Polycystic kidney disease (PKD), in its autosomal recessive (AR) or autosomal dominant (AD) form, is characterized by the formation and expansion of numerous fluid-filled cysts within the kidneys. Quite often, the disease spreads to extrarenal territories including the liver. In addition to cyst formation, interstitial collagen deposition or scarring is sometimes observed in both kidney and liver. Progressive enlargement of the kidneys via replacement of the renal parenchyma with cysts and decreasing renal function makes ADPKD the leading genetic cause of renal transplantation. Highly aggressive fibrocystic kidney and liver disease in ARPKD means that many children with this form of disease do not live past the age of ten years. Using the PCK rat model of PKD, we have identify a minimally invasive biomarker cluster with high correlative value for fibrocystic disease progression. These results are important in that patient compliance, disease prognosis, interventional decisions and outcomes can be further and vastly improved by identification of minimally invasive or non-invasive biomarkers that are prognosticative of disease progression. Furthermore, rather than rely on a single biomarker, clinical outcomes may be better predicted by identification of a cluster of disease-relevant biomarkers which would bring increased correlation with disease progression. Clinical trials of therapeutics for chronic fibrotic diseases would also benefit from identification of such biomarkers given Big Pharma’s reluctance to invest in trials wherein endpoints could be years away with no interim hint of success/failure. Identification of minimally invasive or non-invasive biomarkers in proliferative fibrocystic disease can better stratify children waitlisted for scarce kidneys and/or livers. The tangible outcome/technology/product that will result from the proposed research is biomarker-cluster chips designed to read urine or serum samples to determine disease progress or remission from disease. It is anticipated that these chips can eventually be mass produced in a relatively inefficient fashion and would have the predictive power≥imaging technologies but at far lesser cost and far lesser inconvenience. Eventually, this paradigm and the resulting technology may be extended to other diseases.
How to Cite:
Narayan, P. et al., (2015). Prognosticative Biomarker Clusters for Polycystic Kidney Disease. New Horizons in Translational Medicine. 2(2), p.66. DOI: http://doi.org/10.5334/j.nhtm.2014.11.038
Narayan P, Huang B, Paka P, D. Goldberg I. Prognosticative Biomarker Clusters for Polycystic Kidney Disease. New Horizons in Translational Medicine. 2015;2(2):66. DOI: http://doi.org/10.5334/j.nhtm.2014.11.038
Narayan, P., Huang, B., Paka, P., & D. Goldberg, I. (2015). Prognosticative Biomarker Clusters for Polycystic Kidney Disease. New Horizons in Translational Medicine, 2(2), 66. DOI: http://doi.org/10.5334/j.nhtm.2014.11.038