By Michael V. Milburn, John A. Ryals, and Lining Guo
Many ’omics’ technologies have now become the primary way of discovering new biomarkers, and validation of these biomarkers is a relatively new and evolving concept. Whether it’s genomics (profiling RNA and DNA), proteomics (profiling proteins), metabolomics (profiling biochemicals or metabolites) or some other discovery technology, the process starts with an idea of the type of biomarkers that are needed for a condition/disease/or health risk.
For example, kidney dysfunction is a well studied condition that can lead to a number of health problems, including kidney failure. A number of simple fasting blood and urine biomarkers do exist for kidney function and have their utilities. Creatinine is cleared slowly from the blood through the kidneys and is continuously produced from creatine in the muscle.
An increase or decrease in serum creatinine (SCr) is commonly used as an assessment of kidney function, since it measures how well the metabolite is cleared from the blood into the urine. Other biomarkers of kidney function include blood urea levels (BUN), albumin in urine, and total protein in urine. Several alternative biomarkers have also been proposed in the literature, including urinary kidney injury molecule-1 (KIM-1), neutrophil gelatinase-associated lipocalin (NGAL), osterponein, and others.
However, new and more predictive biomarkers of kidney function are needed to better assess those patients that are at risk of developing more serious kidney dysfunction. By the time that many of these current biomarkers (both metabolites and proteins) are detectable, kidney impairment is usually fairly advanced and it can be too late to reverse the loss of kidney function. Also, unlike liver injury which may be able to heal itself over time, kidney injury is often permanent and effects patients for the rest of their lives.