Prognostic Stratification in Primary Glomerulonephritis: Integrating Histology, Biomarkers, and Risk Prediction Models

TL;DR

This paper reviews how combining kidney tissue analysis, biomarkers, and risk models can improve predicting outcomes for patients with primary glomerulonephritis.

Contribution

The paper integrates histology, biomarkers, and predictive models to advance precision risk assessment in primary glomerulonephritis.

Findings

Histological grading systems like Oxford and MEST-C are valuable for prognosis.

Biomarkers such as PLA2R antibodies and complement components show promise in outcome prediction.

Machine learning tools enhance the accuracy of risk prediction models.

Abstract

Primary glomerulonephritis encompasses a diverse group of kidney diseases with variable clinical trajectories and outcomes. Accurate prognostic stratification is critical for guiding individualized management and improving long-term renal survival. This narrative review synthesizes current evidence on the prognostic value of histological grading systems, circulating and urinary biomarkers, and integrative risk prediction models across major primary glomerulonephritis subtypes, including IgA nephropathy, membranous nephropathy, and focal segmental glomerulosclerosis. Emphasis is placed on the utility of established classification systems (e.g., Oxford, MEST-C, chronicity scores), emerging tissue and fluid biomarkers (e.g., PLA2R antibodies, complement components, cytokine profiles), and the validation of multivariable prognostic tools and nomograms. We highlight areas of convergence between histopathologic lesions and molecular markers, as well as the evolving role of machine learning in predictive modeling. Ultimately, combining morphological, biochemical, and algorithmic tools holds promise for precision risk assessment and treatment tailoring in primary glomerulonephritis.

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