Structural modeling for Oxford histological classifications of immunoglobulin A nephropathy

Kensuke Joh, Takashi Nakazato, Akinori Hashiguchi, Akira Shimizu, Ritsuko Katafuchi, Hideo Okonogi, Kentaro Koike, Keita Hirano, Nobuo Tsuboi, Tetsuya Kawamura, Takashi Yokoo, Ichiei Narita and Yusuke Suzuki

PLOS ONE, 2022, vol. 17, issue 9, 1-12

Abstract: In immunoglobulin A nephropathy (IgAN), Cox regression analysis can select independent prognostic variables for renal functional decline (RFD). However, the correlation of the selected histological variables with clinical and/or treatment variables is unknown, thereby making histology-based treatment decisions unreliable. We prospectively followed 946 Japanese patients with IgAN for a median of 66 mo. and applied structural equation modeling (SEM) to identify direct and indirect effects of histological variables on RFD as a regression line of estimated glomerular filtration rate (eGFR) via clinical variables including amount of proteinuria, eGFR, mean arterial pressure (MAP) at biopsy, and treatment variables such as steroid therapy with/without tonsillectomy (ST) and renin–angiotensin system blocker (RASB). Multi-layered correlations between the variables and RFD were identified by multivariate linear regression analysis and the model’s goodness of fit was confirmed. Only tubular atrophy/interstitial fibrosis (T) had an accelerative direct effect on RFD, while endocapillary hypercellularity and active crescent (C) had an attenuating indirect effect via ST. Segmental sclerosis (S) had an attenuating indirect effect via eGFR and mesangial hypercellularity (M) had accelerative indirect effect for RFD via proteinuria. Moreover, M and C had accelerative indirect effect via proteinuria, which can be controlled by ST. However, both T and S had additional indirect accelerative effects via eGFR or MAP at biopsy, which cannot be controlled by ST. SEM identified a systemic path links between histological variables and RFD via dependent clinical and/or treatment variables. These findings lead to clinically applicable novel methodologies that can contribute to predict treatment outcomes using the Oxford classifications.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0268731

DOI: 10.1371/journal.pone.0268731

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