HLA-DR/DQ eplet mismatch predicts de novo donor-specific antibody development in multi-ethnic Southeast Asian kidney transplant recipients on different immunosuppression regimens
Bibliographic record
Abstract
Eplet mismatch has been recognized as a more precise strategy for determining HLA compatibility by analyzing donor-recipient HLA differences at the molecular level. However, predicting post-transplant alloimmunity using single-molecule eplet mismatch categories has not been validated in Asian cohorts. We examined a cohort of Southeast Asian kidney transplant recipients (n = 234) to evaluate HLA-DR/DQ eplet mismatch as a predictor of de novo donor-specific antibody (dnDSA) development. HLA-DR/DQ single-molecule eplet mismatch was quantified using HLA Matchmaker, and we utilized previously published HLA-DR/DQ eplet mismatch thresholds to categorize recipients into alloimmune risk groups and evaluate their association with dnDSA development. Recognizing that the predominance of cyclosporine use (71%) may alter published eplet mismatch thresholds derived from a largely tacrolimus-based (87%) cohort, we evaluated cohort-specific thresholds for HLA-DR/DQ single-molecule eplet mismatch categories. Recipient ethnicities included Chinese (65%), Malays (17%), Indians (14%), and others (4%). HLA-DR/DQ dnDSA developed in 29/234 (12%) recipients after a median follow-up of 5.4 years, including against isolated HLA-DR (n = 7), isolated HLA-DQ (n = 11), or both (n = 11). HLA-DR/DQ single-molecule eplet mismatch risk categories correlated with dnDSA-free survival ( p = 0.001) with low-risk recipients having a dnDSA prevalence of 1% over 5 years. The cohort-specific alloimmune risk categories improved correlation with HLA-DR/DQ dnDSA-free survival and remained significant after adjusting for calcineurin inhibitor and anti-metabolite immunosuppression ( p < 0.001). We validated the performance of single-molecule eplet mismatch categories as a prognostic biomarker for HLA-DR/DQ dnDSA development in a cohort of predominantly Asian kidney transplant recipients after adjusting for different immunosuppression regimens.
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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".