An Empirical Biomarker-based Calculator for Autosomal Recessive Polycystic Kidney Disease - The Nieto-Narayan Formula

TL;DR

This study develops an empirical, biomarker-based calculator to estimate cystic index in ARPKD patients, facilitating disease monitoring and clinical trial assessments without relying on expensive imaging.

Contribution

The paper introduces a novel formula derived from serum and urine biomarkers to estimate cystic index, improving disease management and trial evaluation in ARPKD.

Findings

Derived a family of equations for estimating cystic index from biomarkers

Distilled equations into a single empirical formula for practical use

Potential to monitor disease progression non-invasively

Abstract

Autosomal polycystic kidney disease (ARPKD) is associated with progressive enlargement of the kidneys fuelled by the formation and expansion of fluid-filled cysts. The disease is congenital and children that do not succumb to it during the neonatal period will, by age 10 years, more often than not, require nephrectomy+renal replacement therapy for management of both pain and renal insufficiency. Since increasing cystic index (CI; percent of kidney occupied by cysts) drives both renal expansion and organ dysfunction, management of these patients, including decisions such as elective nephrectomy and prioritization on the transplant waitlist, could clearly benefit from serial determination of CI. So also, clinical trials in ARPKD evaluating the efficacy of novel drug candidates could benefit from serial determination of CI. Although ultrasound is currently the imaging modality of choice for diagnosis of ARPKD, its utilization for assessing disease progression is highly limited. Magnetic resonance imaging or computed tomography, although more reliable for determination of CI, are expensive, time-consuming and somewhat impractical in the pediatric population. Using a well-established mammalian model of ARPKD, we undertook a big data-like analysis of minimally- or non-invasive serum and urine biomarkers of renal injury/dysfunction to derive a family of equations for estimating CI. We then applied a signal averaging protocol to distill these equations to a single empirical formula for calculation of CI. Such a formula will eventually find use in identifying and monitoring patients at high risk for progressing to end-stage renal disease and aid in the conduct of clinical trials.