Abstract
Diagnosis and management of chronic kidney disease (CKD) will be characterized in the future by an increasing use of biomarkers—quantitative indicators of biologic or pathologic processes that vary continuously with progression of the process. “Classical” biomarkers of CKD progression include quantitative proteinuria, the percentage of sclerotic glomeruli or fractional interstitial fibrosis. New candidate biomarkers (e.g., urinary proteomic patterns) are being developed based on both mechanistic and “shotgun” approaches. Validation of potential biomarkers in prospective studies as surrogate endpoints for hard clinical outcomes is often complicated by the long lag time to the ultimate clinical outcome (e.g., end-stage renal disease). The very dense data sets that result from shotgun approaches on small numbers of patients carry a significant risk of model overfitting, leading to spurious associations. New analytic methods can help to decrease this risk. It is likely that clinical practice will come to depend increasingly on multiplex (vector) biomarkers used in conjunction with risk markers in early diagnosis as well as to guide therapy.
Highlights
Kidney diseases have been described in terms of clinical observations supplemented by chemical mea-The term biomarker has been in use since at least the 1970s, initially in clinical research on cancer and cardiovascular disease
As defined by a US National Institutes of Health (NIH) working group [3], a biomarker is a quantitative indicator of a definable biologic or pathologic process that may be used in diagnosis or to monitor therapy
Examples of genetic risk factors include homozygous angiotensin converting enzyme (ACE) deletion genotype or certain α-adducin gene polymorphisms. These genetic risk factors may be responsible for a hypertensive milieu that deleteriously modifies the context of a renal disease, shifting the process from one trajectory to another “parallel” trajectory (Fig. 1)
Summary
Kidney diseases have been described in terms of clinical observations supplemented by chemical mea-. Examples of genetic risk factors include homozygous angiotensin converting enzyme (ACE) deletion genotype or certain α-adducin gene polymorphisms These genetic risk factors may be responsible for a hypertensive milieu that deleteriously modifies the context of a renal disease, shifting the process from one trajectory to another “parallel” trajectory (Fig. 1). The National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) of the NIH has encouraged the use of biomarkers, such as albuminuria, as surrogate endpoints to improve clinical trial efficiency and decrease the need for lengthy studies of slowly progressive diseases [4]. In addition to their use in clinical trials (for drug screening, patient preselection, or as surrogate endpoints), biomarkers can be used for tracking the natural history of disease in early studies They will someday be used in clinical practice for the early detection of disease states or for monitoring treatment efficacy or toxicity. A forward-looking use of biomarkers is based on biomarker discovery, validation, and translation to clinical practice [1]
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