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Biomarker algorithm may predict lupus treatment response

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WASHINGTON — The renal activity in lupus urinary biomarker algorithm can predict treatment response, according to data presented at the American College of Rheumatology Annual Meeting.

“Proteinuria, urinary sediments and other measures have been unreliable measures of activity or treatment response,” Gaurav Gulati, MD, in the Division of Immunology, Allergy and Rheumatology at the University of Cincinnati College of Medicine, said in a presentation.

Gulati and colleagues previously showed the ability of the renal activity in lupus (RAIL) algorithm — which utilizes six urinary biomarkers (UBMs) — to predict lupus disease activity in children and adults. For the algorithm, the researchers used the following UBMs:

• neutrophil gelatinase-associated lipocalin (NGAL);
• ceruloplasmin (CP);
• kidney injury molecule 1 (KIM 1);
• monocyte chemotactic protein 1 (MCP 1);
• adiponectin (ADIPO); and
• hemopexin.

For this study, the researchers tested the ability of the algorithm to predict response to cyclophosphamide vs. mycophenolate mofetil. The researchers enrolled 72 patients with systemic lupus erythematosus and proven class 3/4 lupus nephritis (80% female). Of these patients, 32 responded to treatment and 40 did not.

During 12 months of treatment, the researchers obtained patients’ urinalysis, complement levels, serial urine samples and anti-double stranded DNA antibody levels. They measured the six UBMs longitudinally using standardized ELISA-based assays for every 3 months.

After 3 months, all UBMs showed significantly lower levels among responders vs. non-responders, except for NGAL. After 6 months, investigators found this held true for all six UBMs. After 9 months, investigators found only KIM 1 and ADIPO showed a significant reduction. After 12 months, NGAL and HPX re-emerged and showed a significant reduction, while the other four UBMs did not.

The researchers concluded these RAIL UBMs could be highly reliable in making therapy changes. – by Will Offit

Reference:

Gulati G, et al. Abstract #966. Presented at: American College of Rheumatology Annual Meeting; Nov. 11-16, 2016; Washington.

Disclosure: One researcher reports funds from the NIH.