4.7 Article

New approaches to the treatment of dense deposit disease

期刊

JOURNAL OF THE AMERICAN SOCIETY OF NEPHROLOGY
卷 18, 期 9, 页码 2447-2456

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AMER SOC NEPHROLOGY
DOI: 10.1681/ASN.2007030356

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资金

  1. Medical Research Council [G0001089] Funding Source: Medline
  2. NEI NIH HHS [R24 EY017404, R01 EY011515] Funding Source: Medline
  3. NIDDK NIH HHS [R13 DK077416, R13 DK077416-01] Funding Source: Medline
  4. Wellcome Trust Funding Source: Medline
  5. MRC [G0001089] Funding Source: UKRI
  6. NATIONAL EYE INSTITUTE [R01EY011515, R24EY017404] Funding Source: NIH RePORTER
  7. NATIONAL INSTITUTE OF DIABETES AND DIGESTIVE AND KIDNEY DISEASES [R13DK077416] Funding Source: NIH RePORTER

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The development of clinical treatment protocols usually relies on evidence-based guidelines that focus on randomized, controlled trials. For rare renal diseases, such stringent requirements can represent a significant challenge. Dense deposit disease (DDD; also known as membranoproliferative glomerulonephritis type II) is a prototypical rare disease. It affects only two to three people per million and leads to renal failure within 10 yr in 50% of affected children. On the basis of pathophysiology, this article presents a diagnostic and treatment algorithm for patients with DDD. Diagnostic tests should assess the alternative pathway of complement for abnormalities. Treatment options include aggressive BP control and reduction of proteinuria, and on the basis of pathophysiology, animal data, and human studies, plasma infusion or exchange, rituximab, sulodexide, and eculizumab are additional options. Criteria for treatment success should be prevention of progression as determined by maintenance or improvement in renal function. A secondary criterion should be normalization of activity levels of the alternative complement pathway as measured by C3/C3d ratios and C3NeF levels. Outcomes should be reported to a central repository that is now accessible to all clinicians. As the understanding of DDD increases, novel therapies should be integrated into existing protocols for DDD and evaluated using an open-label Bayesian study design.

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