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Record W2950071076 · doi:10.1093/ndt/gfz106.fp761

FP761NON-HLA AGONISTIC ANTI-ANGIOTENSIN II TYPE 1 RECEPTOR ANTIBODIES INDUCE A DISTINCTIVE PHENOTYPE OF REJECTION IN KIDNEY TRANSPLANT RECIPIENTS: AN OBSERVATIONAL COHORT STUDY

2019· article· en· W2950071076 on OpenAlexaff
Denis Viglietti, Carmen Lefaucheur, Aurélie Philippe, Olivier Aubert, Denis Glotz, Christophe Legendre, Philip F. Halloran, Alexandre Loupy, Duska Dragun

Bibliographic record

VenueNephrology Dialysis Transplantation · 2019
Typearticle
Languageen
FieldMedicine
TopicRenal Transplantation Outcomes and Treatments
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineAgonistic behaviourHuman leukocyte antigenObservational studyAntibodyKidney transplantCohortKidney transplantationImmunologyPhenotypeRenin–angiotensin systemInternal medicineKidneyAntigenBlood pressureGenetics

Abstract

fetched live from OpenAlex

INTRODUCTION: The implementation of the HLA system in clinical practice was a breakthrough in transplant medicine. However, half of transplants fail within 15 years. We aimed to determine whether non-HLA anti-angiotensin II type 1 receptor (AT1R) antibodies might identify kidney recipients at risk of allograft rejection and loss. METHODS: We prospectively enrolled 1845 kidney recipients transplanted in two centers. Patients underwent allograft evaluation within the first year after transplantation, including allograft function, proteinuria, blood pressure, anti-HLA donor-specific antibodies (DSAs), AT1R antibodies (using quantitative ELISA) and allograft biopsy to assess rejection phenotype using histology, immunochemistry and gene expression in allografts based on microarray. RESULTS: Overall, 371 (20.1%) patients had AT1R antibodies (>10 U/mL), 334 (18.1%) had anti-HLA DSAs and 133 (7.2%) had both antibodies. The presence of AT1R antibodies was associated with an increased risk of allograft loss: adjusted HR, 1.49 (95%CI, 1.07-2.06) for AT1R antibodies alone and 2.26 (95%CI, 1.52-3.36) for AT1R antibodies and anti-HLA DSAs. Higher levels of circulating anti-AT1R antibodies were associated with increasing incidence of allograft loss in penalized spline modeling (p<0.001). Patients with AT1R antibodies showed a higher incidence of active antibody-mediated rejection (AMR) compared with patients without AT1R antibodies (n=126/504 (25.0%) vs. n=173/1341 (12.9%); p<0.001). AT1R antibodies identified 51/77 (66.2%) patients as having AMR among patients with histological features of active AMR without evidence of anti-HLA DSAs. Compared to patients with prototypical anti-HLA DSA-mediated rejection, patients with AT1R antibody-associated rejection had more frequently hypertension, increased prevalence of vascular rejection with arterial inflammation and lack of complement deposition in allograft capillaries. Based on gene expression analysis, patients with AT1R antibody-associated rejection showed higher levels of endothelial-associated transcripts demonstrating the interaction of AT1R antibodies with the vascular endothelium (p=0.013) and lower levels of gamma interferon-induced transcripts (p=0.010) compared with those with prototypical anti-HLA DSA-mediated rejection. CONCLUSIONS: Non-HLA AT1R antibodies identify kidney recipients at high risk of allograft rejection and loss, independent of HLA system. Recognition of complement-independent AT1R antibody-mediated vascular rejection could lead to the development of new treatment strategies targeting circulating antibodies and AT1Rs, such as the use of sartans, to improve kidney allograft survival.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.030
GPT teacher head0.303
Teacher spread0.273 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations1
Published2019
Admission routes1
Has abstractyes

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