12 years after donation, donors with the APOL1 high-risk genotype had lower eGFR (57±18 versus 67±15 ml/min per 1.73 m 2 ; P =0.02) and faster decline in eGFR after adjusting for predonation eGFR (1.19; 95% confidence interval, 0 to 2.3 versus 0.4; 95% confidence interval, 0.1 to 0.7 ml/min per 1.73 m 2 per year, P =0.02). Two donors developed ESRD; both carried the APOL1 high-risk genotype. In a subgroup of 115 donors matched to 115 nondonors by APOL1 genotype, we did not find a difference between groups in the rate of eGFR decline ( P =0.39) or any statistical interaction by APOL1 status ( P =0.92). In conclusion, APOL1 high-risk genotype in black living kidney donors associated with greater decline in postdonation kidney function. Trajectory of renal function was similar between donors and nondonors. The association between APOL1 high-risk genotype and poor renal outcomes in kidney donors requires validation in a larger study.
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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 | 0.002 |
| 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) | 0.001 | 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".