# Anti-angiotensin II type 1 receptor autoantibodies of IgG3 subclass outperform total anti-AT1R IgG1-IgG4 levels in predicting transplanted kidney antibody-mediated rejection

## TL;DR

IgG3 subclass of anti-AT1R antibodies better predicts kidney transplant rejection than total IgG levels, suggesting a more accurate diagnostic approach.

## Contribution

The study demonstrates that IgG3 subclass profiling improves antibody-mediated rejection prediction compared to total IgG measurements.

## Key findings

- AT1R-IgG3 levels were significantly elevated in antibody-mediated rejection cases.
- IgG3 showed better predictive performance (AUC 0.63) than total AT1R-IgG (AUC 0.53).
- AMR prevalence increased with higher IgG3 quartiles but not with total AT1R-IgG.

## Abstract

Antibody-mediated rejection (AMR) is a leading cause of kidney allograft loss. Anti-angiotensin II type-1 receptor (AT1R) autoantibodies (AABs) have been implicated in AMR and microvascular inflammation (MVI), particularly in C4d-negative and non-HLA antibody dependent cases. Conventional assays measure only total IgG and do not assess pathogenic subclass heterogeneity. Whether IgG1-IgG4 subclass profiling improves AMR prediction has not yet been investigated.

We included 143 adult kidney-transplant recipients who underwent indication biopsy between 2018 and 2025. Histopathology was classified according to Banff 2017–2022 criteria. Serum samples were analysed for total AT1R-IgG (U/mL) and AT1R IgG1–IgG4 subclasses (relative optical density). Associations with AMR were assessed using group comparisons, correlation analysis, logistic regression, ROC AUC, and quartile-based analyses.

AT1R-IgG3 levels were significantly elevated in AMR (Kruskal–Wallis, p = 0.0396), correlated with AMR (Spearman ρ = 0.19, p = 0.02), and demonstrated better predictive performance (AUC 0.63 vs 0.53) than total AT1R-IgG. Logistic regression showed stronger associations for IgG3 (OR 1.33, p = 0.0004) than total AT1R-IgG (OR 1.19, p = 0.029). AMR prevalence increased across IgG3 quartiles (Q1:10.5% → Q4:31.6%), while no such trend was observed for total AT1R-IgG.

AT1R antibodies of IgG3 subclass outperform total AT1R levels in predicting AMR, revealing pathogenic antibody patterns that are not detectable through global IgG quantitation. Subclass profiling may contribute to more precise AMR risk assessment, but longitudinal and multicentre validation studies with standardized subclass-specific assays are needed to confirm these findings. Although AT1R IgG3 levels were significantly correlated with AMR, the magnitude of these associations is insufficient to support their use as an independent diagnostic marker and may only serve as a complementary AMR marker.