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IgA nephropathy: an update.

Maria F Soares1, Ian S D Roberts

  • 1Department of Cellular Pathology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, United Kingdom.

Current Opinion in Nephrology and Hypertension
|February 22, 2017
PubMed
Summary
This summary is machine-generated.

The Oxford Classification for IgA nephropathy is updated with new findings. Tubular atrophy/interstitial fibrosis strongly predicts kidney survival, while other factors predict function loss and treatment response.

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Area of Science:

  • Nephrology
  • Pathology
  • Immunology

Background:

  • IgA nephropathy (IgAN) is a common glomerular disease.
  • The Oxford Classification provides a standardized method for evaluating IgAN histology.
  • Updates are needed to incorporate new research and refine prognostic capabilities.

Purpose of the Study:

  • To review recent developments since the Oxford Classification of IgA nephropathy was established.
  • To discuss histological lesions not initially included in the classification.
  • To enhance the prognostic and predictive value of the Oxford Classification.

Main Methods:

  • Review of over 20 validation studies of the Oxford Classification.
  • Analysis of histological features and their correlation with clinical outcomes.
  • Examination of repeat biopsy data to assess treatment response.

Main Results:

  • Tubular atrophy/interstitial fibrosis (T score) is the strongest predictor of renal survival.
  • Mesangial hypercellularity (M score) predicts the rate of renal function loss.
  • Endocapillary hypercellularity (E score) and crescents (C score) impact renal survival and function, with C score added in the 2016 revision.
  • Podocytopathic changes correlate with proteinuria, function decline, and worse survival.
  • Combined histological and clinical data improve outcome prediction.

Conclusions:

  • The Oxford Classification is increasingly validated and applicable to broader IgAN populations.
  • Integrating histological findings with clinical data and biomarkers enhances prognostication.
  • This approach aids in identifying patients who may benefit from immunosuppressive therapy.